an agent-based architecture for supporting the work groups creation and the detection of...

8/3/2019 An Agent-Based Architecture for Supporting the Work Groups Creation and the Detection of Out-Of-context Conversation on Problem-Based Learning in Virtu

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An Agent-Based Architecture for Supporting theWorkgroups Creation and the Detection of Out-of-context

Conversation on Problem-Based Learning in VirtualLearning Environments

Laysa Mabel de OliveiraFontes

Rural Federal University of theSemiarid

Postgraduate Program inComputer Science

Software EngineeringLaboratory

[email protected]

Francisco Milton MendesNeto




[email protected]

Alexandre dames AlvesPontes




[email protected]

Gustavo Augusto deLima Campos

State University of CearPostgraduate Program inComputer Science

[email protected]

ABSTRACT

A computer-supported collaborative learning environmentcan enable the students of web-based distance educationcourses to interact with each other and with one or more fa-cilitators to conduct group work. The problem- based learn-ing (PBL) is a learning theory that emphasizes collaborationand teamwork to solve a problem. However, a problem thatoccurs frequently in the implementation of PBL is the out-of-context conversation, which is a situation in which thestudents lose focus and start talking about topics that arenot related to the discussion. In presential learning, theteacher can easily detect this problem and try to avoid itin order to improve the learning process. In distance learn-ing, however, detecting this problem is not a trivial task.That is mostly due to issues related to the students geo-graphic distribution and the lack of information regardingtheir motivation. Another noteworthy aspect is the creationof workgroups. In PBL, the members of a workgroup that isresponsible for solving a problem must have certain comple-mentary knowledge and skills related to the problem, andit might be difficult for the facilitator to assign studentsto workgroups, since the lack of presential contact makesit difficult to perceive important characteristics of the stu-

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dents profiles. Then, this paper presents an agent-basedarchitecture for detecting out-of-context conversation andfor helping in the creation of workgroups on the PBL.

Categories and Subject Descriptors

I.2.11 [Artificial intelligence]: Distributed Artificial In-telligence Intelligent agents Multiagent systems

; K.3.1 [Computers and Education]: Computer Uses

in EducationCollaborative learning

General Terms

Multiagent systems, Collaborative learning

Keywords

Virtual learning environments, out-of-context conversation,workgroups creation, problem-based learning, agents

1. INTRODUCTIONDistance Learning (DL) is a modality of teaching and

learning that has grown and produced good results. Groupactivity is an important component in classroom teaching.Interactions between students over the course of some edu-cational activity are crucial to the learning process, as eachstudent shares with the others his/her knowledge, questionsand impressions of what was discussed in class, thus en-riching the learning process. This form of learning is calledCollaborative Learning [2].

In the traditional approach, the teaching is teacher-centered.In this modality of teaching, classroom activities are devel-oped under the exclusive direction of teacher [15]. In the

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collaborative learning approach, the students work togetherin small groups towards a common goal [2]. Learning is acollective activity and the students are responsible for theirlearning. The discussion of ideas among members of thegroup increases interest and promotes critical reasoning.

[5] shows many different learning theories that have beenextensively used to support collaborative learning such asCognitive Apprenticeship of Collins, Cognitive Flexibility ofSpiro, Distributed Cognition of Salomon and others.

Problem-Based Learning, according to [3], is a methodthrough which students learn while solving a problem thatusually doesnt have a trivial solution and only one correctsolution. The learning is student centric and self directed.The students, organized in small colaborative groups, worktowards identifying what they must learn in order to solvethe problem. The teacher acts as a facilitator in the learningprocess, instead of just transmitting knowledge.

