Keywords extraction for automatic indexing of e-learning resources

Hendez, Marwa; Achour, Hadhémi

doi:10.1109/wscar.2014.6916796

Cited by 7 publications

(10 citation statements)

References 3 publications

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“…As discussed in many studies [18][19][20][21], proper annotation, indexing, or tagging are essential for searching, retrieving, aggregation, recommendation, and reusing of resources. According to [18], the annotation is essential for the accessibility as well as the sharing and reusing of educational resources; the non-indexed resource cannot be found and therefore, is hard to be reused [22]. Generally, annotation strategies can be classified into three categories: ontology based, model based, and crowd-wisdom based.…”

Section: Annotationmentioning

confidence: 99%

“…Such pattern could offer valuable information for identifying the key terms during the annotation process. As discussed in [18], learning resources have a specific structure, which characterizes them from all other types of online resources. For example, many lectures follow specific didactic patterns, such as starting with an introduction, consists of serval subtopics, and ending with a conclusion.…”

Section: Model-based Annotation Strategymentioning

confidence: 99%

“…The authors argued that the organization of the video content could be modelled by a state model, like the Hidden Markov Model (HMM).In many actual cases, a lecture video did start with a course outline; and for each topic, it was more likely it started with a definition and less likely with a discussion. Similarly, based on the structure information of learning materials more weight was put on the terms extracted from the titles and sub-titles [16,18,32].…”

Section: Model-based Annotation Strategymentioning

confidence: 99%

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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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Section: Annotationmentioning

confidence: 99%

Section: Model-based Annotation Strategymentioning

confidence: 99%

Section: Model-based Annotation Strategymentioning

confidence: 99%

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

et al. 2019

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“…After the segmentation process, an annotation step is required to make learning resources both machine understandable and human understandable, to overcome the lack of interpretability of the micro learning resources. As discussed in many studies [3,4], proper annotation, indexing, or tagging are essential for the retrieval, recommendation, and reuse of resources.…”

Section: Pre-processing Modelmentioning

confidence: 99%

“…Segmentation and annotation are mainly driven by the content information of the learning materials; hence, the readiness of the research data is vital for the research on segmentation and annotation. As many learning materials are in video format, many studies of annotation and segmentation heavily rely on transforming the image and audio information to textual format [3,8,9]. Researchers extract textual metadata by applying Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) on the lecture videos [8].…”

Section: Annotation and Segmentationmentioning

confidence: 99%

Towards the Readiness of Learning Analytics Data for Micro Learning

Lin

Sun

Shen

et al. 2019

Services Computing – SCC 2019

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With the development of data mining and machine learning techniques, data-driven based technologyenhanced learning (TEL) has drawn wider attention. Researchers aim to use established or novel computational methods to solve educational problems in the 'big data' era. However, the readiness of data appears to be the bottleneck of the TEL development and very little research focuses on investigating the data scarcity and inappropriateness in the TEL research. This paper is investigating an emerging research topic in the TEL domain, namely micro learning. Micro learning consists of various technical themes that have been widely studied in the TEL research field. In this paper, we firstly propose a micro learning system, which includes recommendation, segmentation, annotation, and several learning-related prediction and analysis modules. For each module of the system, this paper reviews representative literature and discusses the data sources used in these studies to pinpoint their current problems and shortcomings, which might be debacles for more effective research outcomes. Accordingly, the data requirements and challenges for learning analytics in micro learning are also investigated. From a research contribution perspective, this paper serves as a basis to depict and understand the current status of the readiness of data sources for the research of micro learning.

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