A Personalized Learning Strategy Recommendation Approach for Programming Learning

Nowadays, it has been a significant problem to recommend learning strategy for different learners in programming learning projects. This paper discusses a personalized learning strategy recommendation approach to aid programming learning. In this paper, an improved design method of model learner strategies and programming learning strategy recommendation approach are presented. A reward factor is adopted to help to construct a learning strategy recommendation mechanism adaptively. The programming learning strategy recommendation system (ZZULI-PLS) is proposed based on those models to help learners learning in programming according to the actual progresses of learners. Usability tests are conducted to validate the recommendation efficiency in ZZULI-PLS system.

[1]  Haoran Xie,et al.  A Comparative Study on Various Vocabulary Knowledge Scales for Predicting Vocabulary Pre-Knowledge , 2017, Int. J. Distance Educ. Technol..

[2]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[3]  Tony Jenkins,et al.  The motivation of students of programming , 2001, ITiCSE '01.

[4]  Zhendong Niu,et al.  Learning Strategy Recommendation Agent , 2012 .

[5]  Ellen Francine Barbosa,et al.  A recommendation system to support the students performance in programming contests , 2014, 2014 IEEE Frontiers in Education Conference (FIE) Proceedings.

[6]  F. Colace,et al.  Adaptive hypermedia system in education: A user model and tracking strategy proposal , 2007, 2007 37th Annual Frontiers In Education Conference - Global Engineering: Knowledge Without Borders, Opportunities Without Passports.

[7]  Wilson Vicente Ruggiero,et al.  An Approach to Personalisation in E-learning , 2007, Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007).

[8]  Alexandra I. Cristea,et al.  Learning Styles Adaptation Language for Adaptive Hypermedia , 2006, AH.

[9]  Gamze Sezgin Selçuk,et al.  Learning Strategies of Physics Teacher Candidates: Relationships with Physics Achievement and Class Level , 2007 .

[10]  Sei-Wang Chen,et al.  A programming learning system for beginners-a completion strategy approach , 2000, IEEE Trans. Educ..

[11]  Fernando Mendes de Azevedo,et al.  Adaptive Interface Methodology for Intelligent Tutoring Systems , 2004, Intelligent Tutoring Systems.

[12]  Raymond Y. K. Lau,et al.  Generating Incidental Word-Learning Tasks via Topic-Based and Load-Based Profiles , 2016, IEEE MultiMedia.

[13]  Haoran Xie,et al.  The Augmented Hybrid Graph Framework for Multi-level E-Learning Applications , 2016, ICBL.