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A Comprehensive Review of Relevant Techniques used in Course Recommendation System.
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 01 | Jan 2023 www.irjet.net p-ISSN: 2395-0072 © 2023 IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 768 A Comprehensive Review of Relevant Techniques used in Course Recommendation System. Rehan Ahmed Khan, Piyush Kumar Gupta Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India Assistant Professor, Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Through a variety of internet venues, there has been a significant increase in online learning tools in recent years, and COVID lockdown was the cherry on top, making us more web friendly for learning things through the web, therefore defining a course recommendationsystemhasnever been more important. Course recommendation systems try to simplify the difficulty of identifying relevant courses based on ranking and set criteria. Despite all of the advantages, we encounter several difficulties and complexities, such as accuracy, time consumption, and insufficient data. This research examines online recommendation systems using a variety of methods, including Content-Based, Collaborative Filtering, Knowledge-Based, and Hybrid Systems. Datasets, methods, evaluation, and outputs were discovered to be necessary components during the research. And the proposed way of recommendation system in this paper will be highly beneficial in recommending courses to students. Keywords: - e-learning, content based, collaborative filtering, machine learning, online course recommendation 1. INTRODUCTION Since evolution, Humans have been building things or machines that accomplish various tasksinvery simpleways. There are concepts of learning and making others learn or train to make people or machines useful. Like, how we train any algorithm to carryouttasksthatareperformednaturally and effortlessly by machines as humans do. As, Machine Learning enables computers to think and decide on their own like humans do. This is to be one of the noteworthy and most significant developments in the field of computer science. As we move toward a data-centric society, there has been a massive data explosion in recent years. And a large amount of data is meaningless unless it is analysed for hidden patterns. Machine learning approaches allow us to uncover hidden patterns and information about an issue that may be utilised to forecast the future and assist us in different decision-making processes. Learning has undergone a significant transition, moving from the conventional classroom to an online learning environment. Consequently, demonstrating a recommendation system for an online learning environment is a necessity of the hour. Similarly, high-speed internet encourages us to study over the internet; there are a variety of platforms selling a variety of courses, but advising which is most suited to one's personality will make the process go more smoothly. The concept of a recommender system arose from the idea that people rely on others to make routinedecisionsintheirlives. The explosion in the volume and diversity of information available on the internet has aided the development of recommender systems, which has resulted in a rise in profit, user benefits, and industry applications. In order to locate relevant courses for the users, a recommendation technique must establish that a course needs to besuggested.The most common recommendationalgorithmtechniquesareContent Based, Collaborative Filtering, and Hybrid Systems. The popularity of E-learning has resulted inanabundance of courses, making it difficult to choose ones that are appropriate. In contrast to typical learning settings, E- learning allows for the personalization of the learning experience. Traditional learning often accommodates just one learning experience, because in a typical classroom setting, a teacher is frequently dealing with several pupils at the same time in the same area. As a result, each student is forced to get the identical course materials, regardless of their own requirements, traits, orpreferences.Furthermore, determining the optimum learning technique for each learner and implementing it in a real classroom is exceedingly challenging for a teacher. One approach is to leverage recommender system (RS) approaches to personalise the learning process according to each learner's interests and goals. This paper presents a comprehensive overview of current courses recommendation methodologies as well asmachine learning algorithms employed in RS. In order to identify research contributions and limits, the emphasis is on creating categorizationsof recommendationapproaches,ML approaches, as well as methodologies employed,application domains, datasets, evaluation, and input/output data. We used the queries "e-learning recommendation system," "course recommender," "online course recommendation system," "recommendation system," and "e-learning"tofind relevant publications. We found 30 publications from journals that were relevant between 2018 and 2020. The availability of a recommendation system served as the sole selection criteria at this stage. 'E-learning,' 'course suggestion,' and 'aims' that addressed one of RS's remaining issues were the requirements for stage two (i.e., cold-start,
