Improving Cooperation in Virtual Learning Environments Using Multi ...

Session F4C

Improving Cooperation in Virtual Learning Environments Using Multi-Agent Systems and AIML Márcio Alencar, José Magalhães Netto [email protected], [email protected] Abstract - The large number of messages posted on the forum, a key element in Distance Education Courses based on Virtual Learning Environment, which does not receive adequate feedback from the tutor in sufficient time is a typical problem faced by students in these environments. The tutors, in turn, feel a lack of tools to monitor activities carried out by the student. This article proposes an approach for solving these problems based on the concept of perception, using the multiagent system paradigm. The system is composed by intelligent agents, which act in a Moodle discussion forum using an AIML knowledge base. Agents solve questions about matters discussed at the forum, and they use perception to recommend the implementation of activities that the student has not done. The results from simulations based on real courses already completed show that there is a decrease in the workload of tutors. Students are reminded the deadline for the tasks automatically. It was necessary to create hundreds of AIML rules to get answers to good level. The partial results indicate that the approach of combining AIML and MAS is promising to improve the feedback from tutors and motivate students to conclude their work on time. Index Terms – Multiagent Agent System, AIML, Distance Learning, Awareness, Forum, Moodle, VLE.

INTRODUCTION Virtual Learning Environments (VLE) are subjects of increasing researches in recent years by making information more accessible to users of distance education courses, collaborating with learning. These environments have several tools (forum, chat, journal, assignment, wiki, etc.) that support the collective creation of knowledge; however studies show that the forum stands out among such tools for enabling the participant delves into topics of a course. Asynchronous communication offers to students the opportunity to develop their own time of study. Then the student has more time to reflect and discuss about additional issues analyzed in the classroom.

Despite the advantages of the forum, there is a problem that frequently arises when a discussion is started, that is the large number of messages posted by students without feedback from tutors. The delay in response on the forum, caused by periods of inactivity and few posts impairs the performance of the course and could discourage or bore the student [1]. The tutor who communicates constantly and intensely with the most participants facilitates the learning process. Another problem verified by students and tutors in distance learning courses is the lack of a tool to assist in monitoring students' activities, allowing them to be informed of their pending and status in the course. The use of perception in Virtual Environments helps in the assimilation of the content taught and increases the collaborative process therefore it allows tutors and students to see what is happening in the environment, encouraging the best of cooperation [2]. When students are not aware about what is being developed by other participants, the resulting work may not be satisfactory. Understanding other community members activities help students in solving their own activities [3]. This paper proposes an approach to solve those problems based on a Multiagent System (MAS) through a discussion forum in Moodle, using AIML (Artificial Intelligence Markup Language) and JADE framework (Java Agent Development Framework). The Virtual Learning Environment chosen for actuation of the MAS was Moodle, for being friendly software and one of the most widely used. In Moodle was created a user account called "Assistant Tutor", which represents the MAS. MAS is represented by Assistant Tutor, which is operated as soon realizes that the Tutor retarded to give feedback or did not respond to discussion forum. The Tutor Assistant is responsible for collecting constantly questions posted in the forum for students, and monitor the implementation of its activities in the course, playing a vital role, as well as, responding to student questions, the same will report which activities are pending, taking questions and warning other students who follow the forum. The activities to clarify doubts and to alert students, performed by the Assistant Tutor, assist other students who participate in the forum [4].

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Session F4C RELATED WORK

Currently, there are several studies applying MAS in Virtual Learning Environment using knowledge base in AIML. T-BOT and Q-BOT are two virtual assistants, who use natural language to serve as a monitor and evaluator of VLEs. Were implemented in the PHP programming language, integrated with Moodle or Caroline and their knowledge bases uses AIML. T-BOT, through a friendly interface, removes technical questions from students while the Q-BOT applies questionnaires to help in the learning process [5]. Martin [6] verified that the UNED (Spanish University for Distance Education), had communication problems between teachers and students, then it has developed a smart manager able to automatically respond questions from students using information stored in VLEs (dotLRN, Moodle , WebCT). X-Learn was designed to assist users in distance courses. This paper uses agents that communicate using the language ACML. MAS does a customization of attendance through the registration of actions of each user. These informations are written in XML knowledge base, which assists in the learning of users [7]. MASCE (Multi-Agent System for Collaborative Elearning), a Ph.D. work [8], uses collaborative learning to help students and instructors. It works with two agents: the student agent, that manages the profile of the student, monitors their actions, does recommendations to the student. The instructor agent provides training material and evaluates the student's participation during the course. ARCHITECTURE Figure 1 shows MAS architecture, which is composed of: Moodle forum, MySQL Database, AIML interpreter, the AIML knowledge base, and intelligent agents. In figure are shown also users: Student and Tutor distance.

