Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research is available on automatic grading for Arabic. Datasets are important to ASAG, and limited datasets are available in Arabic. In this research, we have collected a set of questions, answers, and associated grades in Arabic. We have made this dataset publicly available. We have extended to Arabic the solutions used for English ASAG. We have tested how automatic grading works on answers in Arabic provided by schoolchildren in 6th grade in the context of serious games. We found out those schoolchildren providing answers that are 5.6 words long on average. On such answers, deep learning-based grading has achieved high accuracy even with limited training data. We have tested three different recurrent neural networks for grading. With a transformer, we have achieved an accuracy of 95.67%. ASAG for school children will help detect children with learning problems early. When detected early, teachers can solve learning problems easily. This is the main purpose of this research.
A scoring rubric for automatic short answer grading systemTELKOMNIKA JOURNAL
During the past decades, researches about automatic grading have become an interesting issue. These studies focuses on how to make machines are able to help human on assessing students’ learning outcomes. Automatic grading enables teachers to assess student's answers with more objective, consistent, and faster. Especially for essay model, it has two different types, i.e. long essay and short answer. Almost of the previous researches merely developed automatic essay grading (AEG) instead of automatic short answer grading (ASAG). This study aims to assess the sentence similarity of short answer to the questions and answers in Indonesian without any language semantic's tool. This research uses pre-processing steps consisting of case folding, tokenization, stemming, and stopword removal. The proposed approach is a scoring rubric obtained by measuring the similarity of sentences using the string-based similarity methods and the keyword matching process. The dataset used in this study consists of 7 questions, 34 alternative reference answers and 224 student’s answers. The experiment results show that the proposed approach is able to achieve a correlation value between 0.65419 up to 0.66383 at Pearson's correlation, with Mean Absolute Error (푀퐴퐸) value about 0.94994 until 1.24295. The proposed approach also leverages the correlation value and decreases the error value in each method.
Exploring Semantic Question Generation Methodology and a Case Study for Algor...IJCI JOURNAL
Assessment of student performance is one of the most important tasks in the educational process. Thus, formulating questions and creating tests takes the instructor a lot of time and effort. However, the time spent for learning acquisition and on exam preparation could be utilized in better ways. With the technical development in representing and linking data, ontologies have been used in academic fields to represent the terms in a field by defining concepts and categories classifies the subject. Also, the emergence of such methods that represent the data and link it logically contributed to the creation of methods and tools for creating questions. These tools can be used in existing learning systems to provide effective solutions to assist the teacher in creating test questions. This research paper introduces a semantic methodology for automating question generation in the domain of Algorithms. The primary objective of this approach is to support instructors in effectively incorporating automatically generated questions into their instructional practice, thereby enhancing the teaching and learning experience.
IRJET- An Automated Approach to Conduct Pune University’s In-Sem ExaminationIRJET Journal
This document describes a proposed automated system for evaluating descriptive answers on technical exams in Pune University. The system would take students' written answers as input and assess them based on predefined model answers, scoring answers based on keywords, context, structure, and cosine similarity to the model answers. This would help address issues with current evaluation methods like human error, inconsistency, and resource intensiveness. The proposed system uses natural language processing and machine learning techniques to classify answers and determine scores. It aims to provide a more accurate and efficient evaluation process.
Automatic Essay Grading System For Short Answers In English LanguageDon Dooley
This document summarizes a research paper that proposes an automatic essay grading system for short answers in English. The system generates alternative model answers using synonyms and evaluates student answers by comparing them to model answers using three algorithms: Common Words, Longest Common Subsequence, and Semantic Distance. The system was tested on 40 questions answered by three students, achieving 82% correlation with human grading, outperforming other state-of-the-art systems.
Convolutional recurrent neural network with template based representation for...IJECEIAES
Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.
Architecture of an ontology based domain-specific natural language question a...IJwest
The document summarizes the architecture of an ontology-based domain-specific natural language question answering system. The proposed architecture defines four main modules: 1) question processing which analyzes and classifies questions and reformulates queries, 2) document retrieval which retrieves relevant documents, 3) document processing which processes retrieved documents, and 4) answer extraction which extracts and generates responses. Natural language processing techniques and ontologies are used to analyze questions and documents and extract relationships and answers. The system aims to generate concise, specific answers to natural language questions in a given domain and achieved 94% accuracy in testing.
Machine Learning Techniques with Ontology for Subjective Answer Evaluationijnlc
Computerized Evaluation of English Essays is performed using Machine learning techniques like Latent
Semantic Analysis (LSA), Generalized LSA, Bilingual Evaluation Understudy and Maximum Entropy.
Ontology, a concept map of domain knowledge, can enhance the performance of these techniques. Use of
Ontology makes the evaluation process holistic as presence of keywords, synonyms, the right word
combination and coverage of concepts can be checked. In this paper, the above mentioned techniques are
implemented both with and without Ontology and tested on common input data consisting of technical
answers of Computer Science. Domain Ontology of Computer Graphics is designed and developed. The
software used for implementation includes Java Programming Language and tools such as MATLAB,
Protégé, etc. Ten questions from Computer Graphics with sixty answers for each question are used for
testing. The results are analyzed and it is concluded that the results are more accurate with use of
Ontology.
AN AUTOMATED MULTIPLE-CHOICE QUESTION GENERATION USING NATURAL LANGUAGE PROCE...kevig
Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language
Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data.
Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming
and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for
Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are
important words in a given lesson material. To validate that the system is not perverse, five lesson materials
were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the
teacher were compared to the auto-generated keywords and the result shows that the system was capable of
extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a
user-friendly interface for easy accessibility.
A scoring rubric for automatic short answer grading systemTELKOMNIKA JOURNAL
During the past decades, researches about automatic grading have become an interesting issue. These studies focuses on how to make machines are able to help human on assessing students’ learning outcomes. Automatic grading enables teachers to assess student's answers with more objective, consistent, and faster. Especially for essay model, it has two different types, i.e. long essay and short answer. Almost of the previous researches merely developed automatic essay grading (AEG) instead of automatic short answer grading (ASAG). This study aims to assess the sentence similarity of short answer to the questions and answers in Indonesian without any language semantic's tool. This research uses pre-processing steps consisting of case folding, tokenization, stemming, and stopword removal. The proposed approach is a scoring rubric obtained by measuring the similarity of sentences using the string-based similarity methods and the keyword matching process. The dataset used in this study consists of 7 questions, 34 alternative reference answers and 224 student’s answers. The experiment results show that the proposed approach is able to achieve a correlation value between 0.65419 up to 0.66383 at Pearson's correlation, with Mean Absolute Error (푀퐴퐸) value about 0.94994 until 1.24295. The proposed approach also leverages the correlation value and decreases the error value in each method.
Exploring Semantic Question Generation Methodology and a Case Study for Algor...IJCI JOURNAL
Assessment of student performance is one of the most important tasks in the educational process. Thus, formulating questions and creating tests takes the instructor a lot of time and effort. However, the time spent for learning acquisition and on exam preparation could be utilized in better ways. With the technical development in representing and linking data, ontologies have been used in academic fields to represent the terms in a field by defining concepts and categories classifies the subject. Also, the emergence of such methods that represent the data and link it logically contributed to the creation of methods and tools for creating questions. These tools can be used in existing learning systems to provide effective solutions to assist the teacher in creating test questions. This research paper introduces a semantic methodology for automating question generation in the domain of Algorithms. The primary objective of this approach is to support instructors in effectively incorporating automatically generated questions into their instructional practice, thereby enhancing the teaching and learning experience.