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 01 | Jan 2023 www.irjet.net p-ISSN: 2395-0072 © 2023 IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 769 data sparsity etc.). Twenty articles were discarded since none of the stage-2 requirements weremet.Thefinal studies indicate an RS for e-learning system. 1.1 Inclusion and Exclusion criteria: Papers were chosen for evaluation based on the following criteria: 1. Papers presenting algorithms for online platform recommendation. 2. Publications outlining plans to use Machine Learning to develop RS. 3. Papers containing the outcomes of the technique's evaluation. The following were used as exclusion criteria: 1. Papers to which full access was not granted; 2. Papers that lackeddetaileddiscussionandappraisal of the issue were all eliminated; 2. BACKGROUND DETAILS Online learning with virtual assistance is in increasing demand, and the ed-tech industry has taken full advantage. Personalizing quizzes, courses, andassessmentsforstudents makes the whole process more convenient, as it overcomes the dilemma of picking courses by defining our own criteria. As a response, researchers classified recommendation systems into four categories: CB, CF, knowledge-based, and hybrid. It is not, however, highly optimized, but there is still much opportunity for improvement. Content Based: It is based on user’s past activities and likes in order to recommend things. By categorizing items using particular keywords, content-based filtering aims to learn what the student like, search up those terms in the database, and then offer relatedcourses. Two differentsortsofdataare employed in this filtering. First, the user's preferences, areas of interest, and personal data like age or, occasionally, history. The user vector is used to represent this data. Second, a subject-related piece of information is referred to as an item vector. The item vector includes all of the attributes that may be usedtocomparethingstooneanother. Through Cosine Similarity, Recommendation would be determined. If ‘A’ is the user vector and ‘B’ is an item vector then cosine similarity is given by Collaborative Filtering: Recommendations are made in accordance with user behavior.Theuser'spastiscrucial.Itis predicated on the idea that related users and projects may be prioritized. [1]. The CF system looks for courses that are comparable based on user feedback. User may provide explicit or implicit feedback, such as a numerical rating to reflect how much users valued a certain course, suchas1for dislike and 5 for extremely like, or implicit, such as internet browsing history or reading time for a particular course. Memory-Based and Model Based algorithms are two forms of collaborative filtering algorithms. In the first, items and user data are saved in memory, and then calculations are used to provide estimates based on the data. The Bayesian network, rule-based, and clustering approaches are only a few of the ML algorithms that are used to build the model process. Knowledge Based: A recommendersystemisconsideredto be knowledge-based when it delivers recommendations based on particular queries instead of a user'sratinghistory. Context-based and ontology-based are two of the knowledge-based techniques. Hybrid Model: Hybrid RS incorporates features of both CB and CF by combining individual forecasts into one, adding relevant data to the CF model, or generating final recommendations based on the collective ranks. Recommender systems (RS) built on one of the aforementioned architectures, are prevalent in all recommenders, and serve as a baseline for addressing
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 01 | Jan 2023 www.irjet.net p-ISSN: 2395-0072 © 2023 IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 770 specific recommendation tasks. They should be using ML techniques, and datasets, be susceptible to monitoring and feedback, have output, according to us. Each of these components is covered in further detail in the following sections. 2.1 Data Used Data is gathered implicitly or explicitly by recommendation systems. Implicit data is raw data that may be divided into two types: those that are purposefully obtained from available data streams andthosethatareby-productsofuser activity and it might be utilized or not. Through registration forms and profile information are used to obtain as explicit data from users. They can also be obtained through internet user reviews. 3. RELATED WORKS The major goal of classifying and categorizing previously completed work is to have a thorough knowledge of the recommender system's deployment invariousdomains. The purpose of this paper is to explainhowcourserecommender systems have improved performance in the currentday.The current research has been divided into four categories: Collaborative, CB, Hybrid Solutions, and Knowledge-Based. 3.1 Content- Based Research Content-based recommendations are based on a user's previous choices, which are used to provide suggestions to other users who have similar likes and dislikes. It made use of historical student data to make predictions regarding learning materials. Using fuzzy clusteringanddecisiontrees, learners are categorized as beginner, intermediate, or master depending on their academic history and learning habits. The pattern discovered during categorization also reflects the amount of interest in taking specific courses.