MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning

Sun, Geng; Cui, Tingru; Yong, Jianming; Shen, Jun; Chen, Jinjun

doi:10.1109/tsc.2015.2473854

Cited by 61 publications

(37 citation statements)

References 32 publications

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“…In other words, several ontologies are drawn at first, followed by 'data processing' work. We attempt to combine the pattern and rule discovery processes of micro learning with a survey of the education literature to produce features that could affect the learning experience and outcomes in a mobile environment [21]. In detail, 'data processing' work involves all operations on data, from the very beginning to the end, such as entity extractions, relationship extractions, resolution disambiguation and so on.…”

Section: Research Problem Identification and Designmentioning

confidence: 99%

“…A chunk is the smallest unit in the micro learning settings-normally a finely-cut piece of an OER from its provider-with an apparently shorter time length (preferably less than 15 min) than its original shape. It can be a mini concept or knowledge point, tinier than what teachers used to deliver; or it can be a cut of course video or lecture notes; or course settings delivered along with a concept, such as assessment, task, reading material and so on [21].…”

Section: Augmented Micro Oer Ontologymentioning

confidence: 99%

“…In our prior work [21], we have discussed the key issues that might cause distraction in micro open learning, which generally comes from two sides, the social side and the environmental side.…”

Section: Demographic Classificationmentioning

confidence: 99%

“…We have reported on the architecture and technical details of MLaaS in prior studies [19,21]. A comprehensive description is beyond the scope of this paper.…”

mentioning

confidence: 99%

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Towards Massive Data and Sparse Data in Adaptive Micro Open Educational Resource Recommendation: A Study on Semantic Knowledge Base Construction and Cold Start Problem

et al. 2017

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Abstract:Micro Learning through open educational resources (OERs) is becoming increasingly popular. However, adaptive micro learning support remains inadequate by current OER platforms. To address this, our smart system, Micro Learning as a Service (MLaaS), aims to deliver personalized OER with micro learning to satisfy their real-time needs. In this paper, we focus on constructing a knowledge base to support the decision-making process of MLaaS. MLaas is built using a top-down approach. A conceptual graph-based ontology construction is first developed. An educational data mining and learning analytic strategy is then proposed for the data level. The learning resource adaptation still requires learners' historical information. To compensate for the absence of this information initially (aka 'cold start'), we set up a predictive ontology-based mechanism. As the first resource is delivered to the beginning of a learner's learning journey, the micro OER recommendation is also optimized using a tailored heuristic.

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Section: Research Problem Identification and Designmentioning

confidence: 99%

Section: Augmented Micro Oer Ontologymentioning

confidence: 99%

Section: Demographic Classificationmentioning

confidence: 99%

“…We have reported on the architecture and technical details of MLaaS in prior studies [19,21]. A comprehensive description is beyond the scope of this paper.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Towards Massive Data and Sparse Data in Adaptive Micro Open Educational Resource Recommendation: A Study on Semantic Knowledge Base Construction and Cold Start Problem

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…A chunk is the smallest unit in the micro learning settings, normally a fine-cut piece of an OER from its provider, and it has an apparently shorter time length (preferably less than 15 mins) than its original shape. It can be a mini concept or knowledge point, tinier than what the teachers used to deliver; or it can be a cut of course video or lecture notes, or a course settings come along with a concept, such as assessment, task, reading material and so on [16].…”

Section: B Augmented Micro Oer Ontologymentioning

confidence: 99%

Ontological Learner Profile Identification for Cold Start Problem in Micro Learning Resources Delivery

Sun

Cui

Shen

et al. 2017

2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)

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Open learning is a rising trend in the educational sector and it attracts millions of learners to be engaged to enjoy massive latest and free open education resources (OERs). Through the use of mobile devices, open learning is often carried out in a micro learning mode, where each unit of learning activity is commonly shorter than 15 minutes. Learners are often at a loss in the process of choosing OER leading to their long term objectives and short term demands. Our pilot work, namely MLaaS, proposed a smart system to deliver personalized OER with micro learning to satisfy their real-time needs, while its decision-making process is scarcely supported due to the lack of historical data. Inspired by this, MLaaS now embeds a new solution to tackle the cold start problem, by opening up a brand new profile for each learner and delivering them the first resources in their fresh start learning journey. In this paper, we also propose an ontology-based mechanism for learning prediction and recommendation.

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A collaborative and open solution for large‐scale online learning

Zhou

Sun

Zhou

et al. 2018

Comp Applic In Engineering

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Collaborative Open Online Courses (COOC), designed as the enhanced variance of Massive Open Online Courses (MOOC), inherently combines the advantages of high‐quality online resources and face‐to‐face classroom teaching. It stimulates the collaboration and improvement of courses between educators and learners, by realizing the innovation for dynamic and real‐time education process, fostering professional skill acquisition and development as well. In this paper, we propose the novel concept and architectural design of COOC that provides the online course, courseware, and online experimental platform, built on top of third party's open source platforms. The preparation of courses is based on GitHub that facilitates multipoint‐to‐multipoint online courses and concurrent compilation of textbook based on GitBook, by providing open source textbooks and rapid updates as well as remote operations through online experimental platforms. The major differences between MOOC and COOC include decentralized platform and adoption of concepts through crowdsourcing, where instructors, professionals, and learners are able to set up and modify the courses independently and concurrently, empowering the course's construction based on the collaborative, open and shared textbooks, and online experimental platform. Users, including both teachers and learners, are also able to browse, download, and make use of courses in this platform. With embedded intellectual property and quality control mechanisms, such as Creative Commons (CC) licenses, COOC platform can generate and offer high‐quality courses and textbooks of emerging knowledge rapidly through consistent updates by professionals, students, and elites in related domains. The proposed design has been tested at trial Chinese universities and initial feedback from instructors and students are very positive. Insights on techniques and related information are provided, to best adapt to any vocational education level, subject matter, or courseware provider.

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MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning

Cited by 61 publications

References 32 publications

Towards Massive Data and Sparse Data in Adaptive Micro Open Educational Resource Recommendation: A Study on Semantic Knowledge Base Construction and Cold Start Problem

Towards Massive Data and Sparse Data in Adaptive Micro Open Educational Resource Recommendation: A Study on Semantic Knowledge Base Construction and Cold Start Problem

Ontological Learner Profile Identification for Cold Start Problem in Micro Learning Resources Delivery

A collaborative and open solution for large‐scale online learning

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