IRJET- An Automated Approach to Conduct Pune University’s In-Sem ExaminationIRJET Journal
This document describes a proposed automated system for evaluating descriptive answers on technical exams in Pune University. The system would take students' written answers as input and assess them based on predefined model answers, scoring answers based on keywords, context, structure, and cosine similarity to the model answers. This would help address issues with current evaluation methods like human error, inconsistency, and resource intensiveness. The proposed system uses natural language processing and machine learning techniques to classify answers and determine scores. It aims to provide a more accurate and efficient evaluation process.
Automatic Essay Grading System For Short Answers In English LanguageDon Dooley
This document summarizes a research paper that proposes an automatic essay grading system for short answers in English. The system generates alternative model answers using synonyms and evaluates student answers by comparing them to model answers using three algorithms: Common Words, Longest Common Subsequence, and Semantic Distance. The system was tested on 40 questions answered by three students, achieving 82% correlation with human grading, outperforming other state-of-the-art systems.
Convolutional recurrent neural network with template based representation for...IJECEIAES
Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.
Architecture of an ontology based domain-specific natural language question a...IJwest
The document summarizes the architecture of an ontology-based domain-specific natural language question answering system. The proposed architecture defines four main modules: 1) question processing which analyzes and classifies questions and reformulates queries, 2) document retrieval which retrieves relevant documents, 3) document processing which processes retrieved documents, and 4) answer extraction which extracts and generates responses. Natural language processing techniques and ontologies are used to analyze questions and documents and extract relationships and answers. The system aims to generate concise, specific answers to natural language questions in a given domain and achieved 94% accuracy in testing.
Machine Learning Techniques with Ontology for Subjective Answer Evaluationijnlc
Computerized Evaluation of English Essays is performed using Machine learning techniques like Latent
Semantic Analysis (LSA), Generalized LSA, Bilingual Evaluation Understudy and Maximum Entropy.
Ontology, a concept map of domain knowledge, can enhance the performance of these techniques. Use of
Ontology makes the evaluation process holistic as presence of keywords, synonyms, the right word
combination and coverage of concepts can be checked. In this paper, the above mentioned techniques are
implemented both with and without Ontology and tested on common input data consisting of technical
answers of Computer Science. Domain Ontology of Computer Graphics is designed and developed. The
software used for implementation includes Java Programming Language and tools such as MATLAB,
Protégé, etc. Ten questions from Computer Graphics with sixty answers for each question are used for
testing. The results are analyzed and it is concluded that the results are more accurate with use of
Ontology.
AN AUTOMATED MULTIPLE-CHOICE QUESTION GENERATION USING NATURAL LANGUAGE PROCE...kevig
Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language
Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data.
Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming
and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for
Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are
important words in a given lesson material. To validate that the system is not perverse, five lesson materials
were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the
teacher were compared to the auto-generated keywords and the result shows that the system was capable of
extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a
user-friendly interface for easy accessibility.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a study that used a back-propagation neural network to estimate students' word recognition abilities based on their performance on a vocabulary test. The study collected test results from 83 elementary school students and used their scores on different word frequency groups as input for the neural network model. The model was trained and tested, showing high correlation between estimated and actual vocabulary volumes. The results demonstrated that a back-propagation neural network can accurately estimate word recognition and could be an effective alternative to traditional statistical methods.
This document summarizes a journal article that evaluates an e-assessment system for automatically scoring short-answer free-text questions. The system uses natural language processing techniques to match student responses to predefined model answers. A study was conducted using this system to assess Open University students. Results found the system could accurately score responses, with accuracy similar to or greater than human markers. The system provides immediate feedback to students to help them learn from mistakes.
Comparison Intelligent Electronic Assessment with Traditional Assessment for ...CSEIJJournal
In education, the use of electronic (E) examination systems is not a novel idea, as E-examination systems
have been used to conduct objective assessments for the last few years. This research deals with randomly
designed E-examinations and proposes an E-assessment system that can be used for subjective questions.
This system assesses answers to subjective questions by finding a matching ratio for the keywords in
instructor and student answers. The matching ratio is achieved based on semantic and document similarity.
The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and
grading. A survey and case study were used in the research design to validate the proposed system. The
examination assessment system will help instructors to save time, costs, and resources, while increasing
efficiency and improving the productivity of exam setting and assessments.
COMPARISON INTELLIGENT ELECTRONIC ASSESSMENT WITH TRADITIONAL ASSESSMENT FOR ...cseij
In education, the use of electronic (E) examination systems is not a novel idea, as E-examination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
COMPARISON INTELLIGENT ELECTRONIC ASSESSMENT WITH TRADITIONAL ASSESSMENT FOR ...cseij
In education, the use of electronic (E) examination systems is not a novel idea, as E-examination systems
have been used to conduct objective assessments for the last few years. This research deals with randomly
designed E-examinations and proposes an E-assessment system that can be used for subjective questions.
This system assesses answers to subjective questions by finding a matching ratio for the keywords in
instructor and student answers.
COMPARISON INTELLIGENT ELECTRONIC ASSESSMENT WITH TRADITIONAL ASSESSMENT FOR ...cseij
In education, the use of electronic (E) examination systems is not a novel idea, as E-examination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity.
The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
A hybrid composite features based sentence level sentiment analyzerIAESIJAI
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
prediction’s accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
A Flowchart-based Programming Environment for Improving Problem Solving Skill...Cynthia Velynne
The document describes a Flowchart-based Programming Environment (FPE) developed to improve problem solving skills for novice computer science students. FPE uses an automatic text-to-flowchart conversion approach to convert a programming problem stated in English text into a corresponding flowchart without human intervention. This allows students to focus on designing solutions in the form of flowcharts rather than programming syntax. The system was evaluated positively by 50 undergraduate students. Results suggest further developing FPE's text-to-flowchart conversion using a multi-agent system could make early programming learning more encouraging for students.
STUDENTS’PATTERNS OF INTERACTION WITH A MATHEMATICS INTELLIGENT TUTOR:LEARNIN...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
STUDENTS’PATTERNS OF INTERACTION WITH A MATHEMATICS INTELLIGENT TUTOR:LEARNIN...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
Student's Patterns of Interaction with a Mathematics Intelligent Tutor: Learn...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be predictors of final marks in the foundation mathematics course with = 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random. Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner were able to retain their mastery of learning after the summative assessment whereas the students who chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide them to choose the correct sequence of topics.