[4] Also, based on a set of rules in [5], Learners are presented with learning components and learning objects via adaptive user interface based on rules presented by author. The CB technique has the drawback of relying on previous user experience and being unable to offer fresh content, which may demotivate users and lead to an unwelcome restricted focus. 3.2. Collaborative Filtering Based Research Collaborative filtering (CF) is a way of combining crowd consumption patterns to generate a mathematical model of all students and courses. To put it another way, it'sa method of identifying a group of people who have similar tastes and filtering things based on the opinions of other users. By looking at their favorite themes and incorporatingtheminto a categorized list of ideas, the approach gets over the limitations of content-based strategies. There are two strategies from the perspective of students and courses.The first, known as User-based CF, analyses all students data to identify comparable customers and anticipate their preferences for various courses, a process known as Social Recommendation. The second technique, known as Item- based CF, begins with course similarities, then analyses recent best-sellers and offers discounts to target customers. The Item Recommendation is what it's called. [3] The CF technique viewed lack of data as a major drawback. As it can commonly result in the cold start problem, which explains the difficulty of offering recommendations when the students or courses are fresh. 3.3. Knowledge Based Research Knowledge-based recommendation systems offer recommendations based on information about studentsand courses. Knowledge-based suggestions aren't based on a student's rating, and they don't require any information about that user to make recommendations. To produce appropriate learning material from the web based on learners' needs, researchers utilized an Ontology-Based model with dependency ratios and parse trees. [7] It comprises several layers, methodologies, and algorithms that are not ideal for all sizes of e-learning systems, Recommendation System Content Based Collaborative Filtering Knowledge Based Hybrid Approach Consequently, the knowledge-based strategy is both time- and money- consuming. 3.4. Hybrid Model Based Research A hybrid model framework based on three models was presented by the researchers: CF, Content-Based, and CF with a Self-Organizing map. The approach's main advantage was that it included all of the scores from all of the models, allowing you to take advantage of all of their advantages. Despite the fact that the technique takes longer to suggest than previous models, the accuracy and performance improvements were considerable. [6] The researcher designed a learner learning object predicted (LLOP) matrix and used collaborative filtering to choose the most acceptable learning objects. They then usedtheSPMmethod to identify the most often occurring sequence of learning objects in the matrix. [2] The drawbacks of CB and CFareeliminatedbythisapproach. Despite being more timeconsumingandmoreexpensive,the hybrid procedure is now the best option because to all the benefits listed above.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 01 | Jan 2023 www.irjet.net p-ISSN: 2395-0072 © 2023 IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 771 4. DISCUSSION, CHALLENGES AND ISSUES We evaluated the methodologies and benefits addressed by researchers and classified the recommender systems reported in study. The issues of the categorized recommendation systems were thenanalyzedandgradedas follows: i. Cold Start issue: This problem is generally handled by freshman or courses. This problem develops due to a main lack of rating, which implies that a freshman may not have rated any items, or that any new item may not have been rated by any students. As a result, making the best recommendations in this circumstance is difficult. ii. Data Sparsity: Due to the fact that active users only evaluated a tiny number of things, data sparsity refers tothe difficulty in locating enough reliable similar users. It's tough to deliver an appropriate suggestion list to the target user if the learning item has a low rating. The learner does not rate existing learning items, resulting in data sparsity. iii. Privacy: The privacy hazards of collecting and processing personal data, on the other hand, are frequently underestimated or neglected. Many users are unaware of if and how much of their data is gathered, whether or not it is sold to third parties, or how safely and for how long it is held. 4.1 Machine Learning Algorithms Machine learning and Data Mining employ a variety of statistical approaches and techniques, as well as diverse algorithms such as classification models, clustering, and regression models, to extract insights from massive sets of data. It allows us to forecast the result based on previous events. Various fusions of data mining methods, such as classification and association rule algorithms,clusteringand association rule algorithms, excreta were compared. were compared. They compared the results and discovered that the optimal mixture includes Clustering, Classification, and Association Rule Algorithms.