Classification of Instructional Videos

Köse, Erman; Taşlıbeyaz, Elif; Karaman, Selçuk

doi:10.1007/s10758-021-09530-5

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…The result of this study suggests that certain aspects of learner control can be beneficial when learning from instructional videos featuring embedded virtual humans. In another study by [17], a scheme for classifying instructional videos based on eight features was presented. Interaction, screen design, sequence, included components (picture, voice, and text), and content are among the important features.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Investigating the Effect of Using Educational Videos and Recorded Lectures in Understanding the Contents of Pre-Engineering Courses

Almekhlafi,

Zaneldin,

Ahmed

2024

Int. J. Eng. Ped.

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Some students may struggle to understand the contents of courses with intensive conceptual content and problem-solving, especially when the courses are offered online. Offering courses online may present challenges such as technological issues, time management, and a lack of interaction with instructors. This paper investigates the effect of using educational videos and recorded lectures on students enrolled learning and performance. A questionnaire survey was conducted among students in the “Engineering Economics” course at an international institution for pre-engineering students before they chose their specialization. The survey was conducted during the Fall 2021 semester (before the coronavirus pandemic) and the “Spring 2022” semester (during the pandemic). The performance of students during the “Spring 2022” semester was compared with their performance during the “Fall 2021” semester, when students relied solely on face-to-face lectures. During the “Spring 2022” semester, educational videos and recorded lectures were made available to students. Analysis results revealed that students had a positive perception towards the use of educational videos and recorded lectures. Students’ feedback regarding watching videos and recorded lectures was quite positive and enhanced their performance in the course. A generic VBA framework was developed to suggest educational videos and recorded lectures relevant to selected course topics. An initial pilot test of this tool received very positive feedback from students.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Investigating the Effect of Using Educational Videos and Recorded Lectures in Understanding the Contents of Pre-Engineering Courses

Almekhlafi,

Zaneldin,

Ahmed

2024

Int. J. Eng. Ped.

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“…However, these styles were not named, but represented by symbols. In (Köse et al, 2021), the authors have as main references the articles by Hansch et al (2015) and Crook & Schofield (2017), and they sought to expand the classification of video lessons beyond their style, identifying new characteristics such as interaction (ability to stop, advance, or rewind the video); connection (internet access requirement for video playback); sequence (transitions between parts of the video are structured); components (image, moving image, audio, and text); image format (2D, 3D); instant (live video); content (the subject of the video has individual and independent parts or not).…”

Section: Related Workmentioning

confidence: 99%

“…In an investigation of the impact of using a video lesson style, it is first necessary to identify and characterize the existing styles. There are different proposals for classifying video lesson styles where the authors have presented different definitions of styles (Hansch et al, 2015;Santos Espino et al, 2016;Crook & Schofield, 2017;Chorianopoulos, 2018;Köse et al, 2021). Although there are different theoretical classifications of video lesson styles in the literature, in reality, none was unanimously accepted.…”

Section: Introductionmentioning

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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“…Scholars have used a variety of labels for videos used in the instructional context, such as instructional videos, demonstration videos, Kahn academy videos, classroom videos or video explanations (Köse et al, 2021). To highlight their explanatory character, we use the term video explanations to describe videos that combine spoken language and visualizations to explain a topic, a principle, or a process to learners.…”

Section: Introductionmentioning

confidence: 99%

A Rating Framework for the Quality of Video Explanations

Ring

Brahm²

2022

Tech Know Learn

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More and more teachers create video explanations for their instruction. Whether or not they are effective for learning depends on the videos’ instructional quality. Reliable measures to assess the quality of video explanations, however, are still rare, especially for videos created by (preservice) teachers. We developed such a measure in a two-step process: First, the categories were theoretically derived. Second, a coding manual was developed and used with 36 videos, which were created by preservice teachers during a university seminar. The resulting framework, which can be used as a coding manual for future research, consists of twelve criteria in five different categories: video content, learner orientation, representation and design, language, and process structure. With this framework, we contribute a reliable measure to evaluate the quality of existing videos. In practice, teachers can also use this measure as a guideline when creating or choosing video explanations for the classroom.

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Classification of Instructional Videos

Cited by 6 publications

References 25 publications

Investigating the Effect of Using Educational Videos and Recorded Lectures in Understanding the Contents of Pre-Engineering Courses

Investigating the Effect of Using Educational Videos and Recorded Lectures in Understanding the Contents of Pre-Engineering Courses

Automatic Classification of Learning Material Styles

A Rating Framework for the Quality of Video Explanations

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