Student's Patterns of Interaction with a Mathematics Intelligent Tutor: Learn...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
Application of hidden markov model in question answering systemsijcsa
By the increase of the volume of the saved information on web, Question Answering (QA) systems have been very important for Information Retrieval (IR). QA systems are a specialized form of information retrieval. Given a collection of documents, a Question Answering system attempts to retrieve correct answers to questions posed in natural language. Web QA system is a sample of QA systems that in this system answers retrieval from web environment doing. In contrast to the databases, the saved information on web does not follow a distinct structure and are not generally defined. Web QA systems is the task of automatically answering a question posed in Natural Language Processing (NLP). NLP techniques are used in applications that make queries to databases, extract information from text, retrieve relevant documents from a collection, translate from one language to another, generate text responses, or recognize spoken words converting them into text. To find the needed information on a mass of the non-structured information we have to use techniques in which the accuracy and retrieval factors are implemented well. In this paper in order to well IR in web environment, The QA system in designed and also implemented based on the Hidden Markov Model (HMM)
Suggestion Generation for Specific Erroneous Part in a Sentence using Deep Le...ijtsrd
This document presents a method for generating suggestions for specific erroneous parts of sentences in Indian languages like Malayalam using deep learning. The method uses recurrent neural networks with long short-term memory layers to train a model on input-output examples of sentences and their corrections. The model takes in preprocessed sentence data and generates a set of possible corrections for erroneous parts through multiple network layers. An analysis of the model shows that it can accurately generate suggestions for word length of three, but requires more data and study to handle the complex morphology and symbols of Malayalam. The performance of the method is limited by the hardware used and it could be improved with a more powerful system and additional training data.
The document discusses using support vector machines (SVM) and various lexical, semantic, and syntactic features for question classification. It aims to develop a state-of-the-art machine learning based question classifier. Various features are discussed, including lexical features like n-grams and stemming, syntactic features like question headwords, and semantic features derived from named entity recognition, WordNet senses, and semantic word lists. SVM is used as the classifier to take advantage of its good performance for text classification tasks. The results show that combining these feature types can achieve accurate question classification.
AUTOMATIC QUESTION GENERATION USING NATURAL LANGUAGE PROCESSINGIRJET Journal
The document describes a proposed method for automatic question generation using natural language processing and T5 text-to-text transfer transformer models. The method uses T5 models trained on the Stanford Question Answering Dataset to generate questions from paragraphs of text without requiring extensive grammar rules. The proposed system aims to assist students in learning by generating questions to test their understanding from provided materials.
Generation of Question and Answer from Unstructured Document using Gaussian M...IJACEE IJACEE
The document describes a system that automatically generates questions and answers from an unstructured document. It involves several steps: (1) simplifying complex sentences, (2) generating initial questions using named entities and semantic role labeling, (3) identifying subtopics using LDA and GMNTM models, (4) measuring syntactic correctness of questions, and (5) extracting answers using pattern matching. The system is expected to produce more accurate results compared to using only LDA for subtopic identification, as GMNTM also considers word order and semantics. Key techniques include semantic role labeling, Extended String Subsequence Kernel for similarity measurement, and syntactic tree kernel for question ranking.
QUrdPro: Query processing system for Urdu LanguageIJERA Editor
This document describes QUrdPro, a query processing system for the Urdu language. It proposes an ontology-based architecture that uses natural language processing to analyze user queries in Urdu, formulate queries based on the domain ontology, search documents to extract relevant answers, and return results to the user. The system aims to improve information retrieval for the Urdu language by leveraging ontologies and avoiding users having to sift through large amounts of unstructured text. It discusses related work on question answering systems and outlines the proposed architecture and four-phase process model of QUrdPro.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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Similaire à Deep learning based Arabic short answer grading in serious games
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a study that used a back-propagation neural network to estimate students' word recognition abilities based on their performance on a vocabulary test. The study collected test results from 83 elementary school students and used their scores on different word frequency groups as input for the neural network model. The model was trained and tested, showing high correlation between estimated and actual vocabulary volumes. The results demonstrated that a back-propagation neural network can accurately estimate word recognition and could be an effective alternative to traditional statistical methods.
This document summarizes a journal article that evaluates an e-assessment system for automatically scoring short-answer free-text questions. The system uses natural language processing techniques to match student responses to predefined model answers. A study was conducted using this system to assess Open University students. Results found the system could accurately score responses, with accuracy similar to or greater than human markers. The system provides immediate feedback to students to help them learn from mistakes.
Comparison Intelligent Electronic Assessment with Traditional Assessment for ...CSEIJJournal
In education, the use of electronic (E) examination systems is not a novel idea, as E-examination systems
have been used to conduct objective assessments for the last few years. This research deals with randomly
designed E-examinations and proposes an E-assessment system that can be used for subjective questions.
This system assesses answers to subjective questions by finding a matching ratio for the keywords in
instructor and student answers. The matching ratio is achieved based on semantic and document similarity.
The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and
grading. A survey and case study were used in the research design to validate the proposed system. The
examination assessment system will help instructors to save time, costs, and resources, while increasing
efficiency and improving the productivity of exam setting and assessments.
COMPARISON INTELLIGENT ELECTRONIC ASSESSMENT WITH TRADITIONAL ASSESSMENT FOR ...cseij
In education, the use of electronic (E) examination systems is not a novel idea, as E-examination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
COMPARISON INTELLIGENT ELECTRONIC ASSESSMENT WITH TRADITIONAL ASSESSMENT FOR ...cseij
In education, the use of electronic (E) examination systems is not a novel idea, as E-examination systems
have been used to conduct objective assessments for the last few years. This research deals with randomly
designed E-examinations and proposes an E-assessment system that can be used for subjective questions.
This system assesses answers to subjective questions by finding a matching ratio for the keywords in
instructor and student answers.
COMPARISON INTELLIGENT ELECTRONIC ASSESSMENT WITH TRADITIONAL ASSESSMENT FOR ...cseij
In education, the use of electronic (E) examination systems is not a novel idea, as E-examination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity.
The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
A hybrid composite features based sentence level sentiment analyzerIAESIJAI
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
prediction’s accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
A Flowchart-based Programming Environment for Improving Problem Solving Skill...Cynthia Velynne
The document describes a Flowchart-based Programming Environment (FPE) developed to improve problem solving skills for novice computer science students. FPE uses an automatic text-to-flowchart conversion approach to convert a programming problem stated in English text into a corresponding flowchart without human intervention. This allows students to focus on designing solutions in the form of flowcharts rather than programming syntax. The system was evaluated positively by 50 undergraduate students. Results suggest further developing FPE's text-to-flowchart conversion using a multi-agent system could make early programming learning more encouraging for students.
STUDENTS’PATTERNS OF INTERACTION WITH A MATHEMATICS INTELLIGENT TUTOR:LEARNIN...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
STUDENTS’PATTERNS OF INTERACTION WITH A MATHEMATICS INTELLIGENT TUTOR:LEARNIN...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
Student's Patterns of Interaction with a Mathematics Intelligent Tutor: Learn...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be predictors of final marks in the foundation mathematics course with = 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random. Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner were able to retain their mastery of learning after the summative assessment whereas the students who chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide them to choose the correct sequence of topics.
