A multi-constraint learning path recommendation algorithm based on knowledge map

Abstract It is difficult for e-learners to make decisions on how to learn when they are facing with a large amount of learning resources, especially when they have to balance available limited learning time and multiple learning objectives in various learning scenarios. This research presented in this paper addresses this challenge by proposing a new multi-constraint learning path recommendation algorithm based on knowledge map. The main contributions of the paper are as follows. Firstly, two hypotheses on e-learners’ different learning path preferences for four different learning scenarios (initial learning, usual review, pre-exam learning and pre-exam review) are verified through questionnaire-based statistical analysis. Secondly, according to learning behavior characteristics of four types of the learning scenarios, a multi-constraint learning path recommendation model is proposed, in which the variables and their weighted coefficients considers different learning path preferences of the learners in different learning scenarios as well as learning resource organization and fragmented time. Thirdly, based on the proposed model and knowledge map, the design and implementation of a multi-constraint learning path recommendation algorithm is described. Finally, it is shown that the questionnaire results from over 110 e-learners verify the effectiveness of the proposed algorithm and show the similarity between the learners’ self-organized learning paths and the recommended learning paths.

[1]  Aviv Segev,et al.  Knowledge maps for e-learning , 2011, Comput. Educ..

[2]  Ray-I Chang,et al.  Data mining for providing a personalized learning path in creativity: An application of decision trees , 2013, Comput. Educ..

[3]  Hongtao Liu,et al.  Multi-faceted Learning Paths Recommendation Via Semantic Linked Network , 2010, 2010 Sixth International Conference on Semantics, Knowledge and Grids.

[4]  Colin Tattersall,et al.  Swarm-based sequencing recommendations in e-learning , 2005, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05).

[5]  Qinghua Zheng,et al.  Automatic extraction of titles from general documents using machine learning , 2006, Inf. Process. Manag..

[6]  Jie Lu,et al.  A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system , 2015, Decis. Support Syst..

[7]  Qinghua Zheng,et al.  A collaborative knowledge construction system design for massive knowledge resources , 2009, 2009 13th International Conference on Computer Supported Cooperative Work in Design.

[8]  David H. Jonassen,et al.  Constructivism and the Technology of Instruction : A Conversation , 2013 .

[9]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[10]  Nelson Baloian,et al.  Combining learning with patterns and geo-collaboration to support situated learning , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[11]  Yueqin Zhang,et al.  An Improved Ant Colony Optimization Algorithm for Recommendation of Micro-Learning Path , 2016, 2016 IEEE International Conference on Computer and Information Technology (CIT).

[12]  Nicola Henze,et al.  Logically Characterizing Adaptive Educational Hypermedia Systems , 2003 .

[13]  Francesco Colace,et al.  Ontology for E-Learning: A Bayesian Approach , 2010, IEEE Transactions on Education.

[14]  Qinghua Zheng,et al.  Personalized Learning Strategies in an intelligent e-Learning Environment , 2007, 2007 11th International Conference on Computer Supported Cooperative Work in Design.

[15]  Qinghua Zheng,et al.  Mining learning-dependency between knowledge units from text , 2011, The VLDB Journal.

[16]  Wei Wang,et al.  Recommender system application developments: A survey , 2015, Decis. Support Syst..

[17]  Hayato Yamana,et al.  Generalized Sequential Pattern Mining with Item Intervals , 2006, J. Comput..

[18]  Julià Minguillón Alfonso,et al.  Content management for e-learning , 2011 .

[19]  Ruey-Shiang Shaw,et al.  A study of learning performance of e-learning materials design with knowledge maps , 2010, Comput. Educ..

[20]  Fan Yang,et al.  Learning Path Construction in e-Learning , 2017 .

[21]  Mohamed Bendahmane,et al.  Individualized Learning Path Through a Services-Oriented Approach , 2017 .

[22]  Mojtaba Salehi,et al.  Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering , 2012, Education and Information Technologies.

[23]  Kuo-Kuang Chu,et al.  Ontology technology to assist learners' navigation in the concept map learning system , 2011, Expert Syst. Appl..

[24]  Nabil Belacel,et al.  Graph theory based model for learning path recommendation , 2013, Inf. Sci..

[25]  Alexey Dukhanov,et al.  An Approach of Learning Path Sequencing Based on Revised Bloom's Taxonomy and Domain Ontologies with the Use of Genetic Algorithms , 2015 .

[26]  Mojtaba Salehi,et al.  Hybrid recommendation approach for learning material based on sequential pattern of the accessed material and the learner's preference tree , 2013, Knowl. Based Syst..

[27]  Ria Hanewald,et al.  Cultivating Life-Long Learning Skills in Undergraduate Students through the Collaborative Creation of Digital Knowledge Maps , 2012 .

[28]  Gwo-Jen Hwang,et al.  A Heuristic Algorithm for planning personalized learning paths for context-aware ubiquitous learning , 2010, Comput. Educ..

[29]  Zhendong Niu,et al.  A learner oriented learning recommendation approach based on mixed concept mapping and immune algorithm , 2016, Knowl. Based Syst..

[30]  Cheng Yan.  A Method of Swarm Intelligence-Based Learning Path Recommendation for Online Learning , 2011 .

[31]  Mahmoud Abd Ellatif,et al.  A proposed paradigm for smart learning environment based on semantic web , 2017, Comput. Hum. Behav..

[32]  Eugenijus Kurilovas,et al.  Recommending suitable learning scenarios according to learners' preferences: An improved swarm based approach , 2014, Comput. Hum. Behav..

[33]  Chun-Hsiung Lee,et al.  Analysis on the adaptive scaffolding learning path and the learning performance of e-learning , 2008 .

[34]  Carlos Delgado Kloos,et al.  Lessons learned from the design of situated learning environments to support collaborative knowledge construction , 2015, Comput. Educ..

[35]  Vibhor Kant,et al.  Learning path recommendation based on modified variable length genetic algorithm , 2018, Education and Information Technologies.

[36]  George Siemens.  Connectivism: A Learning Theory for the Digital Age , 2004 .

[37]  Hayat Zouiten,et al.  Optimizing collaborative learning path by ant's optimization technique in e-learning system , 2016, 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET).

[38]  Mike Paterson,et al.  A Faster Algorithm Computing String Edit Distances , 1980, J. Comput. Syst. Sci..

[39]  Jin-Cherng Lin,et al.  Finding a Fitting Learning Path in E-learning for Juvenile , 2007, Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007).

[40]  Samir Roy,et al.  Online Recommendation of Learning Path for an E-Learner under Virtual University , 2013, ICDCIT.

[41]  Chung-Ho Su,et al.  Designing and Developing a Novel Hybrid Adaptive Learning Path Recommendation System (ALPRS) for Gamification Mathematics Geometry Course , 2017 .

[42]  Edmund Y. Lam,et al.  A new framework of concept clustering and learning path optimization to develop the next-generation e-learning systems , 2014 .

[43]  Hahn-Ming Lee,et al.  Personalized e-learning system using Item Response Theory , 2005, Comput. Educ..

[44]  Denise C. Park,et al.  Neuroplasticity and cognitive aging: the scaffolding theory of aging and cognition. , 2009, Restorative neurology and neuroscience.

[45]  Angela M. O'Donnell,et al.  Knowledge Maps as Scaffolds for Cognitive Processing , 2002 .

[46]  Brett E. Shelton,et al.  Building an Online Adaptive Learning and Recommendation Platform , 2016, SETE@ICWL.