Eliciting Teachers' ICT Competence Profiles Based on Usage Patterns within Learning Object Repositories

User profiling is a technique aimed at capturing and exploiting significant characteristics of the users towards the provision of personalized services within adaptive systems such as Recommender Systems (RS). In the context of Technology enhanced Learning (TeL), from a teachers' perspective, the unique ICT competence characteristics of individuals have not been considered when providing Learning Object (LO) recommendations, despite their vital contribution to the level of ICT uptake of teachers. Moreover, there is a lack of mechanisms for automatically eliciting and updating such personal characteristics within Learning Object Repositories (LOR) in order to exploit them for enhanced LO recommendations. Towards tackling this issue, this paper proposes a teacher ICT Competence elicitation mechanism utilizing fuzzy logic for inferring teachers' ICT Competences based on their usage patterns within LOR and presents the results of its preliminary accuracy evaluation. The results indicate that the proposed approach provides high accuracy and can, therefore, construct reliable depictions of the teachers' ICT Competence Profiles.

[1]  Umberto Straccia,et al.  User recommendation for collaborative and personalised digital archives , 2005, Int. J. Web Based Communities.

[2]  Sheizaf Rafaeli,et al.  QSIA - a Web-based environment for learning, assessing and knowledge sharing in communities , 2004, Comput. Educ..

[3]  Rory McGreal,et al.  A Typology of Learning Object Repositories , 2008 .

[4]  Antonio Moreno,et al.  Automatic preference learning on numeric and multi-valued categorical attributes , 2014, Knowl. Based Syst..

[5]  Pasquale Lops,et al.  Learning Preference Models in Recommender Systems , 2010, Preference Learning.

[6]  Sarah Prestridge,et al.  The beliefs behind the teacher that influences their ICT practices , 2012, Comput. Educ..

[7]  Mario García Valdez,et al.  A Hybrid Recommender System Architecture for Learning Objects , 2009, Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control.

[8]  Yuanyuan Wang,et al.  Semantic ranking of lecture slides based on conceptual relationship and presentational structure , 2010, RecSysTEL@RecSys.

[9]  David A. Wiley,et al.  The Instructional Use of Learning Objects: Online Version , 2000 .

[10]  Martin Cooper,et al.  From ICT coordination to ICT integration: a longitudinal case study , 2010, J. Comput. Assist. Learn..

[11]  Kamal Kant Bharadwaj,et al.  Fuzzy-genetic approach to recommender systems based on a novel hybrid user model , 2008, Expert Syst. Appl..

[12]  Víctor Hugo Menéndez-Domínguez,et al.  A framework for recommendation in learning object repositories: An example of application in civil engineering , 2013, Adv. Eng. Softw..

[13]  Erik Duval,et al.  Dataset-driven research for improving recommender systems for learning , 2011, LAK.

[14]  Víctor Hugo Menéndez-Domínguez,et al.  Fuzzy ontologies-based user profiles applied to enhance e-learning activities , 2012, Soft Comput..

[15]  Kun Hua Tsai,et al.  A Learning Objects Recommendation Model based on the Preference and Ontological Approaches , 2006, Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06).

[16]  Konstantina Chrysafiadi,et al.  Student modeling approaches: A literature review for the last decade , 2013, Expert Syst. Appl..

[17]  George Karypis,et al.  A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.

[18]  Panagiotis Zervas,et al.  ICT Competence-Based Learning Object Recommendations for Teachers , 2014, CELDA 2014.

[19]  Deepa Anand,et al.  Folksonomy-based fuzzy user profiling for improved recommendations , 2014, Expert Syst. Appl..

[20]  Xu Qi.  Research on User Profiling Technology for Personalized Demands , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[21]  L. Pamela Cook,et al.  1. Introduction and Background: An Overview , 1993 .

[22]  Orlando Rodriguez,et al.  A hybrid system of pedagogical pattern recommendations based on singular value decomposition and variable data attributes , 2013, Inf. Process. Manag..

[23]  Carla Limongelli,et al.  Supporting Teachers to Retrieve and Select Learning Objects for Personalized Courses in the Moodle_LS Environment , 2012, 2012 IEEE 12th International Conference on Advanced Learning Technologies.

[24]  Lloyd Rutledge,et al.  ReMashed - Recommendations for Mash-Up Personal Learning Environments , 2009, EC-TEL.

[25]  Rosa Alarcón,et al.  Recommending Learning Objects According to a Teachers' Contex Model , 2010, EC-TEL.

[26]  Panagiotis Germanakos,et al.  Modeling users on the World Wide Web based on cognitive factors, navigation behavior and clustering techniques , 2013, J. Syst. Softw..

[27]  Johan van Braak,et al.  Institutionalised ICT use in primary education: A multilevel analysis , 2014, Comput. Educ..

[28]  Daniel D. Garcia,et al.  Social navigation for educational digital libraries , 2010, RecSysTEL@RecSys.

[29]  Etienne E. Kerre,et al.  Defuzzification: criteria and classification , 1999, Fuzzy Sets Syst..

[30]  Jon Dron,et al.  CoFIND - An experiment in N-dimensional collaborative filtering , 2000, J. Netw. Comput. Appl..

[31]  Chien-Yuan Su,et al.  A Fuzzy Logic-based Personalized Learning System for Supporting Adaptive English Learning , 2012, J. Educ. Technol. Soc..

[32]  Panagiotis Zervas,et al.  Towards Learning Object Recommendations Based on Teachers' ICT Competence Profiles , 2014, 2014 IEEE 14th International Conference on Advanced Learning Technologies.

[33]  Antonio Moreno,et al.  On-line dynamic adaptation of fuzzy preferences , 2013, Inf. Sci..

[34]  Riina Vuorikari,et al.  Collaborative recommendation of e-learning resources: an experimental investigation , 2010, J. Comput. Assist. Learn..

[35]  Fernando Ortega,et al.  Improving collaborative filtering recommender system results and performance using genetic algorithms , 2011, Knowl. Based Syst..

[36]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[37]  Daniel Burgos,et al.  Meta-Mender: A meta-rule based recommendation system for educational applications , 2010, RecSysTEL@RecSys.

[38]  Andreas Dengel,et al.  Extraction of Contextualized User Interest Profiles in Social Sharing Platforms , 2010, J. Univers. Comput. Sci..

[39]  Denis Gillet,et al.  The 3A Personalized, Contextual and Relation-based Recommender System , 2010, J. Univers. Comput. Sci..

[40]  Paul Seitlinger,et al.  Towards a user model for personalized recommendations in work-integrated learning: A report on an experimental study with a collaborative tagging system , 2010, RecSysTEL@RecSys.

[41]  Mimi Recker,et al.  Collaborative Information Filtering: A Review and an Educational Application , 2004, Int. J. Artif. Intell. Educ..

[42]  Johan van Braak,et al.  Student teachers' thinking processes and ICT integration: Predictors of prospective teaching behaviors with educational technology , 2010, Comput. Educ..

[43]  Christos Mettouris,et al.  Context-Aware Recommendations using Topic Maps Technology for the Enhancement of the Creativity Process , 2012 .

[44]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[45]  Brett E. Shelton,et al.  Linking open course wares and open education resources: creating an effective search and recommendation system , 2010, RecSysTEL@RecSys.

[46]  Hendrik Drachsler,et al.  A Trust-based Social Recommender for Teachers , 2012, RecSysTEL@EC-TEL.