Student's Patterns of Interaction with a Mathematics Intelligent Tutor: Learn...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
Application of hidden markov model in question answering systemsijcsa
By the increase of the volume of the saved information on web, Question Answering (QA) systems have been very important for Information Retrieval (IR). QA systems are a specialized form of information retrieval. Given a collection of documents, a Question Answering system attempts to retrieve correct answers to questions posed in natural language. Web QA system is a sample of QA systems that in this system answers retrieval from web environment doing. In contrast to the databases, the saved information on web does not follow a distinct structure and are not generally defined. Web QA systems is the task of automatically answering a question posed in Natural Language Processing (NLP). NLP techniques are used in applications that make queries to databases, extract information from text, retrieve relevant documents from a collection, translate from one language to another, generate text responses, or recognize spoken words converting them into text. To find the needed information on a mass of the non-structured information we have to use techniques in which the accuracy and retrieval factors are implemented well. In this paper in order to well IR in web environment, The QA system in designed and also implemented based on the Hidden Markov Model (HMM)
Suggestion Generation for Specific Erroneous Part in a Sentence using Deep Le...ijtsrd
This document presents a method for generating suggestions for specific erroneous parts of sentences in Indian languages like Malayalam using deep learning. The method uses recurrent neural networks with long short-term memory layers to train a model on input-output examples of sentences and their corrections. The model takes in preprocessed sentence data and generates a set of possible corrections for erroneous parts through multiple network layers. An analysis of the model shows that it can accurately generate suggestions for word length of three, but requires more data and study to handle the complex morphology and symbols of Malayalam. The performance of the method is limited by the hardware used and it could be improved with a more powerful system and additional training data.
The document discusses using support vector machines (SVM) and various lexical, semantic, and syntactic features for question classification. It aims to develop a state-of-the-art machine learning based question classifier. Various features are discussed, including lexical features like n-grams and stemming, syntactic features like question headwords, and semantic features derived from named entity recognition, WordNet senses, and semantic word lists. SVM is used as the classifier to take advantage of its good performance for text classification tasks. The results show that combining these feature types can achieve accurate question classification.
AUTOMATIC QUESTION GENERATION USING NATURAL LANGUAGE PROCESSINGIRJET Journal
The document describes a proposed method for automatic question generation using natural language processing and T5 text-to-text transfer transformer models. The method uses T5 models trained on the Stanford Question Answering Dataset to generate questions from paragraphs of text without requiring extensive grammar rules. The proposed system aims to assist students in learning by generating questions to test their understanding from provided materials.
Generation of Question and Answer from Unstructured Document using Gaussian M...IJACEE IJACEE
The document describes a system that automatically generates questions and answers from an unstructured document. It involves several steps: (1) simplifying complex sentences, (2) generating initial questions using named entities and semantic role labeling, (3) identifying subtopics using LDA and GMNTM models, (4) measuring syntactic correctness of questions, and (5) extracting answers using pattern matching. The system is expected to produce more accurate results compared to using only LDA for subtopic identification, as GMNTM also considers word order and semantics. Key techniques include semantic role labeling, Extended String Subsequence Kernel for similarity measurement, and syntactic tree kernel for question ranking.
QUrdPro: Query processing system for Urdu LanguageIJERA Editor
This document describes QUrdPro, a query processing system for the Urdu language. It proposes an ontology-based architecture that uses natural language processing to analyze user queries in Urdu, formulate queries based on the domain ontology, search documents to extract relevant answers, and return results to the user. The system aims to improve information retrieval for the Urdu language by leveraging ontologies and avoiding users having to sift through large amounts of unstructured text. It discusses related work on question answering systems and outlines the proposed architecture and four-phase process model of QUrdPro.
Similaire à Deep learning based Arabic short answer grading in serious games (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
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Deep learning based Arabic short answer grading in serious games
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 1, February 2024, pp. 841~853
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp841-853 841
Journal homepage: http://ijece.iaescore.com
Deep learning based Arabic short answer grading in serious
games
Younes Alaoui Soulimani1
, Lotfi El Achaak2
, Mohammed Bouhorma1
1
Smart Systems and Emergent Technologies Team, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University,
Tangier, Morocco
2
Data and Intelligent Systems Team, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Tangier,
Morocco
Article Info ABSTRACT
Article history:
Received Dec 26, 2022
Revised May 3, 2023
Accepted Jun 4, 2023
Automatic short answer grading (ASAG) has become part of natural
language processing problems. Modern ASAG systems start with natural
language preprocessing and end with grading. Researchers started
experimenting with machine learning in the preprocessing stage and deep
learning techniques in automatic grading for English. However, little
research is available on automatic grading for Arabic. Datasets are important
to ASAG, and limited datasets are available in Arabic. In this research, we
have collected a set of questions, answers, and associated grades in Arabic.
We have made this dataset publicly available. We have extended to Arabic
the solutions used for English ASAG. We have tested how automatic
grading works on answers in Arabic provided by schoolchildren in 6th grade
in the context of serious games. We found out those schoolchildren
providing answers that are 5.6 words long on average. On such answers,
deep learning-based grading has achieved high accuracy even with limited
training data. We have tested three different recurrent neural networks for
grading. With a transformer, we have achieved an accuracy of 95.67%.
ASAG for school children will help detect children with learning problems
early. When detected early, teachers can solve learning problems easily. This
is the main purpose of this research.
Keywords:
Automated short answer
grading
Bidirectional encoder
representations from
transformers
Long-short-term-memory
Machine learning
Natural language processing
Serious games
Transformer
This is an open access article under the CC BY-SA license.
Corresponding Author:
Younes Alaoui Soulimani
Smart Systems and Emergent Technologies Team, Faculty of Sciences and Techniques of Tangier,
Abdelmalek Essaâdi University
P.O. Box 416, Tangier, Morocco
Email: younes.alaoui@amana.ac.ma
1. INTRODUCTION
Assessment and evaluation of learning are important steps in learning and knowledge transmission
processes. Instructional design processes that focus on designing and developing learning systems [1],
include always a phase called “develop assessment instruments” [2]. Teachers and instruction designers will
create assessment tools like exams, assignments, or quizzes. They will usually create different types of
questions, which answers are true/false, multiple choice, matching, numerical values, essay, or short answers.
Short answer questions require students to answer in free text composing some sentences, typically one or
two. This type of question has the advantage of requiring students to construct an answer by themselves,
rather than selecting answers from predetermined lists.
Serious games refer currently to video games designed to train people or to transmit learning.
Serious games can complement classroom transmission or help with distance learning. The development of
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serious games involves pedagogy, didactic, learning design, and game design [3]. Assessment of learning
within games is an important feature that helps make learning effective in serious games [4].
Answers written in free text such as short answers and essays have traditionally been absent from
computerized tests and serious games because they were considered difficult to evaluate and grade
automatically [5], [6]. Because of this challenge, automatic short answer grading (ASAG) has become a
research problem. Burrows et al. [5] classified the different approaches tried on ASAG problems in five eras,
the fourth one being machine learning. Recent advances in natural language processing (NLP) as well as in
machine learning applied to NLP are providing promising results on ASAG problems [7]–[9]. Different
ASAG applications started emerging and active research in this field has developed [5].
The objective of this research is to extend automated grading based on machine learning to
questions and answers written in Arabic. Recent research has tested deep learning approaches on ASAG of
answers written in English mainly. The approaches of these researchers seemed generic enough to adapt to
Arabic. We wanted also to extend and test the same approaches on answers collected initially in Arabic and
not originating from datasets translated from English.
