Intelligent web-based tools to support e-learning

transcript

Eva Millán

(IA)2 GroupUniversity of

Málaga

Eva Millán

A little bit about me...I am associate professor at Malaga University, where I lecture on

Approximate Reasoning (fuzzy logic, Bayesian networks) Operations Research

Most of my research work has been about “deep thinking” in student modeling with Bayesian networks (the subject of my PhD thesis)In parallel, I have developed several tools for teaching Linear ProgrammingI have also worked in other projects developed in my research team, like MEDEA and SIETTELately, I have also been working in the evaluation of LeActiveMath.Since 2003, I am also vice-dean (of innovation in education) of the Computer Science School, in which I lead the Bologna process

Eva Millán

What am I working (in research) at the moment…Writing a paper with Tomek Loboda (preliminary title “Bayesian student modeling without tears”)Modeling Felder’s learning styles with Bayesian Networks and using learning algorithms (with Gladys Castillo and Cristina Carmona, for Cristina’s PhD)Using Bayesian Networks to model collaboration in virtual communities (with Beatriz Barros and Javier Burón, for Javier’s master thesis project)Developing ITS for education in the moral (ethical) values, dedicated to children in Venezuela prisons (with Arlenys Varela from Venezuela, for her master thesis project)Keep collaboration with other members in my research team in projects like MEDEA and SIETTE

Eva Millán

So what I am going to present in this talk?

Something “old” TAPLI, a web-based tutor for linear programming (2003)Something “new” A tool based on Bayesian Networks to analyze the collaboration from the logs of a virtual community (submitted but not published yet)

TAPLI: a web-based tool for Linear Programming

Eduardo Guzman (connection with SIETTE)Emilio Garcia (implementation)Eva Millán (domain expert, design, development of contents)

Something “old”

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IntroductionTAPLI is an adaptive web-based learning environment for Linear Programming, that consists in the integration of three tools in the same environment:

a) An adaptive hypermedia component, that is responsible of presenting the learning contents;

b) An adaptive testing component, that allows self-evaluation using tests

c) An adaptive drill‑and‑practice component, which: dynamically generates exercises coaches students, offering guidance, support, help

and feedback.

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MotivationTapli is a web-based learning environment about Linear ProgrammingTAPLI has been designed and implemented to be used by students of Operations Research in the Computer Science School of the University of Málaga as an extra help for learning.TAPLI was based in previous work of our group, namely EPLAR and ILESA, which were former versions and also SIETTE, and adaptive web-based tool for testing

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The domainWhat is a Linear Programming Problem?A linear programming problem is a problem of the type:

Optimize c1x1+...+cnxn

Subject to a11x1+…+a1nxn b1

am1x1+…+amnxn bm

It has all sorts of applications in any situation in which resources are scarce

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The domainTo solve a linear programming problem, there is a systematic procedure called the Simplex Algorithm (Dantzig, 1940) The simplex algorithm has been named among the 10 more important algorithms developed in the 20th century.In a finite number of steps, it always conducts to the solution. An example:

Maximize 3x + 2ySubject to 2x + y 3

-5x + 2y 6

Maximize 3x + 2ySubject to 2x + y + s1 = 3 -5x + 2y + s2 = 6

First step: Introduce slack variables to convert the inequalities into equalities

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To our purposes, the important thing is that….

The domain is strongly based in problem solvingThe steps are always performed in the same orderTypes of errors are easily identified Problems can be generated at the right level of difficultyWhich, in our context, allows:Coached problem solvingDynamic generation of problems

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Components in TAPLITAPLI is in fact a set of three tools, running in the same environment: An adaptive hypermedia component,

responsible of presenting content to students A testing tool, that allows the evaluation of

students A drill-and-practice environment, in which

students are posed a problem adapted to their knowledge level and they can solve it while being coached by the system

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The tools in TAPLI: Adaptive hypermedia

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Curriculum

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Learning contents

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Recommendations

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Student model

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Student model

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Student model

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Student model

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Pages visited by student

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Pages visited by student

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Adaptable features: Content presentation is adapted to student’s

goals and level, by means of stretch text and link hiding (tests)

Adaptive features: The list of visited pages is used to suggest the

next piece of content, Student’s knowledge level is used to suggest

the next activity (take a test, solve an exercise, read some content),

This information is combined in a recommendation to the student.

But TAPLI only suggests (free navigation is supported)

Adaptation in the hypermedia component

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The testing component in TAPLIWhile learning contents, students can test their knowledge using tests or exercisesBoth activities are supported by the SIETTE systemSIETTE is a web-based environment for adaptive testing, that can be used by Instructors to develop web-based tests Students to take such tests

Though SIETTE supports adaptive testing (e.g. different test lengths for different users) based on IRT theory, tests in TAPLI are not adaptive (due to the lack of a database of properly callibrated items for linear programming).

