ULEARN: Personalised Learner's Profile Based on Dynamic Learning Style Questionnaire

E-Learning recommender system effectiveness relies upon their ability to recommend appropriate learning contents that match the learner preferences and learning style. An effective approach to handle the learner preferences is to build an efficient learner profile in order to gain adaptation and individualisation of the learning environment. It is usually necessary to know the preferences and learning style of the learner on a domain before adapting the learning process and course content. This study focuses on identifying the learners’ learning styles in order to adapt the course content and learning process. ULEARN is an adaptive recommender learning system devised to provide personalised learning environment, such as course learning objects that match the learner’s adaptive profile. This paper presents the algorithm used in ULEARN to reduce dynamically the number of questions in Felder-Silverman learning style questionnaire used to initialize the adaptive learner profile. Firstly, the questionnaire is restructured into four groups, one for each learning style dimension; and a study is carried out to determine the order in which questions will be asked in each dimension. Then an algorithm is built upon this ranking of questions to calculate dynamically the initial learning style of the user as they go through the questionnaire.

[1]  Steve Graham,et al.  Writing Better: Effective Strategies for Teaching Students with Learning Difficulties , 2005 .

[2]  Costin Badica,et al.  Accommodating Learning Styles in an Adaptive Educational System , 2010, Informatica.

[3]  Tieniu Tan,et al.  Robust eyelid, eyelash and shadow localization for iris recognition , 2008, 2008 15th IEEE International Conference on Image Processing.

[4]  Alexander Tuzhilin,et al.  Personalization in Context: Does Context Matter When Building Personalized Customer Models? , 2006, Sixth International Conference on Data Mining (ICDM'06).

[5]  Spencer P. Magleby,et al.  A Review of Literature on Teaching Engineering Design Through Project‐Oriented Capstone Courses , 1997 .

[6]  Tieniu Tan,et al.  A fast and robust iris localization method based on texture segmentation , 2004, SPIE Defense + Commercial Sensing.

[7]  M. Silberman.  Active Learning: 101 Strategies To Teach Any Subject. , 1996 .

[8]  Margaret E. Gredler,et al.  Games and Simulations and Their Relationships to Learning , 2013 .

[9]  Antonija Mitrovic,et al.  Using Evaluation to Shape ITS Design: Results and Experiences with SQL-Tutor , 2002, User Modeling and User-Adapted Interaction.

[10]  Essaid El Bachari,et al.  E-LEARNING PERSONALIZATION BASED ON DYNAMIC LEARNERS ' PREFERENCE , 2011 .

[11]  P. Jeffries,et al.  A FRAMEWORK for Designing, Implementing, and Evaluating: Simulations Used as Teaching Strategies in Nursing , 2005, Nursing education perspectives.

[12]  Hendrik Drachsler,et al.  Recommender Systems in Technology Enhanced Learning , 2011, Recommender Systems Handbook.

[13]  Franzoni Velázquez,et al.  A proposed method for adapting and integrating student learning style, teaching strategies and electronic media , 2009 .

[14]  Silvia Baldiris,et al.  Adaptation Decisions and Profiles Exchange among Open Learning Management Systems Based on Agent Negotiations and Machine Learning Techniques , 2009, HCI.

[15]  Clara-Inés Peña,et al.  Intelligent Agents in a Teaching and Learning Environment on the Web , 2002 .

[16]  Antonija Mitrovic,et al.  An Intelligent SQL Tutor on the Web , 2003, Int. J. Artif. Intell. Educ..

[17]  Masatoshi Yoshikawa,et al.  Adaptive web search based on user profile constructed without any effort from users , 2004, WWW '04.

[18]  Julie E. Mills,et al.  Engineering Education, Is Problem-Based or Project-Based Learning the Answer , 2003 .

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

[20]  George D. Magoulas,et al.  Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE , 2003, User Modeling and User-Adapted Interaction.

[21]  J. Pellegrino,et al.  Professional Development in Integrating Technology Into Teaching and Learning: Knowns, Unknowns, and Ways to Pursue Better Questions and Answers , 2007 .

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

[23]  Anthony Jameson,et al.  User Modeling and User-Adapted Interaction , 2004, User Modeling and User-Adapted Interaction.

[24]  Katrien Verbert,et al.  Recommender Systems for Technology Enhanced Learning , 2014, Springer New York.

[25]  Analía Amandi,et al.  eTeacher: Providing personalized assistance to e-learning students , 2008, Comput. Educ..

[26]  Martín López-Nores,et al.  Adapting Spreading Activation Techniques towards a New Approach to Content-Based Recommender Systems , 2010 .

[27]  Luciano Vieira Lima,et al.  Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: An experimental analysis , 2013, Expert Syst. Appl..

[28]  R. Felder,et al.  Learning and Teaching Styles in Engineering Education. , 1988 .

[29]  Juan E. Gilbert,et al.  Arthur: Adapting Instruction to Accommodate Learning Style , 1999, WebNet.

[30]  Patricia Alejandra Behar,et al.  Developing a Learning Objects Recommender System Based on Competences to Education: Experience Report , 2014, WorldCIST.

[31]  Judy Kay,et al.  Learner Control , 2001, User Modeling and User-Adapted Interaction.

[32]  Germain Simons,et al.  Learning and Teaching Styles , 2001 .

[33]  Erik Duval,et al.  Reusable learning objects: a survey of LOM-based repositories , 2002, MULTIMEDIA '02.

[34]  Haitham A. El-Ghareeb,et al.  Review of e-Learning Systems Convergence from Traditional Systems to Services based Adaptive and Intelligent Systems , 2009, J. Convergence Inf. Technol..

[35]  Enrique Alfonseca,et al.  The impact of learning styles on student grouping for collaborative learning: a case study , 2006, User Modeling and User-Adapted Interaction.

[36]  Katerina Kostolányová,et al.  Analysis of Learning Styles for Adaptive E-Learning , 2011, ICDIPC.

[37]  Vincent P. Wade,et al.  Adaptive educational hypermedia systems in technology enhanced learning: a literature review , 2010, SIGITE '10.

[38]  Peter Brusilovsky,et al.  User Models for Adaptive Hypermedia and Adaptive Educational Systems , 2007, The Adaptive Web.

[39]  Alexandros Paramythis,et al.  Adaptive Learning Environments and e-Learning Standards. , 2004 .

[40]  Adrian Parkinson,et al.  Equilibriating instructional media for cognitive styles , 2003, ITiCSE.

[41]  Ann O'Brien,et al.  Learning Objects Update: Review and Critical Approach to Content Aggregation , 2008, J. Educ. Technol. Soc..

[42]  José Fernando Rodrigues,et al.  An approach to design the student interaction based on the recommendation of e-learning objects , 2010, SIGDOC '10.

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

[44]  Deniz Ortaçtepe,et al.  Approaches and Methods in Language Teaching , 2016 .

[45]  Kathryn A. Becker-Blease,et al.  Adaptive Quizzing Associated with an Increase in Overall Learning , 2014 .

[46]  Fabiano A. Dorça,et al.  An Automatic Approach for Customization of Teaching Process Based on Learning Styles in Adaptive and Intelligent Learning Systems , 2014 .

[47]  Curtis A. Carver,et al.  Enhancing student learning through hypermedia courseware and incorporation of student learning styles , 1999 .

[48]  Francesco Ricci,et al.  Context-aware music recommender systems: workshop keynote abstract , 2012, WWW.

[49]  Christian Wolf,et al.  iWeaver: Towards 'Learning Style'-based e-Learning in Computer Science Education , 2003, ACE.