PBL emphasizes team work as the key to the learning pro-cess success; in other words, colaboration is essential [11].However, a problem that occurs frequently in the implemen-tation of PBL is the out-of-context conversation, which is asituation in which the students lose focus and start talk-ing about topics that are not related to the problems do-

main. In presential learning, the teacher can easily detectthis problem and try to avoid it in order to improve thelearning process. In distance learning, however, detectingthis problem is not a trivial task. That is mostly due toissues related to the students geographic distribution andthe lack of information regarding their motivation. Anothernoteworthy aspect is the creation of workgroups. In PBL,the members of a workgroup that is responsible for solv-ing a problem must have certain complementary knowledgeand skills related to the problem, and it might be difficultfor the facilitator to assign students to workgroups, sincethe lack of presential contact makes it difficult to perceiveimportant characteristics of the students profiles. Then,this paper presents an agent-based architecture for detectingout-of-context conversation and for helping in the creation

of workgroups on the PBL.This paper is divided in six sections. Section 2 presents

a discussion on multiagent systems. Section 3 discusses theproblem-based learning theory. Section 4 presents relatedworks. Section 5 describes the agent-based approach pro-posed in this work, as well as the agents that are involvedin the process. The last section presents our final remarksand future works.

2. MULTIAGENT SYSTEMSAccording to [10], agents are autonomous software enti-

ties that perceive their environment through sensors andperform actions on the environment through actuators, pro-cessing information and knowledge. A Multiagent System

(MAS) consists of a set of autonomous agents that collab-orate to solve a problem that is beyond the capacity of asingle agent.

There are several types of agents. They can be of soft-ware or of hardware, stationary or mobile, persistent ornon-persistent, reactive or cognitive (intelligent). One ofthe most important classifications of agents is as reactive orcognitive.

Cognitive agents are more complex because they havean explicit representation of both the environment and theother agents. This agent type has a memory, which enables

it to plan future actions based on situations that took placepreviously [6].

Reactive agents are simple agents that note changes in theenvironment and react without any knowledge of previousactions. Since these agents have no memory, they are un-able to plan future actions. Simple reactive agents select anaction based on its current perception of the environment,ignoring previous perceptions.

Reactive agents with internal state, in order to achieve a

more rational performance, have an internal state with as-pects of the domain that may not be evident in the currentperception. Said state depents on the history of previousperceptions of the environment, and is defined in a set ofpossible curent internal states, = {1,...,1}. This agentstructure assumes that: (1) the agent receives information,though sensors, regarding the environments state, definedin a set of possible states; (2) the agent has a perception sub-system and a decision-making subsystem; and (3) the agentexecutes the selected action on the environment through ac-tuators.

Figure 1 gives the structure of the reflex agent, showinghow the current percept is combined with the old inter-nal state to generate the updated description of the current

state.

Figure 1: A reflex agent with internal state

3. PROBLEM-BASED LEARNINGThe empirical studies in [4] show that students who learn

through this approach have a greater ability to apply theirknowledge in new problems and to use more effective strate-gies for self-learning than students who learn through tradi-tional teaching methods.

The role of the facilitator is to guide students in this pro-cess, identifying possible deficiencies in their knowledge andskills necessary to solve the problem proposed. Thus, in thislearning theory, rather than the facilitator simply transfer-ring the knowledge to the students and then testing themthrough evaluations, he causes the students to apply theiracquired knowledge in new situations. In this approach, stu-dents often face ill-structured problems and are motivatedto discover, through investigation and research, useful solu-tions.

For successfully applying of PBL as pedagogic strategy the

following stages must be accomplished: i) the facilitator pro-poses an ill-structured problem to the students group; ii) thestudents try to generate facts and identify hypotheses aboutthe problem through an initial brainstorming; iii) Then, thestudents formulate and analyze the problem aiming to gen-erate ideas for problem solving; iv) After this, the students,supported by the facilitator, identify knowledge deficienciesfor solving the problem by explanations and justifications;v) In the following, the students look for new knowledge re-lated to the domain, for following try to generate facts aboutthis new knowledge; restarting the PBL cycle.

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4. RELATED WORKSMultiagent Systems (MAS) have been widely used in edu-

cational applications. This technology has been quite promis-ing as an aid in collaborative learning environments, makingthese environments more proactive and autonomous. MAScan be used, for example, to assist in the implementation ofa particular learning theory in a collaborative environment.

In [17], a simulated student architecture is designed todetect and avoid three situations that decrease the bene-fits of learning in collaboration: off-topic conversations, stu-dents with passive behavior and problems related to stu-dents learning.

