Mining Precedence Relations among Lecture Videos in MOOCs via Concept Prerequisite Learning

Xiao, Kui; Bai, Youheng; Wang, Shihui

doi:10.1155/2021/7655462

Cited by 4 publications

(8 citation statements)

References 17 publications

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“…Obviously, when the number of video lectures is large, the manual way is not feasible. Xiao et al [9] used the TextRazor to automatically extract core concepts from video captions, and then used LSTM networks to predict concept prerequisite relations, and finally inferred the precedence relations between video lectures in a MOOC.…”

Section: Related Workmentioning

confidence: 99%

“…Obviously, this way is inefficient. Xiao et al [9] used TextRazor 2 to automatically extract relevant Wikipedia concepts from the video captions. A Wikipedia concept is the title of a Wikipedia article.…”

Section: Video Captions Extraction and Core Concepts Selectionmentioning

confidence: 99%

“…Inspired by [9] and [17], we also utilized TextRazor to extract all relevant Wikipedia concepts from video captions and sorted them according to their relevance_score from largest to smallest. We selected the top 10 concepts as candidate core concepts.…”

Section: Video Captions Extraction and Core Concepts Selectionmentioning

confidence: 99%

“…The precedence relation measurement we used is consistent with the method of Xiao et al [9]. We assume that the precedence relations between video lectures arise from prerequisite relations between their core concepts.…”

Section: Video Pair Precedence Relation Extractionmentioning

confidence: 99%

“…In this section, we will evaluate the approach of precedence relation extraction. Here, we use the video lecture datasets created by Xiao et al [9]. The datasets contain four MOOCs offered by the MOOC provider Coursera, including "Food and health", "Algorithms", "Calculus" and "Global history".…”

Section: Precedence Relation Prediction Experimentsmentioning

confidence: 99%

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Extracting Precedence Relations between Video Lectures in MOOCs

Xiao

Bai

Zhang

2022

Proceedings of the 2022 International Conference on Multimedia Retrieval

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Nowadays, the high dropout rate has become a widespread phenomenon in various MOOC platforms. When learning a MOOC, many learners are reluctant to spend time learning from the first video lecture to the last one. If we can recommend a learning path based on learners' individual needs and ignore irrelevant video lectures in the MOOC, it will help them learn more efficiently. The premise of learning path recommendation is to understand the precedence relations between learning resources. In this paper, we propose a novel approach for extracting precedence relations between video lectures in a MOOC. According to "knowledge depth" of concepts, we extract the core concepts from the video captions accurately. Transformer-based models are used to discover concept prerequisite relations, which help us identify the precedence relations between video lectures in MOOCs. Experiments show that the proposed method outperforms the state-of-the-art methods. CCS CONCEPTS• Applied computing → E-learning; • Computing methodologies → Information extraction; Reasoning about belief and knowledge.

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Section: Related Workmentioning

confidence: 99%

Section: Video Captions Extraction and Core Concepts Selectionmentioning

confidence: 99%

Section: Video Captions Extraction and Core Concepts Selectionmentioning

confidence: 99%

Section: Video Pair Precedence Relation Extractionmentioning

confidence: 99%

Section: Precedence Relation Prediction Experimentsmentioning

confidence: 99%

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Extracting Precedence Relations between Video Lectures in MOOCs

Xiao

Bai

Zhang

2022

Proceedings of the 2022 International Conference on Multimedia Retrieval

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The Roles of Visual Elements in Lightboard Videos for Online Learning

Wan Omar Sukri,

Daimin,

Nor Azlan

2024

Proceedings of the International Conference on Advancing and Redesigning Education 2023

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Annotation Protocol for Textbook Enrichment with Prerequisite Knowledge Graph

Alzetta,

Torre,

Koceva

2023

Tech Know Learn

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Extracting and formally representing the knowledge embedded in textbooks, such as the concepts explained and the relations between them, can support the provision of advanced knowledge-based services for learning environments and digital libraries. In this paper, we consider a specific type of relation in textbooks referred to as prerequisite relations (PR). PRs represent precedence relations between concepts aimed to provide the reader with the knowledge needed to understand a further concept(s). Their annotation in educational texts produces datasets that can be represented as a graph of concepts connected by PRs. However, building good-quality and reliable datasets of PRs from a textbook is still an open issue, not just for automated annotation methods but even for manual annotation. In turn, the lack of good-quality datasets and well-defined criteria to identify PRs affect the development and validation of automated methods for prerequisite identification. As a contribution to this issue, in this paper, we propose PREAP, a protocol for the annotation of prerequisite relations in textbooks aimed at obtaining reliable annotated data that can be shared, compared, and reused in the research community. PREAP defines a novel textbook-driven annotation method aimed to capture the structure of prerequisites underlying the text. The protocol has been evaluated against baseline methods for manual and automatic annotation. The findings show that PREAP enables the creation of prerequisite knowledge graphs that have higher inter-annotator agreement, accuracy, and alignment with text than the baseline methods. This suggests that the protocol is able to accurately capture the PRs expressed in the text. Furthermore, the findings show that the time required to complete the annotation using PREAP are significantly shorter than with the other manual baseline methods. The paper includes also guidelines for using PREAP in three annotation scenarios, experimentally tested. We also provide example datasets and a user interface that we developed to support prerequisite annotation.

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Mining Precedence Relations among Lecture Videos in MOOCs via Concept Prerequisite Learning

Cited by 4 publications

References 17 publications

Extracting Precedence Relations between Video Lectures in MOOCs

Extracting Precedence Relations between Video Lectures in MOOCs

The Roles of Visual Elements in Lightboard Videos for Online Learning

Annotation Protocol for Textbook Enrichment with Prerequisite Knowledge Graph

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