[8] According to the research paper studied, clustering algorithms were the most popular, along with partitioning approaches such as k-means and k-nearest neighbour. Researchers evaluated several combinations of data mining techniques, including classification and association rule algorithms, clustering and association rule algorithms, integrating clustering and classification algorithms into an association rule algorithm, and solely association rule algorithms. [9] Researchers discovered that the optimal combination is clustering with classification and an association rule approach. In [10] researcher proposed a framework where clustering algorithm were applied to the dataset, and then frequent pattern mining algorithm applied which recommend the courses based on historical data. 4.2 In-depth analysis of chosen publications We have provided brief information of the chosen publications in Table 2 below, including recommendation systems, technology employed, target usergroupsfor whom the proposed RS was produced, nation of study, data used in the assessment of the RS, and the proposed system's application area. It also demonstrates the programming language used in developing RS, with innovation-based learning systems as the primary application. The target demographics were schools, universities and their students. Foreign data was used by all systems to evaluate their systems. Categories Techniques Drawbacks Benefits Content Based RC Utilizes historical student data and provide predictions Depends on the user's history. Recommendations are very relevant and transparent. Collaborative Filtering Clustering similar users based on their similar likes and dislikes Data sparsity, Cold start Problem Student can get broader exposure to the courses. Knowledge Based Ontology based Models Time Consuming, Expensive It is not based on user rating Hybrid Model Combination of CB and CF Time Consuming, Expensive Overcome the disadvantages of CF and CB Table 1. Shows techniques and Drawbacks
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 01 | Jan 2023 www.irjet.net p-ISSN: 2395-0072 © 2023 IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 772 References RS System Technology used User group to be targeted Area of application Studying country Data Collected [1] Optimized Collaborative Filtering based on text similarity N/A Universities and Colleges Students Optimizing algorithm China Real world data [2] Learner learning objects recommendation (LLOR) Sequential pattern mining Students Optimized learning algorithm Saudi Arabia Real world data [3] Online Course Multimedia Learning System (OCMLS) MATLAB, Matrix Students Optimized learning path Taiwan Real world data [4] Personalized group- basedrecommendation system PHP, Java and MySQL Student, Academic Institutions Web search in e-learning Malaysia Learning management System [5] Rule-based adaptive user interface N/A Students Learning portal India Learning Management System [6] Hybrid model with CB, CF and CF with self- organizing map Python Students Optimizing algorithm Morocco User’s rating [7] Adaptable and personalised e- recommendation PHP, HTML Students Web based learning portal N/A Data extracted from web searches [8] Combination of clustering with classification and association rule mining N/A Students Optimizing algorithm India Learning Management System, i.e., Moodle [9] Customised clustering algorithm N/A Students Optimizing algorithm N/A Real world data [10] Customised e- recommendationbased on grades N/A Students Web based learning India University databases, online registration form Table 2. In-depth analyses of chosen journal articles
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 01 | Jan 2023 www.irjet.net p-ISSN: 2395-0072 © 2023 IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 773 Based on the search queries andresearchpapersstudied, fig. 2 demonstrates an assessment of the popularity of various RS approaches. CF approaches are utilisedinroughly48%of papers, whereas CB and Knowledge-based are used in around 18% and 14% of papers, respectively, and hybrid models are employed in around 20% of papers, despitetheir significant benefits. 6. CONCLUSIONS AND FUTURE WORKS The fulfilment of personalised educational resource recommendations has become the key challenge that has to be solved in intelligent education. This review is based on e- learning publications that deal with Recommendation System. This paper's key contribution is, different ML methods and a taxonomy of RS systems are presented, assessment measures, and insights into obstacles and concerns that need to be considered in future study. This study found that CF is a common recommendation strategy in e-learning, with the majorityofstudiesseekingtoincrease suggestion quality. Hybrid approaches provide a competitive advantage over the other four strategies, but their popularity is modest. Personalization and adaptive user interface have become important features in e-learning as it will be engaging and interesting to learn through the web, and the research obtained from the exploratory study reported that learners in group four benefited the most from the individualized recommendation links provided by the personalised web- based system. [4] They had much superior learning comprehension than the other three groups since they had more Web search experiences. Data sparsity and latency are major challengesthatstill need to be addressed with RS, as well as privacy and shilling threats. The hybrid model has been proved to tackle the majority of contemporarysystemissues.Furthermore,much research is necessary to improve user confidence and facets of user engagement. REFERENCES [1] Chen, Zheng, Xueyue Liu, and Li Shang."Improvedcourse recommendation algorithmbasedoncollaborativefiltering." 