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A few words about SIETTE

For teachers to define tests

For students to take tests

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Some words about SIETTEIt is the result of TEN years of intensive work in adaptive testing (by Ricardo Conejo and Eduardo Guzman)It has been used in around twenty different real courses to evaluate more of 2000 real students, in different locations, in all kinds of domains (from Java programming, to Artificial Intelligence, Botany, etc.)It has solid theoretical foundations, grounded in Probability theory, and in particular in Item Response Theory which allows for adaptive testing (reduced test length while increasing accuracy)It can be used as a tool for testing or, even more interesting for ITS developers, as a diagnosis tool to perform the role of the student modeling component in web-based learning environments (just as we did in TAPLI), thus saving lots of work to AWES developers.Much more information in related publications (just type SIETTE in google)

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Linear Programming tests in SIETTE

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Communication between TAPLI and SIETTE:Architecture

SIETTE STUDENT MODEL INTERF

INSTRUCTIONAL PLANNER

Students

STUDENT MODEL

REPOSITORY

ADAPTIVE HYPERMEDIA

COMPONENT

TESTING COMPONENT

DRILL-AND-PRACTICE COMPONENT

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Interactivity with the hypermedia component

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Students

STUDENT MODEL

REPOSITORY

ADAPTIVE HYPERMEDIA

COMPONENT

TESTING COMPONENT

Presents theoretical concepts and examples

The architecture of TAPLI

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Students

STUDENT MODEL

REPOSITORY

ADAPTIVE HYPERMEDIA

COMPONENT

TESTING COMPONENT

Evaluates student’s

knowledge

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Students

STUDENT MODEL

REPOSITORY

ADAPTIVE HYPERMEDIA

COMPONENT

TESTING COMPONENT

Coaches problem solving

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Students

STUDENT MODEL

REPOSITORY

ADAPTIVE HYPERMEDIA

COMPONENT

TESTING COMPONENT

Selects the next component

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Students

STUDENT MODEL

REPOSITORY

ADAPTIVE HYPERMEDIA

COMPONENT

TESTING COMPONENT

Stores the student model

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Communication between TAPLI and SIETTE:Procedure

The communication of this component with SIETTE is based on URL calls with the proper parameters. Initially, the testing component sends to SIETTE:

a) the set of topics to be assessed; c) the number of knowledge levels in which the student

can be classified; b) the current estimation of student’s knowledge about

these topics;d) the URL to which the results will be returned, and e) additional parameters to configure the test (item

selection mechanism, finalization criteria, ...).Once the evaluation has finished, SIETTE invokes the given url and passes the new estimated knowledge level of the student. With these data, TAPLI updates the student model.

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The drill and practice component in TAPLI

This component in TAPLI is able to generate problems at the adequate

level of difficulty. supports coached problem solving

How is this achieved?

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Generation of problems in TAPLI

1517 1816

2 3 4 65 7

23Basic skill.

Ability to solve certaintype of problem

1. Introduce slack variables.2. Fill in the simplex tableau with data.3. Identify solution and objective value in the

tableau.4. Select entering variable for maximization

LP0´s.5. Select leaving variable.6. Perform calculations.7. Identify optimal solutions.8. Level 1. Solve max. problems with unique

solutions.9. Recognize alternative optimal solutions.10.Level 2. Solve problems with alternative

solutions.11.Recognize unbounded solution.12.Level 3. Solve problems with unbounded

solutions.13.Level 4. Solve any maximization problem.14.Select entering variable for minimization

LP0´s.15.Level 5. Solve any minimization problem.16.Introduce artificial variables.17.Construct problem for Phase 1.18.Identify unfeasibility in Phase 1.19.Level 6. Solve problems with unfeasible

solutions.

Relationship among skills and types of problems is incremental

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Generation of problems in TAPLIThere are several approaches for automatic generation of problems (see Belmonte et al, 2002).In the TAPLI case, we need to generate:

A criterion (maximize, minimize) A set of numbers for objective function and constraints A direction for constraints (>=, <=)

Some basic rules control the random generation process, for example

For infinite solutions, one of the constraints should be parallel to the objective function.

Mechanisms are also used to control the difficulty of the computationsIn this way problems for each of the levels can be generated UNLIMITED SET OF PROBLEMS

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Coached problem solving in TAPLIThe level of the student is the highest level of problems that he/she can correctly solve.Therefore there are seven levels for SIETTE to classify students in.The integration with SIETTE is transparent to the student.Once the problem has been generated by the system or introduced by the student, he/she can solve it within the same environment.The sequence of steps will be guided by a set of applets integrated in SIETTE

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Coached problem solving in TAPLI

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Coached problem solving in TAPLIStudents can ask for helpIf students make mistakes, the system will provide feedbackBoth help and feedback are penalized by the systemProblems are evaluated by the applet, which classifies them as correct or incorrectThen SIETTE returns this information to TAPLI, so it can update the student model.

