A novel cluster-based approach for keyphrase extraction from MOOC video lectures

Albahr, Abdulaziz; Che, Dunren; Albahar, Marwan Ali

doi:10.1007/s10115-021-01568-2

Cited by 5 publications

(9 citation statements)

References 30 publications

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“…Pan et al extended the pre-trained embedding model by adding a graph propagation algorithm to capture relationships between words and courses, enabling domain concepts to be identified within a course [18]. Albahr et al used the skip-gram model with the Wikipedia corpus to ascertain word embedding vectors for concept extraction in massive open online courses [19]. To address noisy and incomplete annotations during highquality knowledgeable concept extraction from these types of courses, Lu et al developed a three-stage framework [22].…”

Section: Rule-based Methodsmentioning

confidence: 99%

“…Its utility was tested by taking five well-known unsupervised learning algorithms as baselines: TF-IDF, TextRank, K-means, isolation forest, and one-class support vector machine (SVM). Both TF-IDF and TextRank served as a heuristic to determine candidate concepts' domain relevance; these have been widely used for educational concept extraction [17,19,29]. Different from these two methods, we fit seed concepts to the K-means, one-class SVM, or isolation forest approaches to promote novelty detection.…”

Section: Baselines and Evaluation Metricsmentioning

confidence: 99%

“…Concepts also tend to appear in multiple subject areas (e.g., in deep learning and information technology). Rich linguistic understanding and semantic computation [18,19] are needed to extract educational concepts effectively. Thirdly, an obstacle within this type of concept extraction entails the scarcity of extensive annotated data.…”

Section: Introductionmentioning

confidence: 99%

“…The unsupervised concept extraction methods that researchers typically use mostly rely on word frequency statistics, which can easily overlook low-frequency educational concepts and fail to capture underlying semantic information [16,20,21]. Fortunately, due to the increasing prevalence of pre-trained models, numerous scholars have started to use word embedding techniques for unsupervised concept extraction [18,19,22]. Even so, it is not desirable to directly incorporate pre-trained models into unsupervised concept extraction tasks: these models are trained on extensive corpora and may not cover every subject-specific concept.…”

Section: Introductionmentioning

confidence: 99%

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WERECE: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement

Huang,

Ding,

et al. 2023

Applied Sciences

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The era of educational big data has sparked growing interest in extracting and organizing educational concepts from massive amounts of information. Outcomes are of the utmost importance for artificial intelligence–empowered teaching and learning. Unsupervised educational concept extraction methods based on pre-trained models continue to proliferate due to ongoing advances in semantic representation. However, it remains challenging to directly apply pre-trained large language models to extract educational concepts; pre-trained models are built on extensive corpora and do not necessarily cover all subject-specific concepts. To address this gap, we propose a novel unsupervised method for educational concept extraction based on word embedding refinement (i.e., word embedding refinement–based educational concept extraction (WERECE)). It integrates a manifold learning algorithm to adapt a pre-trained model for extracting educational concepts while accounting for the geometric information in semantic computation. We further devise a discriminant function based on semantic clustering and Box–Cox transformation to enhance WERECE’s accuracy and reliability. We evaluate its performance on two newly constructed datasets, EDU-DT and EDUTECH-DT. Experimental results show that WERECE achieves an average precision up to 85.9%, recall up to 87.0%, and F1 scores up to 86.4%, which significantly outperforms baselines (TextRank, term frequency–inverse document frequency, isolation forest, K-means, and one-class support vector machine) on educational concept extraction. Notably, when WERECE is implemented with different parameter settings, its precision and recall sensitivity remain robust. WERECE also holds broad application prospects as a foundational technology, such as for building discipline-oriented knowledge graphs, enhancing learning assessment and feedback, predicting learning interests, and recommending learning resources.

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Section: Rule-based Methodsmentioning

confidence: 99%

Section: Baselines and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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WERECE: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement

Huang,

Ding,

et al. 2023

Applied Sciences

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“…Other: Part of the studies presented tools to extract key-phrases [8,265], slides [83,153], and software source code (from video tutorials to learn computer programming) [7,114,265].…”

Section: Information Extraction Toolsmentioning

confidence: 99%

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

Navarrete¹,

Nehring²,

Schanze³

et al. 2023

Preprint

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People increasingly use videos on the Web as a source for learning, be it for daily tasks or in formal as well as informal educational settings. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning from computer science databases for the period from 2016 to 2021 using the PRISMA guidelines. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners' activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner's activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner's activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends in the development of design guidelines to produce learning videos.

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Knowledge discovery for course choice decision in Massive Open Online Courses using machine learning approaches

Nilashi

Minaei‐Bidgoli

Alghamdi

et al. 2022

Expert Systems with Applications

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A novel cluster-based approach for keyphrase extraction from MOOC video lectures

Cited by 5 publications

References 30 publications

WERECE: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement

WERECE: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

Knowledge discovery for course choice decision in Massive Open Online Courses using machine learning approaches

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