Building a Smart E-Portfolio Platform for Optimal E-Learning Objects Acquisition

Ke, Chih-Kun; Liu, Kai-Ping; Chen, Wen‐Chin

doi:10.1155/2013/896027

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…courses) and comprehensive tasks (e.g. license testing, practical training and science research tasks) (Ke et al, 2013).…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

The effects of perceived quality differences between the traditional classroom and online distance learning on student satisfaction: evidence from COVID-19 pandemic in Indonesia

Istijanto¹

2021

QAE

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Purpose This study aims to investigate the aspects of perceived quality differences that influence student satisfaction when the mode of learning is changed from the traditional classroom to online distance learning because of the COVID-19 pandemic. Design/methodology/approach This study used an online survey for data collection by distributing online questionnaires to students at a private university in Indonesia. Convenience sampling was applied as the sample method. A total of 253 valid questionnaires were acquired, and all items were recorded on the seven-point comparative scale. Exploratory factor analysis was adopted to identify the dimensions of perceived quality. Then, multiple regression analysis was used to examine the impacts of these dimensions on student satisfaction. Findings Four dimensions of perceived quality differences were identified, namely, the perceived quality of the campus facilities, learning process, teaching staff and administrative staff. Regression analysis results proved that the perceived differences in the quality dimensions between the traditional classroom and online distance learning had positive and significant effects on student satisfaction. Research limitations/implications As a result of using a convenient sample, this study exhibited a limited sample and context. Therefore, the generalizability of these findings is limited. Future research can use a stratified sample with more diverse categories of respondents. Practical implications The findings of this study will help universities that plan to change the mode of learning from traditional classroom to online distance learning. Higher education institutions can gain a deep understanding of these perceived quality attributes that have significant impacts on student satisfaction. Originality/value To the author’s best knowledge, this study is the first to address the effect of changing modes of learning from traditional classroom to online distance learning because of the COVID-19 pandemic on student satisfaction in the Indonesian context.

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“…courses) and comprehensive tasks (e.g. license testing, practical training and science research tasks) (Ke et al, 2013).…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

The effects of perceived quality differences between the traditional classroom and online distance learning on student satisfaction: evidence from COVID-19 pandemic in Indonesia

Istijanto¹

2021

QAE

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“…The Moodle LS environment [9] employs a graph-based technique for educators to search and retrieve personalized LOs in LORs, based on metadata describing learners' personal attributes. The authors of [10] employ utility functions on learning context and fuzzy weighted multicriteria decision analysis to highlight the highest quality LOs out of a large set of e-Learning LOs. The work of [11] proposes a way to find the optimal path to travel across a hierarchical graph of nodeslessons and edges-activities, based on the users' learning styles, via an ant-colony optimization algorithm.…”

Section: A Adaptive User-centric Losmentioning

confidence: 99%

Modelling learning experiences in adaptive multi-agent learning environments

Tsatsou¹,

Vretos²,

Daras³

2017

2017 9th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games)

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In next generation technology-enhanced learning environments, intelligent educational systems can benefit from tapping into multi-agent, adaptive, gamified learning experiences, which transform the traditional instructional paradigm from classroom-based learning to personalised learning in any setting, whether collective or individual. Such settings enable learning targeted to each individual's learning styles and needs, through the use of autonomous technological agents as actuators of the learning process. Learning components which will respond to the needs of such an educational framework should provide capabilities for adaptive, affective and interactive learning, automatic feedback and automatic assessment of the learners' behavioural state. A novel methodology is proposed to model such components, which focuses on the representation and management of learning objects (LOs) for any educational domain, any type of learner and learning style and any learning methodology, while fostering non-linearity in the educational process. This methodology is supported by a strategy for modelling and adapting re-usable learning objectives, coupled with an ontology that enables scalable and personalized decision-making over learning activities on autonomous devices, enabling dynamic modularisation of learning material during the learning process.

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