In [1], a collaborative learning environment is presented,called Cole, which focuses on social interaction among theparticipants from the learning process. This environmentwas built to support project-based learning. The learningprocess in the environment is accomplished with the aid ofportfolios, which are described as planned and organized col-lections of works produced by the student(s), over a certainperiod of time. The portfolio reflects the profile of each stu-dent and of each teacher during the teaching-learning pro-cess. In [8] , another environment built to support project-based learning is presented.

In [7], a model for virtual learning environments is pre-sented that employs intelligent agents to implement Vygot-skys sociocultural theory, focusing on the social aspect ofinteraction. The proposed model has several agents, amongwhich we highlight the following: (i) the social agent, whosemain goals are the construction of models for groups of stu-dents and the identification of groups of students that cancooperate in good conditions; (ii) the tutor agent, whichevaluates the students educational goals and recommendssome type of activity; and (iii) the personal agents for as-sistance to the students, which monitor their activities andthen inform other agents of the results of the monitoring.

In [13, 14], a system called I-Minds is presented that pro-vides a computer-supported collaborative learning (CSCL)infrastructure and environment for learners in synchronous

learning and classroom management applications for instruc-tors, for large classroom or distance education situations. Ithas a host of intelligent agents for each classroom: a teacheragent ranks and categorizes real-time questions from thestudents and collects statistics on student participation, anumber of group agents that each maintains a collaborativegroup and facilitate student discussions, and a student agentfor each student that profiles a student and finds compatiblestudents to form the students buddy group.

As a distinctive feature of our work, we highlight thefact that our approach uses an animated interface agentwith socio-affective features, i.e., when the problem detectoragent identifies unfocused behavior, the animated interfaceagent tries to solve or minimize the problem by motivat-

ing the students to participate in activities and discussions.For this purpose, it uses facial expressions, gestures, soundsand text messages. Moreover, unlike the others works dis-cussed in this section, the proposed approach is based onPBL, which is a learning theory that has been proven to beeffective [16, 15, 12].

5. AGENT-BASED APPROACH TO WORK-

GROUPS CREATION AND OUT-OF-

CONTEXT CONVERSATION DETECTION

ON PBLIntelligent agents can perform many tasks in computer-

supported collaborative learning, such as monitoring stu-dents participation in discussions, facilitating the selectionof topics for discussion, and assessing student performancein relation to the use of communication and cooperationtools available in the environment, among others. The useof agents to assist with these tasks is becoming increasinglyimportant, mainly due to the increasing number of studentswho interact in learning support systems, which makes itvery difficult to the facilitators to manage these activities atdistance.

The approach proposed in this paper is shown in Figure2.

Figure 2: Agent-based approach to group creation

and out-of-context conversation detection on PBL

According to the approach presented in Figure 2, fourtypes of agents are proposed: a Problem Detector Agent(PDAg), a Workgroup Creator Agent (WCAg), a StudentAgent (SAg), and an Animated Interface Agent (AIAg).The Problem Detector Agent is responsible for detectingout-of-context conversations based on the environments col-laborative tools and an ontology. After detecting the stu-dents focus has been lost, he notifies the Student Agent,which searches its history base to verify whether this stu-dent has already been stimulated.

If he has not been previously stimulated, the SAg willtrigger the animated interface agent that will search for thefirst stimulus in the base of stimuli previously registered.Then the animated interface agent will try to motivate thestudent with this stimulus. In the following, the interfaceagent will record the type of stimulus used in its historicalbase and notify the facilitator about the students situation.

However, if the SAg detecting in its historical base thatthe student was previously stimulated, it will check whetherit has already passed the deadline (previously registered bythe facilitator) given to the student improve their collabo-ration on the learning environment (e.g. 15 days). If thedeadline has not passed, it does nothing. But if the dead-line has passed, he will notify the animated interface agent,which will search for the next stimulus in the base of stim-uli by motivating the student in a different way. Then itrecords this information in its historical base and it notifiesthe facilitator about the students situation.

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The agents will repeat this process while there is stimulusregistered in the base of stimuli. The animated interfaceagent is responsible for motivating students to focus moreof the discussions and to use the tools available in the virtuallearning environment.

The WCAg is responsible for the automatic assignment ofstudents to workgroups in order to solve a problem. It willcreate the workgroups based on the students profiles andon the characteristics of the problem that must be solved by

the group.The following subsections describe the processes of detect-

ing out-of-context conversations and creating workgroups,respectively.

