Preliminary Explorations on the Statistical Profiles of Highly-Rated Learning Objects

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.2.4.158 ◽

2018 ◽

Vol 2 (4) ◽

pp. 271 ◽

Cited By ~ 3

Author(s):

Outmane Bourkoukou ◽

Essaid El Bachari

Keyword(s):

Learning Style ◽

Recommender System ◽

Adaptive Learning ◽

Learning Objects ◽

Research Field ◽

Learning System ◽

K Nearest Neighbors ◽

E Learning ◽

Hybrid Recommender System ◽

Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

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Evaluasi Aksesibilitas Learning Object Berdasarkan Web Content Accessibility Guidelines 2.0

Jurnal ULTIMATICS ◽

10.31937/ti.v9i2.602 ◽

2017 ◽

Vol 9 (2) ◽

pp. 67-71

Author(s):

Herru Darmadi ◽

Yan Fi ◽

Hady Pranoto

Keyword(s):

Learning Objects ◽

Authoring Tool ◽

Learning Object ◽

Web Content ◽

Web Browser ◽

E Learning ◽

Index Terms ◽

Accessibility Guidelines ◽

Content Accessibility

Learning Object (LO) is a representation of interactive content that are used to enrich e-learning activities. The goals of this case study were to evaluate accessibility and compatibility factors from learning objects that were produced by using BINUS E-learning Authoring Tool. Data were compiled by using experiment to 30 learning objects by using stratified random sampling from seven faculties in undergraduate program. Data were analyzed using accessibility and compatibility tests based on Web Content Accessibility Guidelines 2.0 Level A. Results of the analysis for accessibility and compatibility tests of Learning Objects was 90% better than average. The result shows that learning objects is fully compatible with major web browser. This paper also presents five accessibility problems found during the test and provide recommendation to overcome the related problems. It can be concluded that the learning objects that were produced using BINUS E-learning Authoring Tool have a high compatibility, with minor accessibility problems. Learning objects with a good accessibility and compatibility will be beneficial to all learner with or without disabilities during their learning process. Index Terms—accessibility, compatibility, HTML, learning object, WCAG2.0, web

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Proposal of an instrument to evaluate interaction spaces in a VLE

Journal of Teaching and Educational Research ◽

10.35429/jter.2019.15.5.1.13 ◽

2019 ◽

pp. 1-13

Author(s):

Rubí Estela Morales-Salas ◽

Daniel Montes-Ponce

Keyword(s):

Learning Environment ◽

Design Space ◽

Collaborative Work ◽

Virtual Learning ◽

Interaction Space ◽

Learning Objects ◽

Traditional Education ◽

Virtual Learning Environment ◽

Discussion Forums ◽

Exhibition Space

A virtual learning environment is conceived as an interaction space that ease the realization of mediated activities by technology, in this case the internet; besides using multimedia materials, learning objects, social networks, among others; which have changed imminently the traditional education. In this article an instrument is proposed in a checklist format, to evaluate any platform that has interaction spaces such as a Virtual Learning Environment, in this case responding to four spaces or general indicators: information Space, Mediation / Interaction Space, Instructional Design Space and Exhibition Space. Criteria are used according to the interactions and activities carried out by the consultant and virtual student. These, in turn, come up from the analysis and interaction of the advisers achieved in the discussion forums and portfolio activities through collaborative work. It was situated as a qualitative research, with a descriptive nature since it is not limited to data collection only, but also it refers and analyzes the interaction of the advisers achieved in the discussion forums and portfolio activities through the collaborative work of the workshop course "Virtual Learning Environments" developed in a virtual learning environment.

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An Architecture of a Distributed Archive for Re-Useable Learning Objects

SSRN Electronic Journal ◽

10.2139/ssrn.2286101 ◽

2004 ◽

Author(s):

Daniel Tan ◽

Fazilah Mohamed Ismail ◽

Tony Chan ◽

Ravi S. Sharma

Keyword(s):

Learning Objects

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Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2213111607666190215120017 ◽

2020 ◽

Vol 13 (2) ◽

pp. 153-164

Author(s):

Narina Thakur ◽

Deepti Mehrotra ◽

Abhay Bansal ◽

Manju Bala

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Time Window ◽

Optimal Solution ◽

Similarity Score ◽

Learning Objects ◽

Retrieval Algorithm ◽

Support Vector ◽

Computationally Efficient ◽

User Query

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

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