This research targeted questions and answers aimed at schoolchildren from fifth and sixth grade and
aged 11 and 12 years old. We have collected a dataset for this research. We have used a standard NLP
pipeline. We have leveraged an existing machine-learning algorithm to project Arabic words on numerical
vectors that deep-learning algorithms can work with. To grade our short answers initially written in Arabic,
we have tested three deep learning approaches namely long-short-term-memory (LSTM), transformers, and
bidirectional encoder representations from transformers (BERT). We have deployed our automatic grading in
an operational environment and tested this grading in the context of continuous learning evaluation and
serious games. We have also made the dataset available to other research projects.
The organization of the paper is as follows. We review the state of the art in section 2. We present
our research method in section 3. We discuss the results of this research in section 4. We summarize this
work and describe possible enhancements and future work in the last section.
2. STATE OF THE ART
ASAG grades answers written in free text and leverages the approach from NLP. The NLP
approaches currently used for ASAG are exploring deep learning models with recurrent neural networks.
Kumar et al. [10], Prabhudesai and Duong [11], and Xia et al. [12] have explored LSTM-based models for
ASAG. Alikaniotis et al. [13] have introduced a model based on LSTM for text scoring and are able to
discover which specific words impact the score. Roy et al. [14] have proposed a technique to overcome the
need to have labeled training data and graded student answers for every assessment. In the first stage, they
used a classifier of student answers coupled with a classifier of similarity with respect to model answers. In
the second stage, they used a canonical correlation analysis based on transfer learning to build the classifier
ensemble for questions having no labeled data.
Riordan et al. [7] have carried out a series of experiments across several short answer scoring
datasets. They took as a reference the architecture of the neural network used by Taghipour and Ng [15]. This
neural network provided good performances on automated essay scoring. The network leveraged a
convolutional neural networks (CNN) architecture with regression and a simple LSTM. Zhang et al. [16]
addressed the grading of open-ended questions. These questions do not usually have a limited number of
reference answers. Students can express opinions or personal thoughts on these questions. They have used a
deep learning model that integrates both domain-general and domain-specific information. The proposed
model used an LSTM to classify word sequence information. The dataset had about 16,000 sample answers
related to seven reading comprehension questions.
Other researchers have used transformer and transfer learning in their systems to train models.
Camus and Filighera [17] have fine-tuned existing and already trained transformer based architectures. They
have explored the transfer learning from one dataset to another one and its impact on generalization and
performance. Condor [18] has used BERT as a tool to assist instructors with ASAG. Condor targeted
situations where final human judgment is considered necessary.
2.1. ASAG datasets
There is a variety of datasets already listed in the literature to train and test ASAG models. As we
know, well-structured datasets lead to good results. The Hewlett Foundation [19] has released a dataset called
ASAP to train and benchmark ASAG systems. ASAP is currently available on Kaggle. This dataset contains
about 10,686 samples belonging to 8 different sets of essays. Each essay has an average of 150 to 550 words
response. Each essay is followed by one or more scores given by human graders. The objective is to match
the “expert human graders for each essay.”
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Some researchers have used datasets collected during university courses. The dataset used by
Mohler and Mihalcea [20] at the University of Texas consists of 80 questions collected from a computer
science course named Data Structures. The questions were used in multiple assignments and two
examinations. They have collected answers through an online learning system. The size of the dataset is 80
questions, 2,273 responses provided by 31 students and 2 human expert graders. Menini et al. [21] released a
dataset named Statistics to test short answer grading. They have built the dataset from statistics exams.
2.2. ASAG in Arabic
Gomaa and Fahmy [22] have pioneered ASAG for Arabic. They have collected a dataset in Arabic
that has 61 questions. Each question had about ten student answers, and all answers were labeled with grades.
They have built the dataset from the Environmental Science course of the Egyptian curriculum. For grading,
they have used a text similarity-based grading that measures the similarity between student answers and
reference answers. They have not used any automatic grading via a machine learning approach.
Nael et al. [23] researched a deep learning-based system to score short answers in Arabic and
achieved good performances. However, they have not used a dataset built out of questions and answers
written initially in Arabic. They have used a translated version to Arabic of an English test dataset called
ASAP short answer scoring.
As we wanted to explore automated grading for schoolchildren in Arabic, we wanted to know how
such data would look alike in reality. Our objective motivated us to collect and build our own dataset
following the best practices already mentioned in the literature. Because data is key to machine learning
algorithms, we targeted collecting real and genuine data from schoolchildren in Arabic to create good models
that can handle our problems well.
3. OUR RESEARCH METHOD
All the datasets cited in the literature were collected either manually through forms or dedicated web
applications [24], [25] or using an automated mechanism like web scraping. We have built our dataset
manually from answers provided by schoolchildren aged between 11 to 12 years old and graded by a teacher.
Figure 1 illustrates an excerpt of the dataset. We have used Google Forms to collect the answers. All
participants were studying in the sixth grade of primary education in Morocco. The schoolchildren answered
18 questions related to the Islamic education course. We have collected 1,276 answers. A teacher has
evaluated and graded all answers. The grades were between 0 and 2: 0 for completely incorrect, 1 for
partially correct, and 2 for correct.
Figure 1. Islamic Education short answer dataset (answers 1 to 4 read Gabriel, Gabriel, Gabriel peace be
upon him, our master Gabriel peace be upon him, respectively)
Schoolchildren have answered these questions at home on a computer or on mobile. We have
noticed that 75% of the answers have 8 words or less. Figure 2 shows the number of answers for a given
number of words. Table 1 statistical indicators on the length of answers provide some statistical indicators
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related to the number of characters and the number of words in the dataset. Compared to other datasets
collected in university or from adult responses, the answers that we have collected have fewer words. We
also looked at the data from the score perspective to ensure that all scores were present. We found out that it
is important to ensure that all scores are present in the dataset to help machine learning algorithms work
correctly. Table 2 provides the number of answers per score.
Table 1. Statistical indicators on the length of answers
Number of characters Number of words
Mean value 29.0 5.6
Standard deviations 32.4 6.2
Quartile 1 6 1
Quartile 2 18 3
Quartile 3 40 8
Minimal value 0 0
Maximal value 311 55
Figure 2. Number of answers per number of words
Table 2. Number of answers per score
Scores Number of answers
0 27%
1 20%
2 52%
3.1. NLP pipeline for ASAG
For Arabic NLP, researchers are currently using pipelines and architectures similar to what is being
used for English NLP [26]. On the other hand, the NLP pipelines used for ASAG are similar to the generic
pipelines used for other NLP applications. For this research, we have used a pipeline similar to the ones used
for English ASAG when leveraging NLP and machine learning. The adopted NLP pipeline was composed of
three main stages as illustrated in Figure 3. The classification or grading happens in the third stage. We have
used two stages upfront to preprocess the text and transform words into numerical vectors before
classification can be applied.
The first stage is text processing and includes tasks like segmentation, tokenization, stop word
removal, and stemming or lemmatization. The output of this first stage is the list of important words
composing the initial answer but reduced to their root words. Stemming and lemmatization are both used in
NLP to normalize words by reducing each word to it is root or dictionary form. Stemming algorithms chop
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off suffixes and are fast, but they may reduce words to wrong roots or non-existing words. Lemmatization
algorithms apply a contextual analysis to words and link them on average to more appropriate root words.
