An improved adaptive personalization model for instructional video-based e-learning environments

Due to the unexpected COVID-19 pandemic, video-based e-learning environments for programming education have disrupted traditional classroom teaching methods. The major drawbacks of these environments are that they never consider the individual differences and personal traits of the learner while bui...

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Vydáno v:Journal of computers in education (the official journal of the Global Chinese Society for Computers in Education) Ročník 12; číslo 1; s. 267 - 313
Hlavní autoři: Sanal Kumar, T S, Thandeeswaran, R.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2025
Springer Nature B.V
Témata:
Access to Education
Algorithms
Blended learning
Classrooms
Cognition & reasoning
Cognitive style
Computer programming
Computers
Computers and Education
Conventional Instruction
Customization
Distance Education
Distance learning
Education
Educational Environment
Educational Experience
Educational Media
Educational objectives
Educational Technology
Electronic Learning
Evaluation
Higher education
Influence of Technology
Instructional Improvement
Instructional Materials
Knowledge
Knowledge acquisition
Learner Engagement
Learning Experience
Learning Management Systems
Learning Problems
Learning Processes
Online Courses
Online instruction
Outcomes of Education
Personalized learning
Positive feedback
Preferences
Programming
School environment
Students
Teaching methods
Undergraduate study
Video
Python programming
Statistical analysis
Learning style model
Higher education
Blended learning
Personalized instructional videos
ISSN:2197-9987, 2197-9995
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Popis
Shrnutí:Due to the unexpected COVID-19 pandemic, video-based e-learning environments for programming education have disrupted traditional classroom teaching methods. The major drawbacks of these environments are that they never consider the individual differences and personal traits of the learner while building a challenging course like programming, having high dropout and failure rates. To address this issue, this paper proposed a learning style-enabled novel rule-based personalized instructional video delivery model for programming education. The model used the following four learning parameters for delivering the instructional videos: (a) most recent instructional video, (b) assessment score, (c) complexity level, and (d) weight (variance of two recent assessments) score. This work was designed using a paired pre-test–post-test experimental approach with first-year undergraduate students. For the experimental evaluation, students were randomly classified into three groups. Learner scores and feedback were taken as evaluation metrics. Results revealed that the proposed model-driven group showed significant improvements in knowledge acquisition, grade, and positive feedback compared to the other groups. Hence, the proposed model is highly recommended for traditional programming e-learning environments to deliver personalized instructional videos based on learners’ receptive pace, cognitive level, and learning preference.
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ISSN:2197-9987
2197-9995
DOI:10.1007/s40692-023-00310-x