This document discusses using a chatterbot to access learning objects on mobile devices through a data mining search engine. It introduces using metadata to create courses from learning objects typically stored in repositories. It then discusses using a chatterbot interface to search repositories based on user text input to generate queries. The problem is accessing shared learning resources on mobile devices, which increased available online courses. The proposed solution is an architecture allowing learning object use across devices through a chatterbot for conversation and information to drive searches. It aims to interconnect devices and optimize multiple searches. The prototype was developed in Java for portability and uses a chatterbot for initial user interaction to generate search patterns and display related information. The goal is an interface for constant student-device communication through
1. Use of Chatterbot for Accessing Learning
Objects on Mobile Devices With a Data
Mining Search Engine
2. Introduction
The use of learning objects as resources are
focused in the use of a virtual learning
environment. The use of metadata with each
learning object allows the possibility of use of
all the information to create courses. The
Learning Objects are typically stored in
repositories that provides a list of services like
view, download and updates.
3. Introduction
Current work presents an important collection
of information that may be included in
content that will allow the chatterbot to
access. Next section present sets out the
possibility of a user that allows to access to a
range of learning objects obtained via
chatterbot, based on a basic pattern of search
using the text introduced to generate a search
in the repositories.
4. Problem Outline
The use of mobile devices to communicate
and search information in the web generates
the need of the exploitation of shared
resources used by teachers in the UAA. That’s
resulted on the increased of resources
available in other media like learning objects ,
this creation derived from the needs
generated a considerable increase in the
number of online courses.
5. Problem Outline
• The creation of an architecture that enables the use of learning objects in
a multiplatform device.
• The use of a chatterbot to create a conversation and have information to
the searches.
• Create a research model to create an interconnection between multiple
devices mobile and desktop computers.
• The performance of the application must be optimums in multiple
searches.
• The generation of a multiple interface that works into mobile devices and
desktop computers.
• The use of a specification to search information based on a metadata.
9. Case of Study
The prototype application was developed in
Java to make easy the portability of the
application and create the chatterbot that
allows interaction between the user and the
application, generating a first emulated chat
where you can get basic questions about
topics that allow the creation of a pattern in
order to search and display of related
information.
13. Conclusions
• This work shows the beginning of an application that
combines the use of learning objects primarily operated by a
chatterbot using the information introduced by the user and
searching some patterns of knowledge and with this
information we can generate queries in the learning object
repository implementing a cluster algorithm into the data
base search to have a best time to get the list of learning
objects.
• The main goal of this article is to show an architecture that
allows to exploit the benefits of an interface that allows
constant communication between a student and the mobile
device through a simulated chat get answers necessary to
show the best possible results on screen.
Hello my name is Alan Calvillo and I’m going to present the paper “use of chatterbot for accessing learning objects on mobile devices with a data mining search engine”
The use of digital information to store goes to the creation of multiple learning objects of different topics . We were working with this kind of information because with some learning objects you can create multiple on line curses working with this kind of logistic goes to the easy creation of full courses in platforms like moodle. All these learning objects usually are stored on repositories and have services like view, download and updates. All the information generated with this process are stored firstly in a local repository and we can have access to federal repositories.
The searches in multiple repositories usually creates a lot of learning objects and the user doesn’t know what learning objects are important . The current work shows how facilitate the access to all information contained in multiple repositories using a tool named Chatterbot with this tool the user had a conversation and using some questions create patterns of search that we use to make searches in repositories . We are proposing this tool because there’s a lot of information and usually there are learning objects with information that probably the user needs.
We are working with mobile devices and that generates the main necessity to use this kind of devices to communicate and search information using a web interface and our principal users are teachers of different areas. The use of digital resources resulted in the increase of learning objects available for users .
The problem outline generates the next points : We need an architecture that enables the use of learning objects in multiple devices. We need the use of a chatterboot to simulate a conversation and obtain information to the searches . Create a research model to interconnections between multiple devices including mobile and desktop Good performance in time of response while the user makes searches Use the metadata contained into each learning object
This is the main architecture in the first step we have the user interacting with the chatterbot and we can access using mobile devices or a desktop computer, while we are interacting the chatterbot send multiple requests to our local search in this search we just access to the local repository and in the other side we have a federated search a place where are multiple repositories of different countries . The process of data mining implements an internal clustering to select the correct cluster and send a request to a process with a query who gives the information with learning objects . Finally we have a list of learning objects with specific services like search, visualization, interaction and evaluation.
The internal process is using a window with a chat created and this process has a listener with the chatterboot , it works like an agent listening each key pressed by the user and then send questions to get information and this information are searched into a database with related themes this process creates two process synchronous with one process to generate a pattern consulting the pattern database and the other searching learning objects with keywords into the database.
This is the main architecture of federated search with multiple devices using a level with services working as agents and in this level we have a gui adapter that detects the dispositive, the proces of local search , federated search and list objects with his services. All the searches are using mexican repositories like UV, Upa, UAA , Latinamerican Repositories like ESPOL, Alfa and Aproa. And European Repositories like Ariadne and MERLOT.
The prototype was developed on java because it’s more easily to port into different devices and allows the interaction between the user and the application generating an emulated chat who gets information to choose the correct pattern in order to search and display information.
This is the first approach with a java interface into one emulator that was created by netbeans. The chat starts making some basic questions in this case it’s about a topic Data Structure, and there’s questions like “what language ?” , “ is there oriented object programming?” “ are you going to use Nodes ?” , with this questions the chatterbot gets information to relate with the correct pattern and launch all the learning objects available.
The process are finished when the user receive the request with all the learning objects available in this case shows a list of objects with topics of interest to the user once he select the learning object he can access to the list of services available to the final user.
The process of get information are based on xml because makes easier the multiple queries on different repositories with different formats, one way to solve this problem is using an standard like xml with specific tags in the xml file , generating the structure of searches and visualization .
This work shows an approach to an application that combines the use of learning objects accesed by a chatterbot using information introduced by the user and searching some patterns of knowledge and with this information we can generate queries in the learning object repository implementing a cluster algorithm into the database search to have a good performance in the multiple searches. The purpose of this article is to show an architecture that allows to exploit the benefits of an interface that allows constant communication between a student or teacher and the mobile devices through a simulated chat and shows the possible results on screen.