However, if the text is long, then lemmatization takes considerably more time.
The second stage called feature extraction or embedding is where we map each word with a
numerical vector belonging to a relatively low-dimensional continuous space, called embedding space. An
important requirement for this mapping is that words sharing similar meanings or semantics should translate
in the embedding space to numerical vectors that are close to each other [26]. Each dimension of the
embedding space is usually linked to some semantic features of our vocabulary. Table 3 shows the example
of embedding vectors associated with six different words and provides an example of six words projected on
an embedding space of three dimensions where each dimension is associated with a pure semantic feature,
namely {Person; Location; Duration}.
Figure 3. NLP pipeline architecture for ASAG
Table 3. Example of embedding vectors associated with six different words
Vocabulary
Prophet Messenger Mecca Year Medina Preach
Embedding dimensions Person 0.97 0.95 0.01 0.06 0.07 0.26
Location 0.12 0.19 0.76 0.23 0.72 0.11
Duration 0.36 0.43 0.23 0.98 0.12 0.24
Words used in similar contexts usually have similar meanings or semantics, thus these words must
be close to each other along some dimensions of the embedding space. Different techniques are used for
feature extraction. In this research, we have used word2vec [27] to generate the embedding vectors. The
word2vec algorithm leverages machine-learning techniques and is key to NLP. The dimension of the
embedding space was 300. This means that all the words of the Arabic corpus that we have used were
projected on vectors of dimension 300.
The third stage performs the classification task. This stage leverages deep learning algorithms and
implements our machine learning models. We have first trained these models on our data. We then tested
them on unseen answers. For both training and testing, we have fed these models with data that went through
the two first stages of our pipeline.
For both LSTM and transformer models, we have used the Gensim toolkit during lemmatization,
tokenization, and word embedding [28]. Gensim addresses many common NLP tasks and provides an
implementation of the word2vec algorithm. We usually train the word2vec algorithm on a corpus and
associated texts to generate a word vector encoding how to map each word from the corpus on a numerical
low-dimension vector. In our research, we have used word2vec with “Wiki.ar.vec” as the pre-trained word
vector [26]. “Wiki.ar.vec” was trained on ar.wikipedia. For the BERT model, we have performed the
tokenization task using a pre-trained model called “Bashar-talafha/multi-dialect-bert-base-arabic” [29].
3.2. Deep learning architectures used for ASAG
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Our approach to Arabic ASAG was to test and adapt the models used for English ASAG. The first
unknown was the quality of the word vectors. A second one was how the models would behave on answers
written in simple words by schoolchildren. We have tested an LSTM architecture [30], a transformers-based
architecture [31], and a transfer learning by fine-tuning a BERT pre-trained model [32]. This section presents
the results.
3.3. LSTM model
The architecture based on the LSTM model is composed of 7 layers and is described in Figure 4.
The input layer has 54 nodes because the longest response in our system can have 54 words. LSTM is a
recurrent network and will iterate on 54 words. The embedding layer has 300 nodes, 300 being the length of
the vector after word2vec encoding. The LSTM layer has 64 units, followed by two dropout layers with 64
and 32 nodes, followed by one flattened layer with 3456 nodes. As we have three possible final grades
{0, 1, 2}, the output layer has 3 nodes to provide the result of our classification task. In total, the trainable
parameters of the LSTM model are around 204,163 parameters.
Figure 4. LSTM layer design for ASAG
For the ASAG problem, we found that the hyper-parameters used with LSTM have an impact on
learning and test results. We have tested different hyper-parameters in Table 4. The Hyper-parameter of
LSTM Architecture for ASAG. l is the best hyper-parameter found to train the LSTM model for Arabic
ASAG. We present the performances achieved with these parameters in section 6.
Table 4. Hyper-parameter of LSTM architecture for ASAG
Batch size Learning Rate Beta 1/2 Epochs Optimizer Regularization
256 0.001 0.9 150 Adam Dropout early stopping
3.4. Transformer model
The architecture based on transformer model is composed of 6 layers as shown in Figure 5. The
input layer has 54 nodes, 54 being the length of the longest response of our dataset. Follows a token position
embedding layer with 300 nodes where 300 is the size of the computed embedding vectors. The transformer
layer also has 300 nodes, followed by a max-pooling layer for dimensionality reduction, then one dropout
layer with 300 nodes and finally a 3 nodes output layer to perform the classification task. The trainable
parameters of the transformer model are about 768,311 parameters.
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For transformer, we have also tested several hyper-parameters and compared results. Table 5 hyper-
parameter of transformer architecture for ASAG presents the hyper-parameters that provided the best
performances. We discuss the performances achieved in section 6.
Figure 5. Transformer layer design for ASAG
Table 5. Hyper-parameter of transformer architecture for ASAG
Batch size Learning Rate Beta 1/2 Epochs Optimizer Regularization
256 0.001 0.9 25 Adam Dropout early stopping
3.5. BERT model
The last tested architecture is based on BERT Model. We have used an architecture composed of 6
layers, see Figure 6. The input layer has 309 nodes. The BERT layer has 110,617,344 parameters, followed
by two dense layers with 64 and 32 nodes and two dropout layers with 64 and 32 nodes. The output layer has
3 nodes to perform the classification task. Overall, the trainable parameters of the BERT model are about
51,395 parameters.
Figure 6. BERT layer design for ASAG
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As for LSTM and transformer, the performance of a BERT model on ASAG varies with the
parameters used. We have tested and experimented with different parameters before finding a set of
parameters that provided good performances on our ASAG problem. We have listed these parameters in
Table 6. We present and discuss the performances achieved with these parameters in section 4.
Table 6. Hyper-parameter of BERT architecture for ASAG
Batch size Learning rate Beta 1/2 Epochs Optimizer Regularization
256 0.001 0.9 100 Adam Dropout early stopping
3.6. Deployment to an operating environment
We wanted to test the deployment and the behavior of the grading service in an operating
environment. In operations, short answers need to go through text processing and feature extraction before
classification. We have embedded the 3 stages of our NLP pipeline in our deployment server, as shown in
Figure 7.
To execute the machine learning models in our operating environment, we have installed
TensorFlow [33] on our server and used it as an inference engine to execute our classification models. We
have deployed our models from Colab and Kaggle after training and tuning to TensorFlow in a ‘H5’
container [34]. We wanted to make the automatic grading service available to multiple front ends. We made
this service available through a service-oriented architecture (REST API). We consumed this service through
a web and a mobile application. The service-oriented architecture has proved flexible to deploy and operate
the trained models.
Figure 7. Web application architecture
4. RESULTS AND DISCUSSION
To evaluate the performance of each model, we have used four evaluation metrics: accuracy,
precession, recall, and Cohen kappa. The values of these metrics for the LSTM model are listed in Table 7,
while Figure 8 plots the metrics against the number of epochs. For the transformer model, Table 8 provides
the metrics, and Figure 9 plots the metrics against epochs. For the BERT model, Table 9 and Figure 10
provide the values of the metrics and the graph plotting the metrics against epochs.
