Digital Value-Adding Chains in Vocational Education: Automatic Keyword Extraction from Learning Videos to Provide Learning Resource Recommendations

Schulten, Cleo; Manske, Sven; Langner-Thiele, Angela; Hoppe, H. Ulrich

doi:10.1007/978-3-030-57717-9_2

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…A popular approach was to extract topics, concepts, and keywords to compute the similarity to other videos. The use of ontologies and external knowledge bases was used particularly to extract concepts [24,25,40,206]. Some interesting approaches considered other characteristics such as video readability [111],…”

Section: Discussionmentioning

confidence: 99%

“…Regarding tasks and technologies, we identified some low-level tasks performed as basic steps, of which, many rely on deep learning approaches. For example, for textual video elements, several studies employed optical character recognition [14,21,29,32,49,50,54,64,67,102,109,114,123,138,152,160,161,179,195,255,261,262,265,273,276], keyword extraction [14,40,43,61,92,105,106,109,112,121,128,131,161,206,255,264,271], generic natural language processing methods (e.g., [29,127,128,194,243]), or utilized word embeddings (e.g., [54,…”

Section: Audio Featuresmentioning

confidence: 99%

“…Based on ontologies and external knowledge bases: Schulten et al [206] determined related concepts through external ontologies based on Wikipedia articles and extracted keywords using the term frequency-inverse document frequency algorithm to discover new learning resources through a web search API. In the same way, Borges and Reis [24] extracted concepts and their relationships from an existing knowledge base to recommend videos.…”

Section: Recommender and Retrieval Systemsmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Discussionmentioning

confidence: 99%

Section: Audio Featuresmentioning

confidence: 99%

Section: Recommender and Retrieval Systemsmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…It explores and forms the basic theory and method of data-driven micro-video learning resource analysis to support the research of personalized educational theory and method. Schulten et al [25] explores a way to provide such intelligent learning resource recommendation based on a specific learning environment, aiming to automatically analyze learning videos to extract key words, which can then be used to discover and recommend new learning materials related to videos. The result shows that the extracted key words are consistent with those generated by users and well summarize the contents of videos.…”

Section: Introductionmentioning

confidence: 99%

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Liu¹

2023

Int. J. Emerg. Technol. Learn.

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Video is an important carrier of all kinds of information on the Internet. More and more students choose to watch online learning videos from online learning platforms with various forms of intelligent terminals. To allow students to get the videos which they really need and are interested in from the mass videos, it is necessary to improve the existing personalized recommendation algorithm. Content-based recommendation method, collaborative filtering-based recommendation method and hybrid recommendation method often have the problems of loss of feature information or cocoon. To this end, this paper studies the personalized recommendation service of media educational resources based on multi-dimensional user features. This paper constructs a multi-feature candidate set based on student-video interaction, extracts and splices category feature, label feature and semantic feature of Internet media educational videos, and generates the representation of students’ learning preference feature. Based on the improved forward and backward recurrent neural network, this study constructs a recommendation model of Internet media educational videos oriented to students' preference points. The experimental result verifies the validity of the model.

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Connecting the dots – A literature review on learning analytics indicators from a learning design perspective

Ahmad

Schneider

Griffiths

et al. 2022

Computer Assisted Learning

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BackgroundDuring the past decade, the increasingly heterogeneous field of learning analytics has been critiqued for an over‐emphasis on data‐driven approaches at the expense of paying attention to learning designs.Method and objectiveIn response to this critique, we investigated the role of learning design in learning analytics through a systematic literature review. 161 learning analytics (LA) articles were examined to identify indicators that were based on learning design events and their associated metrics. Through this research, we address two objectives. First, to achieve a better alignment between learning design and learning analytics by proposing a reference framework, where we present possible connections between learning analytics and learning design. Second, to present how LA indicators and metrics have been researched and applied in the past.Results and conclusionIn our review, we found that a number of learning analytics papers did indeed consider learning design activities for harvesting user data. We also found a consistent increase in the number and quality of indicators and their evolution over the years.

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Digital Value-Adding Chains in Vocational Education: Automatic Keyword Extraction from Learning Videos to Provide Learning Resource Recommendations

Cited by 3 publications

References 24 publications

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

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

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Connecting the dots – A literature review on learning analytics indicators from a learning design perspective

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