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 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.