5.1 Out-of-context Conversations DetectionIn this subsection, the agents involved in the task of de-

tecting out-of-context conversations are described.

5.1.1 Problem Detection Agent

The PDAg is also responsible by detect students with un-focused behavior during the PBLs interactions. The Algo-rithm 1 presents the steps accomplished for reaching thisgoal.

As can be seen in Algorithm 1, the PDAg monitors con-stantly the tools used by the students for cooperating andcommunicating during the problem solving. Then it com-pares the words used by the students in their interactionswith a set of words previously instantiated in an ontology ofwords related to context of the problem being solved.

Algorithm 1 Action: Detection of Unfocused Behavior

Considering : > student : student solving some problem in P BL > interaction tool : some synchronous or asynchronousmechanism for communication or collaboration > message : some message sent by a student throughsome interaction tool > word : a word in a message > ContextRelatedWordsOntology : ontology in which

related words to the problem context are instantiated > RW : amount of related words > OutofC ontextW ordsOntology : ontology in whichnot related words to the problem context are instantiated > NRW : amount of not related words > : balance f actor used by f acilitator > LD : level of focus (stores result of the algorithm)

1: for all student do2: for all message interaction tool do3: for all word message do4: if word ContextRelatedWordsOntology then5: RW = RW + 16: else7: if word OutofContextW ordsOntology then8: NRW = NRW + 19: end if

10: end if

11: end for12: end for13: LD = 2 NRW/RW14: return LD15: end for

In the following, the PDAg calculates the percentage ofwords used by each student on the tools available in thelearning environment out of the context of the problem.This is useful for identifying the level of defocus (uncon-cern) of the student on the subjects related to problem in

discussion. This level can be obtained by the expressionLD = 2 NRW/RW , where LD = Level of Defocus;NRW = amount of Not Related Words; RW = amount ofRelated Words; and is a factor that the facilitator canmanage for increase or decrease the impact of not relatedwords.

5.1.2 Student Agent

The Student Agent (SAg) is a reflex agent with internalstate. Its internal state reflects the historical base of stimuliused to motivate the students with passive and/or unfocusedbehavior detected by the PDAg. Over the time, the SAg per-ceives the effectiveness of application of each stimulus. TheSAg see and next functions integration serves to update thehistorical base of stimuli with data about the use of thesestimuli to motivate the students. In the proposed agent-based approach, for each stimulus used by the animated in-terface agent in the learning environment, the internal stateof the SAg will change to reflect that the stimulus was al-ready used and its use date. Based in this date, the SAgwill know if the deadline previously defined by the facilita-tor for the student improve his collaboration has finished ornot. This is important because the student only must be

motivated again if this deadline has been ended.

5.1.3 Animated Interface Agent

The Animated Interface Agent was implemented for com-munication with students. This agent aims to motivatestudents to participate more and correctly in discussionsthrough the collaborative tools. It was shown in [9] thatinterface characters can have a positive impact on studentsinteractions with learning environments. This agent gen-erates more reliance because it is socio-affective, i.e., it isable to express emotions through animations, gestures andknown representations, stressing its social features.

The animated agent has a stimulus base that allows it toprovide the student with several distinct stimulus. The Stu-dent Agent will interact with the animated interface agentby asking it to either provide him with a stimulus or wait,depending on the expiring date on the Student Agents stim-ulus base. The Problem Detector Agent interacts with theStudent Agent by giving him the list of students that havebeen unfocused. If any of those students are online, theStudent Agent may ask the Animated Interface Agent tointervene. It will then try to stimulate the student with astimulus from its base.

5.2 Automatized Group Creation on PBLAs seen in the previous sections, the interaction in PBL

plays a very important role on the learning process. In thiscontext, the workgroups creation process in the learning en-vironment is very important to the overall performance of

the process. In presential learning, the students are veryclose to each other and, usually, the teacher knows each oneof them, and the students know each other. In distancelearning, the students are geographically distributed, there-fore even the facilitator doesnt know all of the students,and the students dont know each other either. The facili-tator must create the workgroups in PBL, but in distancelearning he doesnt have enough information regarding thestudents in order to perform this task on his own. In theapproach proposed in this paper, an agent is used to helpthe facilitator in this task. The workgroups creation process

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is ilustrated in Figure 3.