Table 7. LSTM model for ASAG metrics results
Accuracy Precision Recall Cohen Kappa Loss
Training 83.95% 86.34% 78.78% 71.11% 0.4305
Test 69.62% 73.50% 62.03% 45.39% 0.727
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Figure 8. LSTM model for ASAG metrics’ graphs
Table 8. Transformer model for ASAG metrics results
Accuracy Precision Recall Cohen Kappa Loss
Training 95.67% 95.67% 95.67% 92.49% 0.090
Test 77.22% 77.35% 76.37% 59.96% 0.9340
Figure 9. Transformer model for ASAG metrics’ graphs
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Table 1. BERT model for ASAG metrics results
Accuracy Precision Recall Cohen Kappa Loss
Training 85% 88.68% 78.56% 73.84% 0.4157
Test 71.31% 77.08% 66.67% 47.61% 0.4761
Figure 10. BERT model for ASAG: metrics’ graphs
4.1. Discussion
From the results of the model evaluation section, we notice that the transformers model has the best
accuracy with 95.67%, followed by the BERT model with an accuracy of 88.68%, and then LSTM with an
accuracy of 83.95%. The same remark applies to the other metrics like precision, recall, and Cohen kappa.
When we see the metrics graphs of each proposed model, we notice that the transformer model overfits faster
compared to both LSTM and BERT. The difference between the training curve and the test curve becomes
larger as the epochs increase. This is due to the complexity of the model architecture and the size of the used
dataset. In order to deal with this problem, we have used two technics usually used for this kind of problem.
The first one consisted of using the dropout layers to reduce the complexity of the model. The second one
consisted of using an early stopping technique.
We have compared our models with other models from the literature as illustrated in Table 10. As
for accurate results on ASAG, we can conclude that we have followed a good approach and achieved results
comparable to the other research. We can improve our results by varying some hyper-parameters like
architecture or regularization techniques, by adding more data to the dataset, or by using dedicated
lemmatization and word embedding.
Table 10. Accuracy results on ASAG
Model Accuracy
ASAG based Bi-LSTM, Conneau et al. [35] 76 %
ASAG based LSTM, Saha et al. [6] 79.26 %
ASAG based LSTM, Liu et al. [36] 88.9 %
Proposed ASAG based LSTM 83.95%
ASAG based transformers, Wang et al. [37] 80.17 %
ASAG based transformers, Camus and Filighera [17] 79.7 %
Proposed ASAG based transformers 95.67%
Proposed ASAG based BERT 85%
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Deep learning based arabic short answer grading in serious games (Younes Alaoui)
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5. CONCLUSION
We have shown in this research that we can set up an ASAG system for schoolchildren and for
Arabic. ASAG systems in Arabic can leverage and adapt natural language processing pipelines and deep
learning architectures used for English ASAG. We have used a lemmatization adapted to Arabic to
transform words into their dictionary forms. As deep learning algorithms require us to map or embed
Arabic words into low-dimension numerical vectors, we have used the open-source word2vec algorithm
trained on Arabic Wikipedia to compute these numerical embedding vectors. Our ASAG system targeted
schoolchildren from fifth and sixth grades aged 11 and 12 years old. The answers given by these
schoolchildren turned out to be short and composed of 5.6 words on average. The collected dataset proved
large enough to train our model and to provide good results. We have also made this dataset public and
available for future research projects. Moreover, service-oriented architecture proved beneficial to
deploying our models to production in an environment providing ASAG services. We were able to
consume the ASAG service via different front ends.
We have used a word-embedding algorithm trained on Arabic Wikipedia. As the style and
expressions used by schoolchildren are different from what we can find in Wikipedia, one can explore
training a word-embedding algorithm on a corpus made out of school textbooks in Arabic. We can also
improve our ASAG system by adding correction capabilities. This means that the system will correct wrong
or ambiguous answers and propose to schoolchildren how to improve their responses. Finally, Our ASAG
system was trained on one chapter of the curriculum of the fifth grade. One can extend this to cover all
chapters of the fifth grade or of primary education. Such extension will make a continuous evaluation of
learning in primary schools easy and will help teachers detect early schoolchildren with learning problems.
Once detected, teachers can help these schoolchildren overcome their difficulties. With the recent emergence
of large language models, developing ASAG systems to cover full primary curricula and for continuous
evaluation of learning seems a promising direction.
DATA AVAILABILITY
Dataset: https://github.com/FSTT-LIST/GLUPS-ASAG-Dataset
Models: https://www.kaggle.com/code/lotfielaachak/asag-lstm
https://www.kaggle.com/code/lotfielaachak/asag-transformer
https://www.kaggle.com/code/lotfielaachak/asag-bert
REFERENCES
[1] K. Zierer and N. M. Seel, “General didactics and instructional design: eyes like twins a transatlantic dialogue about similarities
and differences, about the past and the future of two sciences of learning and teaching,” SpringerPlus, vol. 1, no. 1, Dec. 2012,
doi: 10.1186/2193-1801-1-15.
[2] W. Dick, L. Carey, and J. O. Carey, “The systematic design of instruction,” Sixth Edition, Allyn & Bacon, pp. 1-400,
2005.
[3] Y. Alaoui, L. El Achaak, A. Belahbib, and M. Bouhorma, “Serious games for sustainable education in emerging countries: An
open-source pipeline and methodology,” in Emerging Trends in ICT for Sustainable Development: The Proceedings of
NICE2020 International Conference, Springer International Publishing, 2021, pp. 399–407, doi: 10.1007/978-3-030-53440-
0_42.
[4] F. Bellotti, B. Kapralos, K. Lee, P. Moreno-Ger, and R. Berta, “Assessment in and of serious games: an overview,” Advances in
Human-Computer Interaction, pp. 1–11, 2013, doi: 10.1155/2013/136864.
[5] S. Burrows, I. Gurevych, and B. Stein, “The eras and trends of automatic short answer grading,” International Journal of
Artificial Intelligence in Education, vol. 25, no. 1, pp. 60–117, Mar. 2015, doi: 10.1007/s40593-014-0026-8.
[6] S. Saha, T. I. Dhamecha, S. Marvaniya, R. Sindhgatta, and B. Sengupta, “Sentence level or token level features for automatic
short answer grading?: Use both,” in Lecture Notes in Computer Science, Springer International Publishing, 2018, pp. 503–517,
doi: 10.1007/978-3-319-93843-1_37.
[7] B. Riordan, A. Horbach, A. Cahill, T. Zesch, and C. M. Lee, “Investigating neural architectures for short answer scoring,” in
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, 2017, pp. 159–168, doi:
10.18653/v1/W17-5017.
[8] P. Willett, “Recent trends in hierarchic document clustering: A critical review,” Information Processing and Management,
vol. 24, no. 5, pp. 577–597, Jan. 1988, doi: 10.1016/0306-4573(88)90027-1.
[9] J. P. Callan, “Passage-level evidence in document retrieval,” in SIGIR ’94, London: Springer London, 1994, pp. 302–310, doi:
10.1007/978-1-4471-2099-5_31.
