Apriori Optimization Model for the Intervention Strategies in Educational Model with Sentimental-Based Learning Analytics

Authors

  • Yunlyu Lu Angeles University Foundation, MacArthur Hwy, Angeles 2009, Pampanga, Philippines and Foreign Language Department, Guangxi University of Chinese Medicine, Nanning, Guangxi, 530200, China
  • Emily Sarmiento Angeles University Foundation, MacArthur Hwy, Angeles 2009, Pampanga, Philippines

Keywords:

Apriori Rule, Learning Analytics, Sentimental Analysis, Intervention Strategies, Optimization, Educational Model

Abstract

Sentiment analysis can help educational institutions and instructors analyze student feedback and comments on courses, assignments, and teaching methodologies. This can provide insights into areas of improvement and identify any recurring issues. With analyzing text-based interactions in online learning platforms, sentiment analysis can gauge students' level of engagement and interest in the course content. Positive sentiment indicates high engagement, while negative sentiment may indicate disinterest or confusion. This paper presents a novel approach, the Apriori Tokenization Implicit Whale Optimization (ATiWO), to enhance emotion education and intervention strategies by harnessing the synergies of sentiment analysis and learning analytics. With the implementation of ATiWO demonstrates how textual data can be analyzed to extract emotional nuances, providing insights into students' sentiments and perceptions. Through the proposed ATiWO model the textual data is tokenized with the computation of the Implicit factors. With the integration of the Tokenization with Whale Optimization model. With the consideration of the optimal features in the textual data optimization pattern are computed with the behaviour of the whale. Learning analytics complements sentiment analysis by revealing patterns in student engagement and performance. The ATiWO approach amalgamates sentiment analysis and learning analytics, incorporating the Apriori algorithm, tokenization, and whale optimization. This unique framework optimizes sentiments, generates personalized intervention strategies, and improves academic performance and engagement levels simultaneously. The results demonstrated that the effectiveness of the approach through comprehensive performance metrics, including emotional coherence improvement, strategy success rate, and overall performance score. The results highlight the potential of ATiWO in revolutionizing education through data-driven, emotion-centered intervention strategies that enhance learning experiences, foster emotional well-being, and empower students in families, schools, and communities.

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Published

30.11.2023

How to Cite

Lu, Y. ., & Sarmiento, E. . (2023). Apriori Optimization Model for the Intervention Strategies in Educational Model with Sentimental-Based Learning Analytics. International Journal of Intelligent Systems and Applications in Engineering, 12(6s), 466–480. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3989

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Section

Research Article