Evaluation study of eLGuide: A framework for adaptive e‐learning

Zafar, Aasim; Albidewi, Ibrahim

doi:10.1002/cae.21625

Cited by 14 publications

(9 citation statements)

References 40 publications

(59 reference statements)

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“…The students planned their learning activities based on student perception of course difficulty. This has been solved over with fuzzy knowledge based system, and the fuzzy logic inference system is used to evaluate the learner performance.…”

Section: Related Workmentioning

confidence: 99%

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Skill‐based education through fuzzy knowledge modeling for e‐learning

Gomathi

Rajamani

2017

Comp Applic In Engineering

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Nowadays outcome‐based methods are adapted in e‐learning system to meet the need of the novel development of e‐learning systems for improved web‐based retrieval results. Typically, knowledge retrieval process is denoted by the production rule, frame, semantic network, fuzzy logic, predicate logic, and group of skill concept. To get the optimized result in proposed skill‐based e‐learning, the fuzzy knowledge model is applied. In knowledge retrieval, the fuzzy membership value of the knowledge and the combinations of framed rules are used to acquire the knowledge. The fuzzy techniques are adapted for analyzing the retrieved knowledge concept of individual skills like Inadequate (Ia), Adequate (A), Value added adequate (Vaa), and Integrated skill (I) and in fuzzy inference system in skill‐based education provides a decision about the learner community skill set and it promotes the excellence skill through the delivery of the suitable courses to the learners instead of supplying common courses to different skilled persons. In the existing knowledge modeling methods known as the Knowledge Capturing and Modeling, concept map‐based knowledge modeling confirm the learners to have known or unknown domain concept. Further, many of the researchers present on the analysis of the learner performances, behavior, learning environment, etc. The proposed paper investigate the individual skill abilities and it is suggested a set of courses in adaptive curriculum and syllabus to the learner and also it adapts andragogy in skill‐based education, to model the fuzzy knowledge, the fuzzy membership function and rules are used.

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Section: Related Workmentioning

confidence: 99%

“…This system provides the best appropriate learning styles of the engineering student and teaching faculties of institution. Zafar and Albidewi developed an intelligent tutoring technique to personalize an individual user in the course material and generate guidance to students.…”

Section: Related Workmentioning

confidence: 99%

Skill‐based education through fuzzy knowledge modeling for e‐learning

Gomathi

Rajamani

2017

Comp Applic In Engineering

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“…Such systems are particularly important for adult and lifelong learners who prefer self‐guided learning [32,48]. Adaptive systems generally provide the learners with an intelligent tutoring and adaptive testing [40,55]. They also allow them to create personalized learning materials and scenarios in accordance to their individual skills and objectives [6,33].…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence to solve the curriculum sequencing problem

Menaï

Alhunitah

Al-Salman

2018

Comp Applic In Engineering

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The curriculum sequencing (CS) problem asks to find a personalized optimal path for a learner in an e‐learning environment. Solving this problem contributes to developing adaptive e‐learning systems which provide personalized learning paths compatible with the individual profiles of the learners. The solutions to large CS instances can be only approximated as this problem is NP‐hard. In this paper, we formulate the CS problem as a constraint satisfaction problem and investigate swarm intelligence methods to solve it, including a newly introduced method called SwarmRW and the widely used ant colony system (ACS). In addition, we introduce two variants of SwarmRW, called SwarmRW_rnd and SwarmRW_inc. The performance results obtained on real data show that SwarmRW_rnd achieved the best results in terms of maximizing the number of satisfied constraints. However, SwarmRW_inc demonstrated the best trade‐off between the solution quality and the running‐time to obtain it.

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“…Many researchers have used fuzzy logic to determine and update the student's knowledge level model for each domain concept [25], [26], [27], [28], and to personalise and recommend a learning path to be followed by the students based on their learning evaluation [9], [29]. In particular, [25] have used fuzzy logic integrated into the student model, and four fuzzy sets for defining student knowledge of a domain concept have been identified as: Unknown (Un): the degree of progress in the domain concept is from 0% to 60%.…”

Section: Introductionmentioning

confidence: 99%

“…However, they have suggested making the PeRSIVA framework more valid; it has to be examined in various learning circumstances, such as other courses types; data structures, database systems. [29] have developed an educational application module fuzzy knowledge state definer (FuzKSD) to implement and evaluate a webbased education that performs individualised instruction on the field of programming languages (C Programming language). The system's evaluation showed that the incorporation of fuzzy sets with overlay and stereotype models significantly provides to the adaptation of the learning process to the learning movement of each learner.…”

Section: Introductionmentioning

confidence: 99%

Concept-based and Fuzzy Adaptive E-learning

Duhayyim

Newbury

2018

Proceedings of the 2018 the 3rd International Conference on Information and Education Innovations - ICIEI 2018

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This study aims to test an effective adaptive e-learning system that uses a coloured concept map to show the learner's knowledge level for each concept in the topic. A fuzzy logic system is used to evaluate the learner's knowledge level for each concept in the domain and produce a ranked concept list of learning materials to address weaknesses in the learner's understanding. This system will obtain information on a learner's understanding of concepts by an initial pre-test before the system is used for learning and a posttest after using the learning system. A fuzzy logic system is used to produce a weighted concept map during the learning process. The aim of this research is to prove that such a proposed novel adapted e-learning system will enhance a learner's performance and understanding. In addition, this research aims to increase participants' overall learning level and effectiveness by providing a coloured concept map of understanding followed by a ranked concepts list of learning materials.

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Evaluation study of eLGuide: A framework for adaptive e‐learning

Cited by 14 publications

References 40 publications

Skill‐based education through fuzzy knowledge modeling for e‐learning

Skill‐based education through fuzzy knowledge modeling for e‐learning

Swarm intelligence to solve the curriculum sequencing problem

Concept-based and Fuzzy Adaptive E-learning

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