[10] S. Kumar, S. Chakrabarti, and S. Roy, “Earth mover’s distance pooling over Siamese LSTMs for automatic short answer
grading,” in Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Aug. 2017, pp. 2046–2052,
doi: 10.24963/ijcai.2017/284.
[11] A. Prabhudesai and T. N. B. Duong, “Automatic short answer grading using Siamese bidirectional LSTM based regression,” in
2019 IEEE International Conference on Engineering, Technology and Education (TALE), Dec. 2019, pp. 1–6, doi:
10.1109/TALE48000.2019.9226026.
[12] L. Xia, M. Guan, J. Liu, X. Cao, and D. Luo, “Attention-based bidirectional long short-term memory neural network for short
answer scoring,” in Machine Learning and Intelligent Communications, Springer International Publishing, 2021, pp. 104–112,
doi: 10.1007/978-3-030-66785-6_12.
12. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 841-853
852
[13] D. Alikaniotis, H. Yannakoudakis, and M. Rei, “Automatic text scoring using neural networks,” in Proceedings of the 54th
Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016, pp. 715–725, doi:
10.18653/v1/P16-1068.
[14] S. Roy, H. S. Bhatt, and Y. Narahari, “An iterative transfer learning based ensemble technique for automatic short answer
grading,” arXiv preprint arXiv:1609.04909, Sep. 2016.
[15] K. Taghipour and H. T. Ng, “A neural approach to automated essay scoring,” in Proceedings of the 2016 Conference on
Empirical Methods in Natural Language Processing, 2016, pp. 1882–1891, doi: 10.18653/v1/D16-1193.
[16] L. Zhang, Y. Huang, X. Yang, S. Yu, and F. Zhuang, “An automatic short-answer grading model for semi-open-ended questions,”
Interactive Learning Environments, vol. 30, no. 1, pp. 177–190, Jan. 2022, doi: 10.1080/10494820.2019.1648300.
[17] L. Camus and A. Filighera, “Investigating transformers for automatic short answer grading,” in Lecture Notes in Computer
Science, Springer International Publishing, 2020, pp. 43–48, doi: 10.1007/978-3-030-52240-7_8.
[18] A. Condor, “Exploring automatic short answer grading as a tool to assist in human rating,” in Lecture Notes in Computer Science,
Springer International Publishing, 2020, pp. 74–79, doi: 10.1007/978-3-030-52240-7_14.
[19] ASAP, “The Hewlett foundation: automated essay scoring,” Kaggle. https://www.kaggle.com/competitions/asap-aes (accessed
Dec. 14, 2022).
[20] M. Mohler and R. Mihalcea, “Text-to-text semantic similarity for automatic short answer grading,” in Proceedings of the 12th
Conference of the European Chapter of the Association for Computational Linguistics on-EACL ’09, 2009, pp. 567–575,
doi: 10.3115/1609067.1609130.
[21] S. Menini, S. Tonelli, G. De Gasperis, and P. Vittorini, “Automated short answer grading: a simple solution for a difficult task,”
2019.
[22] W. H. Gomaa and A. A. Fahmy, “Automatic scoring for answers to Arabic test questions,” Computer Speech and Language,
vol. 28, no. 4, pp. 833–857, Jul. 2014, doi: 10.1016/j.csl.2013.10.005.
[23] O. Nael, Y. ELmanyalawy, and N. Sharaf, “AraScore: A deep learning-based system for Arabic short answer scoring,” Array,
vol. 13, Mar. 2022, doi: 10.1016/j.array.2021.100109.
[24] S. Basu, C. Jacobs, and L. Vanderwende, “Powergrading: A clustering approach to amplify human effort for short answer
grading,” Transactions of the Association for Computational Linguistics, vol. 1, pp. 391–402, Dec. 2013, doi:
10.1162/tacl_a_00236.
[25] R. D. Nielsen, W. H. Ward, J. H. Martin, and M. Palmer, “Annotating students’ understanding of science concepts,” in
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08), Marrakech, Morocco,
2008.
[26] A. B. Soliman, K. Eissa, and S. R. El-Beltagy, “AraVec: A set of Arabic word embedding models for use in Arabic NLP,”
Procedia Computer Science, vol. 117, pp. 256–265, 2017, doi: 10.1016/j.procs.2017.10.117.
[27] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint
arXiv:1301.3781, Jan. 2013.
[28] R. Rehurek and P. Sojka, “Gensim-python framework for vector space modelling,” NLP Centre, Faculty of Informatics, Masaryk
University, Brno, Czech Republic, vol. 3, no. 2, 2011.
[29] B. Talafha et al., “Multi-dialect Arabic BERT for country-level dialect identification,” arXiv preprint arXiv:2007.05612, Jul.
2020.
[30] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997,
doi: 10.1162/neco.1997.9.8.1735.
[31] A. Vaswani et al., “Attention is all you need,” Advances in neural information processing systems, Jun. 2017.
[32] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language
understanding,” arXiv:1810.04805, Oct. 2018.
[33] M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” in 12th USENIX symposium on operating systems
design and implementation (OSDI 16), May 2016, pp. 265–283.
[34] HDF Group, “HDF5, hierarchical data format, version 5,” HDF Group, 2022. Accessed: Jun. 11, 2023. [Online], Available:
https://www.loc.gov/preservation/digital/formats/fdd/fdd000229.shtml
[35] A. Conneau, D. Kiela, H. Schwenk, L. Barrault, and A. Bordes, “Supervised learning of universal sentence representations from
natural language inference data,” arXiv preprint arXiv:1705.02364, May 2017.
[36] T. Liu, W. Ding, Z. Wang, J. Tang, G. Y. Huang, and Z. Liu, “Automatic short answer grading via multiway attention networks,”
Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019,
Proceedings, Part II 20, pp. 169–173, Sep. 2019.
[37] Z. Wang, A. S. Lan, A. E. Waters, P. Grimaldi, and R. G. Baraniuk, “A meta-learning augmented bidirectional transformer model
for automatic short answer grading,” EDM, 2019.
BIOGRAPHIES OF AUTHORS
Younes Alaoui Soulimani received a master’s degree from Ecole Polytechnique
Paris, France, in 1989. He is a seasoned engineer in information technology. He is a Ph.D.
student at the University of Abdelmalek Essaadi, Tangier Morocco in the field of serious games,
machine learning, and NLP for education. He can be contacted at email:
younes.alaoui@amana.ac.ma.
13. Int J Elec & Comp Eng ISSN: 2088-8708
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Lotfi El Achaak is an assistant professor and doctor at the Faculty of Sciences
and Technologies, University Abdelmalek Essaadi, Tanger. His recent research and policy
interests concentrate broadly on the areas of serious games, augmented reality, reinforcement
learning, machine learning or deep learning, and NLP for education. He can be contacted at
email: lelaachak@uae.ac.ma.
Mohammed Bouhorma is an experienced academic who has more than 25 years
of teaching and tutoring experience in the area of Information Technology at Abdelmalek
Essaadi University. He received his M.S. and Ph.D. degrees in Electronics and
Telecommunications from INPT in France. He has held a visiting professor position at many
universities (France, Spain, Egypt, and Saudi Arabia). His research interests include IoT, big
data analytics, AI, smart cities technology, and serious games. He can be contacted at email:
mbouhorma@uae.ac.ma.