A Novel Resource Deployment Approach to Mobile Microlearning: From Energy-Saving Perspective

Zheng, Ruijuan; Zhu, Junlong; Zhang, Mingchuan; Liu, Ruoshui; Wu, Qingtao; Yang, Li

doi:10.1155/2019/7430860

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…• Nursing education (Bian et al, 2014) • Medical training and health professions (Simons, Foerster, Bruck, Motiwalla and Jonker, 2015) • Language training (Fang, 2018) • Engineering topics (Zheng et al, 2019) • Programming skills (Skalka and Drlik, 2019) Research showed that microlearning helps improve students' motivation, participation in instruction and performance. Jahnke et al (2020) provide an extensive overview of microlearning research.…”

Section: Types Of Microlearningmentioning

confidence: 99%

Microlearning in the Education of Future Teachers: Monitoring and Evaluating Students’ Activity in a Microlearning Course

Javorcik¹,

Kostolányová²,

Havlaskova³

2023

EJEL

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Microlearning has become a promising modern and effective approach to the education of various groups in recent years. In order to be able to further develop microlearning and consider student individualities it is necessary to map their passage through a course in detail. The article presents the conclusions of a research carried out at the Faculty of Education of the University of Ostrava. The aim of the research was to find out whether there are differences in approaches of studying a microlearning course. A microlearning course focused on teaching future teachers was created for the purposes of the research. The aim of the course was to present to students the possibilities of using digital technologies in the educational process. The research was conducted in the winter semester of the academic year 2021/2022. A total of 378 students participated in the study in the first phase (precourse survey) and subsequently 156 students in the second phase (analysis of course participants' behavior). Student activity was monitored during the study through learning analytics tools. Time of study, the number of realized events, the number of registrations, etc. were recorded for each student with these tools. The obtained data were analyzed using cluster analysis. Total of six different approaches that led to the successful completion of a microlearning course were described based on this analysis. The approaches can be used to describe a successful strategy to go through a microlearning course including the extreme ones. An interesting fact is that the choice of strategy is not influenced by the student's gender. The only parameter where significant differences were found was the number of days to finish the microlearning course. In addition, the article describes the behavior of students in the course, the types of learning materials, devices from which they logged in the course and list of the most used course components. This part of the data was recorded via heatmaps. A detailed description of students' study strategies within microlearning courses can improve the effectiveness of microlearning also in connection with the personalized passage and thus improve the quality and efficiency of the educational process of future teachers.

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Section: Types Of Microlearningmentioning

confidence: 99%

Microlearning in the Education of Future Teachers: Monitoring and Evaluating Students’ Activity in a Microlearning Course

Javorcik¹,

Kostolányová²,

Havlaskova³

2023

EJEL

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“…Mobile devices (MDs) have become extremely popular in recent years due to their mobility and convenience [1,3]. Meanwhile, the functionality of the application for MDs becomes increasingly powerful [3], which leads to lack of local resources of MDs, such as computing resources, storage, and energy [4,5]. To this end, computing offloading for MDs has emerged.…”

Section: Introductionmentioning

confidence: 99%

Private Computing Offloading in Edge Cloud via Collaborative Online Learning

Wang

Yang

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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Computing offloading based on mobile edge computing (MEC) for mobile devices (MDs) has received great attentions in recent years. Strategy selection is an extremely important part of computing offloading, so how to make an optimal decision quickly and accurately during the computing offloading is a difficult point. Furthermore, MDs are likely to leak personal privacy when interacting with edge cloud, and there is also an issue about commercial privacy leakage between different cloud service suppliers. In this paper, we propose the privacy-protected edge cloud computing offloading (EPCO) algorithm based on online learning to improve the efficiency of computing offloading while ensuring the privacy of system users. Simultaneously, EPCO also supports different MDs customize their privacy level. We prove that adding privacy protection mechanism is almost no effect on the convergence of the algorithm. The simulation results validate our conclusion using a real-world dataset.

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“…Researchers are enthusiastic to determine the potential factors that drive learning and to maximize the positive influence of micro learning on the learning experience. The deployment of rich, colorful and multi‐mediated micro learning resources has been initialized alongside the large‐scale development of cloud‐based services, which aims to provide mobile learners affordable, efficient, continuous and stable micro learning service (Zheng et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

Refinement and augmentation for data in micro open learning activities with an evolutionary rule generator

Sun¹,

Lin²,

Shen³

et al. 2020

Brit J Educational Tech

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Improving both the quantity and quality of existing data are placed at the center of research for adaptive micro open learning. To cover this research gap, our work targets on the current scarcity of both data and rules that represent open learning activities. An evolutionary rule generator is constructed, which consists of an outer loop and an inner loop. The outer loop runs a genetic algorithm (GA) to produce association rules that can be effective in the micro open learning scenario from a small amount of available data sources; while the inner loop optimizes generated candidates by taking into account both rare and negative association rules (NARs). These optimized rules are further applied in refining and augmenting data denoting learners’ behaviors in open learning into a low‐dimensional, descriptive and interpretable form. The performance of rule discovery and data processing have been empirically evaluated using genuine open learning data.

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A Novel Resource Deployment Approach to Mobile Microlearning: From Energy-Saving Perspective

Cited by 8 publications

References 32 publications

Microlearning in the Education of Future Teachers: Monitoring and Evaluating Students’ Activity in a Microlearning Course

Microlearning in the Education of Future Teachers: Monitoring and Evaluating Students’ Activity in a Microlearning Course

Private Computing Offloading in Edge Cloud via Collaborative Online Learning

Refinement and augmentation for data in micro open learning activities with an evolutionary rule generator

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