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TAPLI is an adaptive web-based learning environment for Linear ProgrammingIt is composed of three educational components: An adaptive and adaptable hypermedia component A drill-and-practice component A testing component

It allows for several types of adaptation: Adaptive navigation support (recommendations) Adaptive content presentation (stretch text, link

hiding) Adaptive problem generation

The system is being used by students at UMA as an extra aid for learning, but has not been formally evaluated

Conclussions

A Bayesian model to analyze collaboration in virtual communities

Beatriz Barros (expert on analyzing collaboration)Javier Burón (implementation)Eva Millán (expert on Bayesian Networks)

Something “new”

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MotivationVirtual learning communities allow now e-learning in groups, with a new perspective that allows active learning in collaboration with other peopleThese new trends in e-learning offer new challenges for researchers in social and collaborative learning, as the environments provide a set of rich data to use for the study of social activity:

How does the group organize the work? How does collaboration arise? When do conflicts arise? Which cases demand help? What was the procedure to get the solution?

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Our proposal:

Mechanism to control the virtual

community

Mechanisms for adapting resources

and content

Analysis algorithm

Virtual learning Community (logs)

Quantitative indicators About the

interaction About

actions Of social

Quantitative indicatorsIndicators of social states

Indicators of collaboration

Bayesian model

About performance(Martinez, 2003)

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IndicatorsQuantitative from interactionQuantitative from actionQuantitative of social typeQualitative of social typeQualitative from collaboration

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IndicatorsQuantitative indicators: Interaction Average intervention size, representing average size

of the contributions, measured in terms of the number of characters and weighted according to the activity type, divided by the total number of interventions in the community

Average number of interventions (in forums, chats, workshops), weighted by community size and by the duration of the activity

Average level of activity, which counts the number of interventions that were answered by a user different to the one that initiated it.

The average intervention size and number of interventions is weighted according to the type of activity, as shown in the following table

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Weights for the different types of activitiesActivity Action ValueForums Add a post 1

Add a discussion 1Evaluate a post 0.7Update a post 0.5Delete a post -1

Glosary Add a new term 1Update a term 0.5Add a comment 0.7Evaluate a term 0.7

Chat Talk 0.7Workshop Workshop delivery 1

Workshop update 0.8Wiki Edit a wiki 1Message Write a message 0.7

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More indicatorsQuantitative indicators: actions Number of actions, which counts for the

number of actions like access to web pages, clicks on links, resources or activities, divided by the number of participants.

Division of work, which measures if students divided the work (instead of collaborating to do it)

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More indicatorsQuantitative indicators for performance: Grade, that measures the quality of the

contributions of the groupQuantitative indicators of social type: Density, which measures the degree of

interconnection in the network Centralization, which is an structural measure

that indicates to what extent the network depends on some of its actors. A high value will indicate that the network depends on few actors, and vice-versa.

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Qualitative indicators of social type Sociability, measuring if all individuals interact

in order to solve the required tasks. Quality of the participation, which relates the

activity of each individual with the cognitive result as individually evaluated by a teacher.

Impasse, which accounts for situations of non-activity

Passivity, which accounts for situations in which individuals do not interact with each other.

Leadership, which measures if there is an individual which is leading the coordination of the group.

More indicators

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More indicatorsQualitative indicators of different types of group work Cooperation (individuals divide work) Coordination (there is a person that

organizes the work: he/she divides it, assigns it to the other members of the community, and generates results)

Collaboration (a group of individuals gets organized so that all members work together (all members in all tasks) and participate in a balanced way in all parts of the solution.

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Methodology

XML: logs of the community

CLIENT-MOODLEWeb services

XML: values of indicators

Logs of users

Web services compute values

of indicators

Moodle database

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The Bayesian network for GeNIe

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An exampleLogs of a MOODLE community studying Biology were analyzed.From the logs, it was automatically inferred that:

Average number of interventions = low Interactivity level = high, Average intervention size = high, Average number of actions = low, Division of work = yes, Density = high, Centralization = low, Average grade = passed

Set of evidence for the BN

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Results in this example:

From the evidence available in this community, it can be deduced that:• The community is not very sociable• The level of activity has been low• Is quite probable they did not reach an impass• There was not a clear leader• Probably there were coalitions• There was more cooperation than coordination and collaboration• In general, the quality of the participation was low

Computed values for indicatorsAverage number of interventions = lowInteractivity level = high, Average intervention size = high, Average number of actions = low, Division of work = yes, Density = high, Centralization = low, Average grade = passed

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ConclussionsA log analyzer based on Bayesian networks has been implemented and integrated into MOODLE.This log analyzer allows to infer the type and quality of the collaboration activity in a virtual communityThis is only a first step in the construction of a module that allows to analyze actions and interactions in virtual communities, independently of the platform being used.Our next steps are: Testing this algorithm in other platforms Evaluating these algorithms compared to other

methods and algorithms to analyze collaboration

Eva Millán

Thanks for your attention…

Contact at eva@lcc.uma.es

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Some more reasons to visit us:Malaga

ZaharaRonda

Sweet grapes,Sweet wine

Paella

Intelligent web-based tools to support e-learning

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