Proceedings Article10.1109/CIT.2016.47
An Improved Ant Colony Optimization Algorithm for Recommendation of Micro-Learning Path
Qin Zhao,Yueqin Zhang,Jian Chen +2 more
- 01 Dec 2016
- pp 190-196
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TL;DR: The premature problem of ant colony algorithm is solved by optimizing the mechanism of initialization and update of pheromone and the experimental results show that the algorithm has high efficiency in micro-learning path recommendation.
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Abstract: This paper proposes an approach of recommending micro-learning path based on improved ant colony optimization algorithm. Micro-learning is a new learning style, which can be used to support learning in short time because of its micro-learning units. Each micro-learning unit consists of a small knowledge unit that can be learned at fragmented time. Meanwhile, micro-learning is more flexible than other learning styles in organizing or reorganizing learning path according to the transition of learner. In order to improve learning efficiency, a suitable learning path contained a sequence of micro-learning units is recommended to learner, which is optimized according to his/her transition. During the process of micro-learning, the proposed algorithm can detect learner's learning transitions of knowledge level, knowledge area and learning goal according to the operation of learner. In this study, the premature problem of ant colony algorithm is solved by optimizing the mechanism of initialization and update of pheromone. The experimental results show that the algorithm has high efficiency in micro-learning path recommendation.
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Citations
A multi-constraint learning path recommendation algorithm based on knowledge map
Haiping Zhu,Feng Tian,Ke Wu,Nazaraf Shah,Yan Chen,Yifu Ni,Xinhui Zhang,Kuo-Ming Chao,Qinghua Zheng +8 more
TL;DR: A new multi-constraint learning path recommendation algorithm based on knowledge map 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.
110
Meta-Heuristic Algorithms for Learning Path Recommender at MOOC
TL;DR: In this article, a multi-objective optimization model was proposed to generate an appropriate learning path for learners based on their background and job goals, which satisfies several learner criteria, such as the critical learning path, number of enrollments, learning duration, popularity, rating of previous learners and cost.
Microlearning in Diverse Contexts: A Bibliometric Analysis
Rajagopal Sankaranarayanan,Javier Leung,Victoria Abramenka-Lachheb,Grace Zhou Seo,Ahmed Lachheb +4 more
TL;DR: In this paper , a bibliometric study collected 208 relevant publications on microlearning from the Scopus database, published in diverse contexts, identifying four major themes in these publications, namely: (1) design of microlearning; (2) implementation of micro-learning as an instructional method strategy and an intervention; (3) evaluation of micro learning; and (4) the utilization of mobile devices for microlearning.
Use of Soft Computing Techniques for Recommender Systems: An Overview
Mohammed Wasid,Rashid Ali +1 more
- 01 Jan 2017
TL;DR: This paper presents a review of the field of recommendation systems that comprises soft computing approaches besides the typical user-item information used in most of the classical recommender systems, and provides the classification for each technique, their ability to address the challenges, and possible extensions to further improvement in the recommendation accuracy.
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References
Intelligent web-based learning system with personalized learning path guidance
TL;DR: Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.
413
A learning style classification mechanism for e-learning
TL;DR: Experimental results indicate that the proposed classification mechanism can effectively classify and identify students' learning styles and is implemented on an open-learning management system.
An attribute-based ant colony system for adaptive learning object recommendation
Yao Jung Yang,Chuni Wu +1 more
TL;DR: This paper proposed an attributes-based ant colony system (AACS) to help learners find an adaptive learning object more effectively and presented an attribute-based search mechanism to find adaptive learning objects effectively.
130
•Journal Article
A Usability Study for Promoting eContent in Higher Education
TL;DR: Although designing and publishing eContent is more complex than the printed version, eContent has a huge potential in education.
83
•Journal Article
XML-based Adaptation Framework for Psychological-driven E-learning Systems
TL;DR: Bitter-Rijpkema, M.E., Sloep, P.B., Jansen, D.B. (2003).
68