Dynamic learner profiling and automatic learner classification for adaptive e-learning environment

Premlatha, K. R.; Dharani, B.; Geetha, T. V.

doi:10.1080/10494820.2014.948459

Cited by 51 publications

(19 citation statements)

References 15 publications

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“…Lange et al [26] analyzed the profiles of open learning students and investigated the associations between characteristics and performance. Premlatha et al [27] classified learners based on their performance and knowledge level by analyzing the learner profiles. Sun et al [28] explored the personal characteristics and mobile usage behavior in terms of mobile learning.…”

Section: Personality and Learningmentioning

confidence: 99%

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An Intelligent Course Decision Assistant by Mining and Filtering Learners’ Personality Patterns

Su¹,

Liao²,

Chen³

2019

Applied Sciences

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For a student, determining how to choose from a set of courses is an important issue prior to learning. An appropriate learning guide can direct students toward an area of interest. The learning results produced by the student in this case are superior due to their strong interest in the subject matter. Although a number of methods have been proposed to address this issue, the effectiveness remains unsatisfactory. To this end, we created an effective system, called the personality-driven course decision assistant, to help students determine the courses they should select by mining and filtering learners’ personality patterns. For learner pattern mining, the relationships between the students’ learning results and the referred personalities are discovered to provide the learners with valuable information before learning. For filtering learner personality patterns, students with similar personality patterns are filtered to predict the potential learning results. Through the actual system, a number of subjective and objective evaluations were conducted, and the evaluation results reveal that the proposed system is highly effective and reliable.

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Section: Personality and Learningmentioning

confidence: 99%

“…In detail, the proposed system can provide an effective course selection support. Further, the high positive ratings for the level 3 questions give an evidence that the data mining technique based on References [5,37] works well for bridging personalities, preferences and achievements [9,23,24,27].…”

mentioning

confidence: 99%

An Intelligent Course Decision Assistant by Mining and Filtering Learners’ Personality Patterns

Su¹,

Liao²,

Chen³

2019

Applied Sciences

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“…Supporting this, Chiu (2008) indicated that identifying learner requirements dynamically is an important and missing part for most conventional e-learning systems. Additionally, Premlatha, Dharani, and Geetha (2016) pointed out that numerous existing e-learning systems have problems with offering…”

Section: E-learning Systemmentioning

confidence: 99%

An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory

Chen

2017

EURASIA J MATH SCI T

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Adaptive learning for individual learners has recently become popular in education. This study aims to fill the void in the existing literature by building an adaptive elearning system with self-assessment rubrics based on the dynamic scaffolding theory in response to different student needs. Meanwhile, the purpose of this study is to explore the effectiveness of using adaptive e-learning with dynamic scaffoldings and rubrics in fostering students' learning outcomes. An experimental design was conducted to evaluate learning effectiveness and learning satisfaction in the Excel (spreadsheet) of the course for using the developed adaptive e-learning system. Sixty undergraduate students from a technology university in central Taiwan participated in this experimental study and executed a pretest and a posttest. Research results revealed that the developed adaptive e-learning system can effectively support students with personalized learning materials and successfully helps students acquired knowledge and develop cognitive abilities. The results recommend that teachers could employ rubrics as a self-assessment tool for supporting students with dynamic scaffoldings to conduct a learner-centered e-learning environment. Additionally, the lack of generalizability is clearly a limitation of the present data due to a few participants. Finally, future research direction of this study was also discussed.

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“…Introduction A MOOC, which is a good example of open e-learning, is meant to serve a significant number of learners from a wide range of cultures and backgrounds; therefore, it is essential that it is equipped with personalisation attributes in order to meet a wide range of individual needs and preferences to some level for any field [7] and particularly for e-learning [8]. Personalisation in web-based interactive environments has been considered a key element in enabling effective and efficient user engagement [9].…”

Section: Personalisationmentioning

confidence: 99%

“…In e-learning, personalisation applies to the adaptive environment for the learner [7]-i.e. adaptive collaboration, adaptive content delivery and adaptive resources [10].…”

Section: Personalisationmentioning

confidence: 99%

A Literature Review of Personalisation, Search, and Navigation Methods in Open e-Learning

Al-Shaikhli¹,

Courtenage²

2018

IJIET

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Abstract-Learning engagement has been a significant field of research during the past decades, especially in terms of e-learning. Personalisation, search, and navigational methods have been reported as potential approaches to supporting learners on e-learning platforms. In this review, we discuss previous research on the above-mentioned fields and recent results that e-learner providers can benefit from.

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Dynamic learner profiling and automatic learner classification for adaptive e-learning environment

Cited by 51 publications

References 15 publications

An Intelligent Course Decision Assistant by Mining and Filtering Learners’ Personality Patterns

An Intelligent Course Decision Assistant by Mining and Filtering Learners’ Personality Patterns

An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory

A Literature Review of Personalisation, Search, and Navigation Methods in Open e-Learning

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