1. CHICO Group Department of Technologies and Information Systems Castilla – La Mancha University (Spain) Tutoring System for Programming Algorithm Learning Francisco Jurado Monroy

2. Position in CHICO: Doctoral student Position in UCLM: Grant holder of the Junta de Comunidades de Castilla La-Mancha Maximum Degree: Computer Science Engineer Research Lines: eLearning standards Distributed Intelligent Tutoring Systems for programming learning Possible Research Stays: YES with Funding.

3. Outline Research motivation Architectural approach System implementation Student cognitive model Instructional model Artefact model Process model

4. Research motivation Programming learning is an important subject for the students of computer science. Students must acquire knowledge and abilities which will deal with their future programming work for solving real problems Students have to solve several difficulties [Brusilovski et al., 1998], [Gomes & Mendes, 1999]. [Brusilovsky et al.,1998] Brusilovsky, P.; Calabrese, E.; Hvorecky, J.; Kouchnirenko, A. & Miller, P. (1998): 'Mini-languages: a way to learn programming principles', Education and Information Technologies 2, pp. 65 -83. [Gomes & Mendes, 2002] Gomes, A. & A.J., M. (2001): Computers and Education in an Interconnected Society, Kluwer Academic Publishers, chapter SICAS: Interactive system for algorithm development and simulation, pp. 159-166.

5. Architectural approach Artefact Model (Imprecision) Process Model (Work flow) Change Particular case (PBL for programming learning) Standard eLearning services integration Student Cognitive Model (Uncertainty) Instructional Model (Learning Design) ITS Solution

6. System implementation: IMS-AF with ICE (I) Prerequisites: Heterogeneity and distribution of services and devices Application in several educational and computational eLearning paradigms (Virtual Learning, Blended Learning, Mobile Learning, ubiquitous educational environments, etc.) Require the middleware to be independent from operating system hardware device programming language. Our proposal: Implementing IMS-Abstract Framework using ICE (Internet Communication Engine) [Jurado et al., 2007] Jurado, F., Redondo, M.A. & Ortega, M.: Enabling distributed eLearning environments integrating ICE-based services. In: Proceeding of the International Technology, Education and Development Conference INTED2007, Valencia, Spain(2007)

7. System implementation: IMS-AF with ICE (II) ICE (Internet Communication Engine) Their authors tried: “to build a middleware platform that is as powerful as CORBA, without making all of CORBA mistakes”. Object-oriented middleware Independent: From the programming language: Slice (Specification Language for ICE) abstraction to separate interfaces of the objects from implementation. Mapping from Slice to C++, Java, C#, Visual Basic.NET, Python, and PHP From the platform Implementations for different architectures and operating systems. Services and tools to facilitate the construction of heterogeneous distributed systems.

8. Student cognitive model: Bayesian network Bayesian Networks (BN) Allows the process of uncertainly Suitable in diagnostic situations, that is, it allows that given an evidence (known values for a set of variables), the subsequent probability for the non observed variables can be calculated. This is known as evidence propagation.

9. Student cognitive model: Bayesian network SiSi Ch i CiCi CiCi CiCi CiCi PiPi PiPi PiPi PiPi PiPi PiPi PiPi Three layers: Subjects (Si) Chapters (Chj) Concepts (Ci) Get the evidence: Problems (Pk) that teacher porpoise to students. Relations will go from the concepts nodes to the subject nodes Ci  Tj  A.

10. Instructional model: IMS-LD Allow specify instructional strategies Theatre metaphor Method is divided in play elements Play elements contain several acts Roles Activities: learning activities, support activities, structure activities Environment

11. Cognitive model & Instructional model Towel, B. & Halm, M. (2005): Learning design: A handbook on modelling and delivering networked education and training. Springer-Verlag, chapter 12 - Designing Adaptive Learning Environments with Learning Design, pp. 215-226. IMS-LD can be used for developing adaptive learning (AL) [Towel & Halm, 2005] LD enriched with variables from student profile Conditions to show/hide learning activities to a specific student Example: IF student::(Knowledge, less-than, 5) THEN hide activity A1 and show activity A2 ELSE show activity A1 and hide activity A2

12. Cognitive model & Instructional model In our architecture The variables used for defining the adaptation rules, are obtained from the student model represented with BN. In programming learning, the evidence nodes must obtain its value from the artefact (algorithm) developed by the student.

13. Artefact model: algorithm analysis with fuzzy logic (I) Comparing the artefact (algorithm) developed by the student with that specified by an expert (teacher). It is necessary to have a way for representing the approximate ideal algorithm that the expert (the teacher) estimates for solving a certain problem. The algorithm that the student has written will be compared with that approximate ideal representation. Techniques of code similarities analysis Algorithm that the student has written is better whatever nearer to the approximate ideal representation for the solution of the problem. Our proposal: Use Fuzzy Logic

14. Artefact model: algorithm analysis with fuzzy logic (II) Teacher Algorithm that solves the problem Ideal Approximated Algorithm Fuzzy Representation Algorithm for trying to solve the problem Writes Metrics calculation Metrics calculation Writes Degree of membership with the fuzzy set Student Jurado, F.; Redondo, M.A. & Ortega, M. (2007): Representación difusa de algoritmos para su aplicación en sistemas tutores inteligentes orientados al aprendizaje de la programación, in 'EATIS'07 ACM-DL Proceedings', Association for Computing Machinery, Inc (ACM) (Acepted).

15. Artefact model: working environment Working method Metrics calculated for each method Actions over the selected method Metrics view tab Working file

16. Process model Steps the student has made till reaching the final solution A log with: Changes made to the code that implements the algorithms Software metrics List of errors and warnings returned by the compilation process Acquiring knowledge from information Automatic machine learning techniques: Data mining, fuzzy logic rules extraction, etc.

17. CHICO Group Department of Technologies and Information Systems Castilla – La Mancha University (Spain) Tutoring System for Programming Algorithm Learning Francisco Jurado Monroy Thank you for your attention