with other agents to acquire informations. The architecture works in three ways: 1. The student enters a question in the discussion forum of the VLE, then the Tutor Agent collects the question, sends AIML interpreter and obtain the answer and stores it, then passes the ID (identifier) of the student to the other agents, who check the implementation of the activities status of the student. Then, based on the collected informations, the Tutor Agent creates a contextualized answer and records in Moodle discussion forum. In addition, the Tutor agent informs the Tutor distance, by internal message, which students had never posted on the forum. 2. Distance Tutor asks to the Tutor Agent the status of the activities of one or more students by internal message via Moodle. The Tutor Agent receives the message, send the request to other agents, who check the activities of one or more students and returned to the Tutor Agent. With this information, the Tutor Agent informs the Distance Tutor. The Distance Tutor may ask the Tutor Agent in these ways: Did the student X do forum activity ? How many students did not do the quiz ? Which students did not do the Assignment ? 3. MAS uses the characteristics of autonomy and proactivity. Each agent keeps monitoring the environment, checking the status of students' activities and informs the Tutor Agent by internal message and passes the information to the Tutor distance. MAS performance and behavior is associated with the timing of the activities available in the environment. To collect the status of the activities of each student, agents realize what is happening in the environment and make recommendations; they need to query multiple tables of the Moodle Database. We can better understand the activity performed by each agent viewing the table 1. TABLE 1 FUNCTION AGENTS

Agent

Tutor Agent

FIGURE 1 ARCHITECTURE

The MAS is composed of six intelligent agents. Tutor agent is the principal agent of the system; its function is to interact

Forum Agent Journal Agent Quiz Agent Wiki Agent Assignment Agent

Function  Collection of the forum, answering questions from students that were not answered by the tutor distance;  Send / Receive doubt AIML Interpreter; Ask the Agents: Forum, Journal, Quiz, Wiki and Assignment, status of activities undertaken by the student;  Make a checklist to verify what students have never posted in the forum;  Generates a contextualized response to the data received;  Send message to distance tutor informing the pendency of the students. Verifies that students viewed / posted in the forum Verifies that students viewed / posted in the journal Verifies that students viewed / posted in the quiz Verifies that students viewed / posted in the wiki Verify if students viewed the task, downloaded, is on schedule delivery and/or sent tasks

Moodle forum stores its posts in the MySQL database.

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Session F4C AIML interpreter is responsible for reading a question, finding the answer in its knowledge base and returning a response. Based on MAS architecture, it was performed the model of the system using the MaSE (Multiagent Systems Engineering) methodology proposed by DeLoach and Wood [9]. MaSE is composed by several models, which allows to the developer to build step by step a MAS, detailing the behavior of agents. MaSE methodology consists of two phases: analysis and design. In the analysis phase there are several stages, including the capture of the objectives of the system, which are divided into sub-objectives to achieve the main objective. We constructed the goal hierarchy diagram to represent this stage (Figure 2).

pupils. We created a knowledge base AIML file (AIML) to resolve the questions posted in the forum, containing the main questions concerning the issue addressed in the discussion forum "Taking your questions - hardware". This knowledge base was established through the didactic material of the course. The AIML is structured similar to XML, through its rules facilitates the composition of a knowledge base, supporting research that uses Learning Virtual Environments [11]. To process the AIML files, it was used the program-D software, an AIML interpreter, implemented in JAVA, which allows the integration of JADE with the AIML knowledge base [12]. It is responsible for loading the rules contained in the AIML files. EXPERIMENTS

The experiments with MAS were applied to students of the technical course in Public Services (http://ead.cetam.am.gov.br/salasp), Professional Education Distance School (CETAM EaD), on the "Applied Computer Science” subject. This subject has used five activities (Journal, Forum, Quiz, Assignment and Wiki) as shown in Figure 3 and involved ten participants.

FIGURE 2 GOAL HIERARCHY DIAGRAM

This diagram was created with the aid of agentTool (aT) software, a tool implemented in Java, aimed at the development of MAS. PROTOTYPE For creating and testing the prototype system were main used software: JAVA language programming 1.6, language AIML, JADE 3.7 and Moodle 1.9.2. In the implementation of intelligent agents it is used JADE software. This framework is intended to develop multi-agent systems compatible with the specification of the Foundation for Intelligent Physical Agents (FIPA). According to [10], JADE enables the creation of agents with different rules of behavior that exchange messages in the specified format ACL. All intelligent agents used in the research exchange information and access the MySQL database to see what is happening in the virtual learning environment and provide to the Assistant Tutor the status of activities undertaken by

FIGURE 3 COURSE "APPLIED COMPUTER SCIENCE”

Before starting the tests, the rules were loaded based on AIML, using program-D software and all agents were started. Students who participated in the simulation were instructed to post a question in the discussion forum with the theme "Taking your questions – hardware”, which is one of the activities of the course "Applied Computer Science”. In the simulation was agreed that the students' responses should not have interference from the distance tutor, in other words, they should not receive feedback from him.

