An Empirical Study of Adaptive Learning Algorithm Based on Intrinsic Motivation in English Online Teaching and Learning

Abstract

An empirical study of adaptive learning algorithms based on intrinsic motivation in English online teaching and learning delves into the intersection of technology and motivation in language education. By investigating how adaptive learning algorithms can leverage intrinsic motivation factors, such as personal interests and curiosity, researchers aim to optimize online English instruction for individual learners. This paper introduces Weighted Feature Clustering Adaptive Learning (WFCAL) as a novel approach to optimizing English teaching and learning processes. Traditional methods of instruction often fail to address the diverse needs and learning profiles of students, leading to suboptimal outcomes. WFCAL offers a solution by dynamically adapting instructional content based on the individual characteristics of learners, thereby creating personalized learning experiences. Through the application of WFCAL, significant improvements in language proficiency have been observed across various skill domains, including vocabulary, grammar, and reading comprehension. The algorithm clusters learners into groups based on their proficiency levels and adapts instructional delivery to cater to the specific needs of each cluster. This approach promotes engagement, motivation, and efficacy in language learning, leading to enhanced outcomes. learners in Cluster 1 showed an average increase in vocabulary knowledge from 65 to 80, while learners in Cluster 2 demonstrated an improvement from 70 to 85 in grammar understanding. Additionally, reading comprehension skills improved by an average of 10 points across all clusters. The algorithm clusters learners into groups based on their proficiency levels and adapts instructional delivery to cater to the specific needs of each cluster.