Segmenting lecture video into partitions by analyzing the contents of video

Ali, Mohammed Mahmood; Qaseem, Mohammad S.; Hussain, A.R.

doi:10.1109/icdabi53623.2021.9655924

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…The other retrieved articles, despite not directly mentioning a style, use it to automatically extract different characteristics from the video lessons. Considering the Voice Over Slides style, Ali et al (2021) presented improvements for indexing video classes, and Balasubramanian et al (2015) showed a way to summarize the contents presented. Regarding the Writing Over Slides style, Kao et al (2013) developed a method for detecting annotations made in slide presentations.…”

Section: How Video Lesson Styles Have Been Characterized In the Work ...mentioning

confidence: 99%

Automatic Classification of Learning Material Styles

Aquino,

Souza,

Barrére

2023

RBIE

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Although video lessons are often used in diverse areas, the lack of a common approach to defining and classifying their styles results in using many different models for these purposes. There is a need to build a framework through which these styles can be defined and classified. Much has been done to investigate the effects of these styles on student engagement and learning outcomes. These studies suggest that video lesson styles affect academic performance and that students learn better through a certain video lesson style. Based on this, we propose a unified model for classifying video lesson styles based on the nomenclatures and definitions used in the literature. Furthermore, we present an approach for automatically classifying four popular video lesson styles. The automatic classification is useful for recommendation systems to suggest materials more consistent with student preferences and their intended learning outcomes.

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Section: How Video Lesson Styles Have Been Characterized In the Work ...mentioning

confidence: 99%