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Session F4C Students who posted on the forum received a final contextualized answer, registered in the discussion forum as a response to the Assistant Tutor, represented MAS, as Figure 4. Students who have not posted, distance Tutor received an internal Moodle informing the pendency of the students. The contextualized answer created by Assistant Tutor is the result of two concatenated sentences: 1st. sentence – the explication of the student question; 2nd sentence contains one or more recommendations of pending activities. If the student does not have pending activities, the second sentence will consist in a enhancement and incentives content, related to student effort, this content is generated randomly, in other words, all students who are up to date will receive different sentences.

ACL language is a basic communication mechanism between FIPA agents. The message ACL JADE has several attributes, which are: Performative, Receiver, Content, Conversation ID.

.

FIGURE 5 SNIFFER TOOL OF JADE

All agents send a message to the DF agent to register (request), receiving a confirmation (Inform). After this procedure, the Tutor Agent support staff responsible for the activities, receiving the answers. The messages exchanged by each agent receive a different color in the Sniffer software, facilitating the monitoring of the developer who needs this information to check whether the system is working properly. FIGURE 4 AGENTS MONITORING DISCUSSION FORUM

To confirm MAS operation, we use a sniffer tool from JADE that graphically shows the messaging between agents, therefore during the experiment execution, Sniffer is activated and it is noticed the exchange message among the six agents involved (forum, journal, quiz, wiki, assignment and tutor), as shown in Figure 5. In Figure 5, there are seven red boxes, six are the MAS agents and one is DF agent (Directory Facilitator), responsible for managing JADE services and forwards the messages to their agents [9]. The exchange of messages between the agents uses ACL (Agent Communication Language) language and with JADE Sniffer tool running, it is held the record of these ACL messages while they are being transmitted.

PARCIAL RESULTS

After the experiment execution, it was done a survey of the actions performed by agents. Table 2 presents the participation of students in activities, each column represents an activity. AIML column will be used to show if student's question was not solved. Letter "N" indicates that the student has not participated in the activity or failed to obtain the response of the doubt and the letter Y” indicates that he participated or received any reply. The contextualized answer (CA) posted by Agent Tutor Forum is comprised of two sentences concatenated: 1st sentence (S1), clarification of doubts and 2nd. sentence recommendations (S2) or praise (S3). If MAS doesn't have the answer because it is not contemplated in AIML rules, then it will be sent a message to the Distance Tutor (MT) informing that the Assistant Tutor could not solve the doubt.

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Session F4C

Student

Journal

Forum

Quiz

Assignment

Wiki

AIML

Contextualiz ed Answer

TABLE 2 PARTICIPATION OF STUDENTS

1 2 3 4 5 6 7 8 9 10

Y N N Y Y Y Y Y Y Y

Y Y N N Y Y Y N Y N

Y N N Y Y N Y Y Y Y

Y Y N Y Y N Y Y N Y

Y N N Y Y N Y N N Y

Y Y N N N Y Y N N N

S1+S3 S1+S2 MT MT MT S1+S2 S1+S3 MT MT MT

Analyzing the tabulated data, we found that six students have posted on the forum but four didn’t post. Among the questions posted in the forum, four received a response generated by MAS through technology AIML, and two didn’t achieve, in other words, they were not among the rules stored in the AIML knowledge base. As the basis of tests had few rules, it was found that these results were valid and correspond to reality, because the students use to ask questions in a subjective way. For unanswered questions, the MAS has sent a message to distance tutor, telling his difficulty in responding. All students who posted on the forum received a sentence of recommendation or a compliment. For students who have not posted in the forum, MAS has sent a message to distance tutor depicting the pending activities. In the experiment, it was observed that the response time was reduced, this occurred because the MAS is always active and monitoring the environment. It was observed that when a student posts a question, MAS gives a time for other students to participate in the discussion, if it does not happen, it tries to answer. It was found that with agents, with their characteristics of autonomy and proactivity, it was possible to verify the deadline for completion of activities and make recommendations to students, improving the performance of the course. It was verified that the significant number of students who participated in the simulation was not sufficient to evaluate all items (both positive and negative) of the prototype. The goal is to improve the knowledge base and apply this MAS in the future with distance courses with more students. CONCLUSION

