Intelligent web-based tools to support e-learning

1. Intelligent web-based tools to support e-learning Eva Millán (IA)2 Group University of Málaga (ia)2

2. 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 Programming • I have also worked in other projects developed in my research team, like MEDEA and SIETTE • Lately, 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

3. 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

4. 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)

5. Something “old” TAPLI: a web-based tool for Linear Programming Eduardo Guzman (connection with SIETTE) Emilio Garcia (implementation) Eva Millán (domain expert, design, development of contents)

6. Introduction • TAPLI is an adaptive web-based learning environment for Linear Programming, that consists in the integration of three tools in the same environment: • An adaptive hypermedia component, that is responsible of presenting the learning contents; • An adaptive testing component, that allows self-evaluation using tests • An adaptive drill‑and‑practice component, which: • dynamically generates exercises • coaches students, offering guidance, support, help and feedback.

7. Motivation • Tapli is a web-based learning environment about Linear Programming • TAPLI 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

8. The domain • What 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

9. The domain • To 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: First step: Introduce slack variables to convert the inequalities into equalities

10. To our purposes, the important thing is that…. • The domain is strongly based in problem solving • The steps are always performed in the same order • Types of errors are easily identified • Problems can be generated at the right level of difficulty • Which, in our context, allows: • Coached problem solving • Dynamic generation of problems

11. Components in TAPLI • TAPLI 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

12. The tools in TAPLI: Adaptive hypermedia

13. The tools in TAPLI: Adaptive hypermedia Curriculum

14. The tools in TAPLI: Adaptive hypermedia Learning contents

15. The tools in TAPLI: Adaptive hypermedia Recommendations

16. The tools in TAPLI: Adaptive hypermedia Student model

17. The tools in TAPLI: Adaptive hypermedia Student model

18. The tools in TAPLI: Adaptive hypermedia Student model

19. The tools in TAPLI: Adaptive hypermedia Student model

20. The tools in TAPLI: Adaptive hypermedia Pages visited by student

21. The tools in TAPLI: Adaptive hypermedia Pages visited by student

22. Adaptation in the hypermedia component • 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)

23. The testing component in TAPLI • While learning contents, students can test their knowledge using tests or exercises • Both activities are supported by the SIETTE system • SIETTE 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).

24. A few words about SIETTE For students to take tests For teachers to define tests

25. Some words about SIETTE • It 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 modelingcomponent 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)

26. Linear Programming tests in SIETTE

27. Communication between TAPLI and SIETTE:Architecture

28. Interactivity with the hypermedia component

29. The architecture of TAPLI Presents theoretical concepts and examples

30. The architecture of TAPLI Evaluates student’s knowledge

31. The architecture of TAPLI Coaches problem solving

32. The architecture of TAPLI Selects the next component

33. The architecture of TAPLI Stores the student model

34. 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.

35. 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?

36. Generation of problems in TAPLI Relationship among skills and types of problems is incremental • Introduce slack variables. • Fill in the simplex tableau with data. • Identify solution and objective value in the tableau. • Select entering variable for maximization LP0´s. • Select leaving variable. • Perform calculations. • Identify optimal solutions. • Level 1. Solve max. problems with unique solutions. • Recognize alternative optimal solutions. • Level 2. Solve problems with alternative solutions. • Recognize unbounded solution. • Level 3. Solve problems with unbounded solutions. • Level 4. Solve any maximization problem. • Select entering variable for minimization LP0´s. • Level 5. Solve any minimization problem. • Introduce artificial variables. • Construct problem for Phase 1. • Identify unfeasibility in Phase 1. • Level 6. Solve problems with unfeasible solutions.

37. Generation of problems in TAPLI • There 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 computations • In this way problems for each of the levels can be generated  UNLIMITED SET OF PROBLEMS

38. Coached problem solving in TAPLI • The 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

39. Coached problem solving in TAPLI

40. Coached problem solving in TAPLI • Students can ask for help • If students make mistakes, the system will provide feedback • Both help and feedback are penalized by the system • Problems are evaluated by the applet, which classifies them as correct or incorrect • Then SIETTE returns this information to TAPLI, so it can update the student model.

41. Conclussions • TAPLI is an adaptive web-based learning environment for Linear Programming • It 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

42. Something “new” A Bayesian model to analyze collaboration in virtual communities Beatriz Barros (expert on analyzing collaboration) Javier Burón (implementation) Eva Millán (expert on Bayesian Networks)

43. Motivation • Virtual learning communities allow now e-learning in groups, with a new perspective that allows active learning in collaboration with other people • These 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?

44. Our proposal: Virtual learning Community (logs) Quantitative indicators About performance About actions Of social type About the interaction (Martinez, 2003) Analysis algorithm Bayesian model Quantitative indicators Indicators of social states Indicators of collaboration Mechanism to control the virtual community Mechanisms for adapting resources and content

45. Indicators • Quantitative from interaction • Quantitative from action • Quantitative of social type • Qualitative of social type • Qualitative from collaboration

46. Indicators • Quantitative 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

47. Weights for the different types of activities

48. More indicators • Quantitative 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)

49. More indicators • Quantitative indicators for performance: • Grade, that measures the quality of the contributions of the group • Quantitative 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.

50. More indicators • 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.