Figure 3: Automatized Group Creation Process

The workgroups creation process happens as follows: thestudents, through a web-based interface, fill their profiles inat the beginning of the process. This process feeds a profilebase that will be used in the workgroups creation process.The students profile is composed of skills, acquirements anddeficiencies, where each of these has a level, which can be

either low, medium or high. A student can have severalskills, deficiencies and acquirements. The web interface inwhich students fill out their profiles is illustrated in Figure4.

Figure 4: Web interface in which students fill out

their profiles

As can be seen in Figure 4, students may add or changeskills, deficiencies and acquirements in your profile. Thefacilitator defines the profiles of the workgroups for eachproblem that must be solved through a web-based interface,similar to the one used by the student. The groups profileis composed of skills, acquirements and deficiencies, whereeach of these has a level, which can be either low, mediumor high, and a fuzzy value that varies from 0.1 to 1. Afterthe facilitator to create the groups profiles, there will be agroups profile base that can be accessed by the WCAg. The

web interface in which the facilitator fills the profiles of thegroups is illustrated in Figure 5.

As can be seen in Figure 5, the facilitator has the optionto create a new profile for the group or edit an existinggroup profile. In the latter option, a new interface, similar toFigure 4, is presented to the facilitator. As shown in Figure5, the facilitator has the option to edit an existing groupprofile, including new skills, acquirements and deficiencies.

The WCAg is responsible for the automatic creation ofgroups. It was implemented using two programming lan-guages: Java and Prolog. The Java section of the agent is

Figure 5: Web interface in which the facilitator fillsthe profiles of the groups

responsible for the creation of candidates that are apt toparticipate in a workgroup. This process is done by ana-lyzing the students profiles and the groups profiles. Afterthis analysis, it generates a file that will be the input for theProlog section.

The generation of candidates is p erformed as follows: (i)the WCAg analyzes the profile of the group and checkswhether there is a candidate that possesses some requiredskill; (ii) If there is a candidate that has at least one re-quired skill, this candidate is enclosed in a list of suitablecandidates to compose the group. This process is done sim-ilarly for acquirements and deficiencies. Thus, candidatesthat have at least one skill, acquirement or deficiency areincluded in the list of suitable candidates to join the group;(iii) Next, the WCAg generates an input for the section inProlog. This input is called perception, and it is actually atext file that contains parameters that will be read by theagent session in Prolog and thus constitutes the interfacebetween the two sections. An example of the file structurecan be seen in Table 1.

1st Parameter 1.2nd Parameter 2.3rd Parameter 11.4th Parameter [a,m,b,a,a].

[0.8,0.4,0.2,0.7,1.0].

5th Parameter [1,John,[m,m,b,m,b]].

Table 1: A file structure example

The first parameter is the number of candidates. Thesecond, a measure of similarity. The third is the amount ofthe universe of discourse. The fourth is the desired situation(sources and importance values) that represents the profileof the group and, finally, the fifth parameter is the currentsituation or the profile(s) of student(s) able to compose thegroup.

The Prolog section of the agent is responsible for the as-signment of students to groups. At the end of this process, afile containing the workgroups is generated. The facilitatormust analyze the is result and decide whether he will acceptthe agents suggestion.

6. FINAL REMARKSPBL is a learning theory that has been successfully ap-

plied to virtual learning environments. This theory empha-sizes teamwork and collaboration in order to solve a problem.However, it can be harmed by out-of-context conversations,which can happen frequently. In presential learning, thefacilitator can easily avoid this problem, since he is phys-ically close to the students. In distance learning, though,

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this is not a trivial task, mostly due the problems regardingthe geographic distribution of the students. In an attemptto solve this problem, this paper presented an agent-basedapproach for detecting out-of-context conversation and forcreating workgroups in a more effective way. The proposedagent-based approach can be used in any virtual learningenvironment, once it was developed as a software layer thatdoesnt rely on any application.

As future work, we intend to realize a case study in a

virtual class of the computer science course to validate theproposed architecture.

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