Developing an adaptive e-learning system for learning excel

Wu, Chunhui; Chen, Ta-Cheng; Yan, Yih-Her; Lee, Chun-Feng

doi:10.1109/icasi.2017.7988583

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…Studied Learner characteristics Theory (Almohammadi & Hagras, 2013b) Learner knowledge Cognitivism (Deeb et al, 2014) Learning style Cognitivism (Fenza et al, 2017) Learner knowledge Cognitivism (Kolekar et al, 2010) Learning style Cognitivism (Rajendran et al, 2018) The learner's Affective states Cognitivism (Malpani, 2011) The Learner's Prior knowledge and current knowledge level. This was done by measuring the ability of the learner to answer quizzes correctly Cognitivism (Sabourin et al, 2011) Learner effect Behaviorism (C. H. Wu et al, 2017) Learner knowledge Cognitivism (Alshammari et al, 2015) Learning style Cognitivism (Whitehill & Movellan, 2018) Learner knowledge Cognitivism (Hwang et al, 2013) Learning style Cognitivism (Yang et al, 2016) Both…”

Section: Authorsmentioning

confidence: 99%

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

Anne¹,

Maina²

2021

OJIT

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We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

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Section: Authorsmentioning

confidence: 99%

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

Anne¹,

Maina²

2021

OJIT

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“…The effectiveness of the use of adapted online training courses depends on the feedback, on the current learning outcomes of the proposed content [25,26,14]. Andrew Thomas Bimba, Norisma Idris, Ahmed Al-Hunaiyyan, Rohana Binti Mahmud and Nor Liyana Bt Mohd Shuib considers different feedback options in an adapted educational environment based on dialogue, intelligent e-learning systems and adaptive hypermedia systems [27].…”

Section: Introductionmentioning

confidence: 99%

Approaches to the choice of tools for adaptive learning based on highlighted selection criteria

et al. 2021

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The article substantiates the relevance of adaptive learning of students in the modern information society, reveals the essence of such concepts as “adaptability” and “adaptive learning system”. It is determined that a necessary condition for adaptive education is the criterion of an adaptive learning environment that provides opportunities for advanced education, development of key competencies, formation of a flexible personality that is able to respond to different changes, effectively solve different problems and achieve results. The authors focus on the technical aspect of adaptive learning. Different classifications of adaptability are analyzed. The approach to the choice of adaptive learning tools based on the characteristics of the product quality model stated by the standard ISO / IEC 25010 is described. The following criteria for the selecting adaptive learning tools are functional compliance, compatibility, practicality, and support. By means of expert assessment method there were identified and selected the most important tools of adaptive learning, namely: Acrobatiq, Fishtree, Knewton (now Wiliy), Lumen, Realize it, Smart Sparrow (now Pearson). Comparative tables for each of the selected tools of adaptive learning according to the indicators of certain criteria are given.

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Research Trends in Adaptive Online Learning: Systematic Literature Review (2011–2020)

et al. 2022

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Developing an adaptive e-learning system for learning excel

Cited by 6 publications

References 6 publications

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

Approaches to the choice of tools for adaptive learning based on highlighted selection criteria

Research Trends in Adaptive Online Learning: Systematic Literature Review (2011–2020)

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