As it was observed, it is considered that in the approach taken, students will always have a feedback, because the Assistant Tutor, besides heal the main doubt, makes recommendations for pending activities or notifies the distance tutor. As a limitation, we emphasize that the rules

contained in AIML files cannot answer all the questions, because there are specific rules that need to be created and/or updated constantly to cover a wider range of situations and environmental changes. In distance education courses we found that there is a repetition of basic questions from students. These basic questions are already encoded in the AIML files. Then, a significant portion of these files does not need to be updated routinely, reducing the workload of updating these files. However there are rules that need to be modified and new rules that need to be inserted in the AIML files. The chosen VLE to operate MAS was Moodle and to achieve the objectives of the research it was necessary to study its structure and philosophy of work and understand the layout of tables, which is very broad and complex (more than 200 tables). Moodle is structured on the concept of course. The implemented MAS allows to be used by any Moodle course, because it was designed to be independent of the course. The MAS implemented can interact with other VLE, separate from Moodle. In this case we need to understand the work of this new VLE and we should adapt the agents to the new structure. This basically consists of changing the behavior of each agent given the new architecture ensuring the independence of the MAS in relation to the system. ACKNOWLEDGEMENT We would like to thank the National Council for Scientific and Technological Development (CNPq) of Brazil for the partial financial support provided to project number 575894/2008-3, from which this paper is part. REFERENCES [1] Gerosa, M. A.; Filippo, Denise; Pimentel, Mariano; Fuks, Hugo; Lucena, Carlos J. P.; “Is the Unfolding of The Group Discussion OffPattern? Improving Coordination Support in Educational Forums Using Mobile Devices”.Computers & Education, Science Direct, Elsevier, doi:10.1016/j.compedu.2009.09.004, 2009. [2] Fuks, H., Gerosa, M.A. & Lucena, C.J.P. (2002) “The Development and Application of Distance Learning on the Internet”. The Journal of Open and Distance Learning, Vol. 17, N. 1, ISSN 0268-0513, February 2002. [3] Kennedy, D.M. “Challenges in Evaluating Hong Kong Students Perceptions of Moodle”, in Conference Proceedings of Australasian Society for Computers in Learning in Tertiary Education (ascilite) December 2005, Brisbane, pp 327 – 336. [4] Alencar, M. A. S.; Netto, J. F. M. “Uma Estratégia Híbrida Combinando Sistemas Multiagente e AIML para Apoiar Fóruns de Discussão de Ambientes Virtuais de Aprendizagem.” Anais do XXI Simpósio Brasileiro de Informática na Educação - SBIE 2010, João Pessoa, PB. (in Portuguese) [5] Mikic, F. A. ; Burguillo, J. C.; Rodríguez, D. A.; Rodríguez, E.; Llamas, M. (2008) “T-BOT and Q-BOT: A Couple of AIML-based Bots for Tutoring Courses and Evaluating Students”. 38th ASEE/IEEE Frontiers in Education Conference - FIE 2008, 2008.

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Session F4C [6] Martín, S.; Sancristobal, E.; Gil, R.; Díaz, G.; Castro, M.; Peire, J. (2007) “Development of an Intelligent Answering Machine based on LMS Knowledge”. International Conference on Engineering Education – ICEE 2007, 2007. [7] P.D. Meo, A. Garro, G. Terracina, and D. Ursino, "X-Learn: An XML-Based, Multi-agent System for Supporting "User-Device" Adaptive E-learning", in Proc. CoopIS/DOA/ODBASE, 2003, pp.739756. [8] Hani Mahdi, Sally S. Attia, "MASCE: A Multi-Agent System for Collaborative E-Learning," aiccsa, pp.925-926, 2008 IEEE/ACS International Conference on Computer Systems and Applications, 2008. [9] Deloach, S. A.; Wood, M. “Developing Multiagent Systems with agentTool”. In: Proceedings of Lecture Notes in Artificial Intelligence. Springer – Verlag. Berlin, 2001. [10] Silva, J. M. C. ; Silveira, R. A. ; Vicari, R. M. “Applying a MultiAgent Systems to Promote Intelligence in Learning Objects”. IEEE Multidisciplinary Engineering Education Magazine, v. 3, p. 1-1, 2008. [11] De Pietro, O.; De Rose, M. ; Frontera, G. (2005). “An application for automatic updating of the Artificial Intelligence TutorBot Knowledge Base in an e-learning platform”. In G. Richards (Ed.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2005 (pp. 2893-2899). Chesapeake, VA: AACE. [12] Batista, A. F. M. ; Marietto, M. G. B. ; Barbosa, G. C. O. ; Kobayashi, G.; Franca, R. S. (2009) “Multi-Agent Systems to Build a Computational Middleware: A Chatterbot Case Study”, In: The 4th International Conference for Internet Technology and Secured Transactions, 2009, London. IEEE Proceedings The 4th International Conference for Internet Technology and Secured Transactions, 2009.

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