2020 International Conference on Big Data and Informatization Education (ICBDIE). IEEE, 2020. [2] Bourkoukou, Outmane, Essaid El Bachari, and Mohamed El Adnani. "A recommender model in e-learning environment." Arabian Journal for Science and Engineering 42.2 (2017): 607- 617. [3] Chen, Yung-Hui, et al."Recommendationsystem basedon rule-space model of two-phase blue-red tree and optimized learning path with multimedia learning and cognitive assessment evaluation." Multimedia Tools and Applications 76.18 (2017): 18237-18264. [4] Rahman, Mohammad Mustaneer, and Nor Aniza Abdullah. "A personalized group-based recommendation approach for Web search in E-learning." IEEE Access 6 (2018): 34166-34178. [5] Kolekar, Sucheta V.; Pai, Radhika M.; M. M., Manohara Pai (2018). Rule based adaptive user interface for adaptive E learning system.EducationandInformationTechnologies,(), –. doi:10.1007/s10639-018-9788-1 [6] Afoudi, Yassine, Mohamed Lazaar, and Mohammed Al Achhab. "Hybrid recommendation system combined contentbased filtering and collaborative prediction using artificial neural network."SimulationModellingPractice and Theory 113 (2021):102375. [7] Aeiad, Eiman, and Farid Meziane. "An adaptable and personalised E-learning system applied to computerscience Programmes design." Education and Information Technologies 24.2 (2019): 1485-1509. [8] Dol Aher, Sunita & Lobo, Louis. (2012).BestCombination of Machine Learning Algorithms for Course RecommendationSystemin E-learning.International Journal of Computer Applications. 41. 1-10. 10.5120/5542-7598. [9] Ricci, Francesco; Rokach, Lior; Shapira, Bracha; Kantor, Paul B. (2011). Recommender Systems Handbook || Data Mining Methods for Recommender Systems., 10.1007/978-
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 01 | Jan 2023 www.irjet.net p-ISSN: 2395-0072 © 2023 IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 774 0-387-85820-3(Chapter 2), 39–71.doi:10.1007/978-0-387- 85820-3_2 [10] Mondal, Bhaskar, et al. "A course recommendation system based on grades." 2020 international conference on computer science, engineering and applications (ICCSEA). IEEE, 2020. [11] Lalitha, T. B., and P. S. Sreeja."Personalisedself-directed learning recommendation system." Procedia Computer Science 171 (2020): 583-592. [12] George, Gina, and Anisha M. Lal. "Review of ontology based recommender systems in e-learning." Computers & Education 142 (2019): 103642. [13] Ali, Sadia, et al. "Enabling recommendation system architecture in virtualized environment for e-learning." Egyptian Informatics Journal 23.1 (2022): 33-45. [14] Aher, Sunita B., and L. M. R. J. Lobo. "Combination of machine learning algorithms forrecommendationofcourses in E-Learning System based on historical data." Knowledge- Based Systems 51 (2013): 1-14. [15] Aher, Sunita B., and L. M. R. J. Lobo. "Combination of machine learning algorithms forrecommendationofcourses in ELearning System based on historical data." Knowledge Based Systems 51 (2013): 1-14. [16] Tan, Huiyi, Junfei Guo, and Yong Li. "E-learning recommendation system." 2008 International conferenceon computer science and software engineering. Vol. 5. IEEE, 2008. [17] Guruge, Deepani B., Rajan Kadel, and Sharly J. Halder. "The state of the art in methodologies of course recommender systems—a review of recent research." Data 6.2 (2021): 18. [18] Lee, Youngseok, and Jungwon Cho. "An intelligent course recommendation system." SmartCR 1.1 (2011): 69 84. [19] Lin, Jinjiao, et al. "Intelligent recommendation system for course selection in smart education." Procedia Computer Science 129 (2018): 449-453. [20] Hameed, Mohd Abdul, Omar Al Jadaan, and Sirandas Ramachandram. "Collaborative filtering based recommendation system:Asurvey." International Journal on Computer Science and Engineering 4.5 (2012): 859. [21] Jin, Chenxia, et al. "Hybrid recommender system with core users selection." (2022). [22] Morales Murillo, Victor Giovanni, et al. "A Systematic Literature Review on the Hybrid Approaches for Recommender Systems." Computación y Sistemas 26.1 (2022). [23] Bhumichitr, Kiratijuta,etal."Recommender Systemsfor university elective course recommendation." 2017 14th international joint conference on computer science and software engineering (JCSSE). IEEE, 2017. [24] Sánchez-Sánchez, Christian, and Carlos R. Jaimez González. "Course Recommendation System for a Flexible Curriculum Based on Attribute Selection and Regression." Proceedings of SAI Intelligent SystemsConference. Springer, Cham, 2021. [25] Choi, Sang-Min, and Yo-Sub Han. "A content recommendation system based on category correlations." 2010 Fifth International Multi-conference on Computing in the Global Information Technology. IEEE, 2010.
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