A Survey of Segmentation, Annotation, and Recommendation Techniques in Micro Learning for Next Generation of OER

Lin, Jiayin; Sun, Geng; Shen, Jun; Cui, Tingru; Yu, Ping; Xu, Dongming; Li, Li

doi:10.1109/cscwd.2019.8791891

Cited by 11 publications

(9 citation statements)

References 54 publications

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“…As a novel online learning style, micro learning aims to utilize users' fragmented spare time by helping them to carry out effective personalized learning activities [1][2][3]. Such online learning activities could be formal, informal, and non-formal [4], and online knowledge sharing is one way of non-formal learning.…”

Section: Introductionmentioning

confidence: 99%

“…Such online learning activities could be formal, informal, and non-formal [4], and online knowledge sharing is one way of non-formal learning. Quora, 1 Zhihu, 2 and Stackoverflow 3 are the most representative and successful online knowledge platforms, where users share knowledge by asking and answering questions. In the meantime, the online platforms continuously recommend questions and topics to the users based on their interests, background, and learning requirements.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service

Lin

Sun

Shen

et al. 2020

Lecture Notes in Computer Science

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Aims to provide flexible, effective and personalized online learning service, micro learning has gained wide attention in recent years as more people turn to use fragment time to grasp fragmented knowledge. Widely available online knowledge sharing is one of the most representative approaches to micro learning, and it is well accepted by online learners. However, information overload challenges such personalized online learning services. In this paper, we propose a deep cross attention recommendation model to provide online users with personalized resources based on users' profile and historical online behaviours. This model benefits from the deep neural network, feature crossing, and attention mechanism mutually. The experiment result showed that the proposed model outperformed the state-of-the-art baselines.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service

Lin

Sun

Shen

et al. 2020

Lecture Notes in Computer Science

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“…Depending on the learners' learning purpose and the credit they would obtain after the completion of a full course, this learning service could be formal, informal, or non-formal [1]. Hence, the volume of learning materials involved in this service and information generated associated with them would also conform with the standard definition of 'big data' [2]. However, operating and maintaining such online service poses the challenges in effectively managing dynamic and massive information from both user-side and resource-side.…”

Section: Introductionmentioning

confidence: 99%

“…In a broader sense, information extraction refers to the task of automatically analysing, locating, distilling, summarizing, and extracting useful information from massive unstructured multimedia documents. The workflow of the micro learning service can be realized via three important modules: nonmicro learning material segmentation, learning material annotation and learning material recommendation [2]. Based on the workflow of micro learning, the utility of information extraction technique could play a vital role in the preprocessing stage of each intelligent module mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Deep Sequence Labelling Model for Information Extraction in Micro Learning Service

Lin

Zhou

Sun

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant preprocessing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early evidence shows that it has satisfactory performance compared to conventional information extraction methods such as conditional random field and bi-directional recurrent neural network, for micro learning services.

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“…Serving as the extension of our previous literature review [5], in this paper, we surveyed a wide range of potential solutions for segmenting and annotating online learning materials; we also reviewed many novel recommendation strategies for e-learning scenarios. For the technical part of each micro learning processing stage, there exist many literature reviews respectively.…”

Section: Introductionmentioning

confidence: 99%

From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

et al. 2019

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The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people's daily activities. Such development together with the lifelong learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners' fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.

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A Survey of Segmentation, Annotation, and Recommendation Techniques in Micro Learning for Next Generation of OER

Cited by 11 publications

References 54 publications

Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service

Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service

Deep Sequence Labelling Model for Information Extraction in Micro Learning Service

From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

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