CHICO GroupDepartment of Technologies

and Information SystemsCastilla – La Mancha University (Spain)

Tutoring System for Programming Algorithm Learning

Francisco Jurado Monroy

Francisco Jurado Monroy

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.

Outline

Research motivation Architectural approach

System implementation Student cognitive model Instructional model Artefact model Process model

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.

Architectural approach

ArtefactModel

(Imprecision)

ProcessModel

(Work flow)

Change

Particular case(PBL for programming learning)

Standard eLearning services integration

StudentCognitive Model

(Uncertainty)

InstructionalModel

(Learning Design)

ITS

Solution

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)

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.

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.

Student cognitive model: Bayesian network

Si

Chi Chi

Ci Ci Ci Ci

Pi Pi Pi Pi Pi Pi Pi

•Three layers:•Subjects (Si)•Chapters (Chj)•Concepts (Ci)

•Get the evidence:•Problems (Pk) that teacher porpoise to students.

Relations will go from the concepts nodesto the subject nodes CiTjA.

Instructional model:IMS-LD Allow specify instructional

strategies Theatre metaphor

Method is divided in play elementsPlay elements contain several acts

RolesActivities: learning activities, support

activities, structure activitiesEnvironment

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

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.

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

Artefact model: algorithm analysis with fuzzy logic (II)

Teacher

Algorithm thatsolves the problem

IdealApproximated Algorithm

Fuzzy Representation

Algorithm for tryingto solve the problem

Writes

Metricscalculation

Metricscalculation

Writes

Degree of membershipwith 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).

Artefact model: working environment

Working methodMetrics calculated for

each methodActions over the selected

method

Metrics view tab

Working file

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.

CHICO GroupDepartment of Technologies

and Information SystemsCastilla – La Mancha University (Spain)

Tutoring System for Programming Algorithm Learning

Francisco Jurado Monroy

Thank you for your attention