Dynamically Personalized E-Training in Computer Programming and the Language C

This paper describes ELaC, a fully implemented and evaluated novel integrated environment for personalized e-training in programming and the language C. Software development relies on many different programming languages and tools, ranging from procedural to object-oriented and query languages; an individual learning a new language may already know a range of other languages, or may know no other languages at all. Given the variety of backgrounds of prospective learners of programming, developing learning environments for all of them is not easy. In the light of these problems, this work has focused on the development of an original integrated e-training environment for programming and the language C, incorporating a student model responsible for identifying and updating the student's knowledge level, which takes into account each individual user's pace of learning. The system can adapt dynamically to each individual learner's needs by scheduling the sequence of learning lessons on the fly. This personalization allows each learner to complete the e-training course on at their own pace and according to their ability.

[1]  Slavomir Stankov,et al.  Controlled experiment replication in evaluation of e-learning system's educational influence , 2009, Comput. Educ..

[2]  Alenka Kavcic,et al.  The role of user models in adaptive hypermedia systems , 2000, 2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099).

[3]  Peter Brusilovsky,et al.  ELM-ART: An Adaptive Versatile System for Web-based Instruction , 2001 .

[4]  José-Luis Pérez-de-la-Cruz,et al.  Bayesian networks for student model engineering , 2010, Comput. Educ..

[5]  Cristina Gena,et al.  Methods and techniques for the evaluation of user-adaptive systems , 2005, The Knowledge Engineering Review.

[6]  Alenka Kavcic,et al.  Fuzzy user modeling for adaptation in educational hypermedia , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Seiji Isotani,et al.  Towards a Bayesian Student Model for Detecting Decimal Misconceptions , 2011 .

[8]  Vladan Devedzic,et al.  Knowledge modeling - State of the art , 2001, Integr. Comput. Aided Eng..

[9]  Maria Virvou,et al.  Evaluation of an Intelligent Web-Based Language Tutor , 2003, KES.

[10]  Ramesh C. Jain,et al.  A user model for personalization services , 2009, 2009 Fourth International Conference on Digital Information Management.

[11]  Yavuz Akbulut,et al.  Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011 , 2012, Comput. Educ..

[12]  Peter Brusilovsky,et al.  Guiding students to the right questions: adaptive navigation support in an E-Learning system for Java programming , 2010, J. Comput. Assist. Learn..

[13]  Shiou-Wen Yeh,et al.  Designing an adaptive web-based learning system based on students' cognitive styles identified online , 2012, Comput. Educ..

[14]  Somnuk Phon-Amnuaisuk,et al.  Properties of Bayesian student model for INQPRO , 2010, Applied Intelligence.

[15]  Beverly Park Woolf,et al.  Student Modeling , 2010, Advances in Intelligent Tutoring Systems.

[16]  Kenneth R. Koedinger,et al.  A Machine Learning Approach for Automatic Student Model Discovery , 2011, EDM.

[17]  Dragan Gasevic,et al.  Evaluating an Intelligent Tutoring System for Design Patterns: the DEPTHS Experience , 2009, J. Educ. Technol. Soc..

[18]  Peter Brusilovsky,et al.  QuizMap: Open Social Student Modeling and Adaptive Navigation Support with TreeMaps , 2011, EC-TEL.

[19]  Dragan Gasevic,et al.  Student modeling and assessment in intelligent tutoring of software patterns , 2012, Expert Syst. Appl..

[20]  Zoran Budimac,et al.  E-Learning personalization based on hybrid recommendation strategy and learning style identification , 2011, Comput. Educ..

[21]  Peter Brusilovsky,et al.  Adaptive Navigation Support , 2007, The Adaptive Web.

[22]  Cristina Conati,et al.  Using Bayesian Networks to Manage Uncertainty in Student Modeling , 2002, User Modeling and User-Adapted Interaction.