NAVIGATING ASSESSMENT ALIGNMENT IN BLENDED LEARNING: A COMPARATIVE ANALYSIS OF MAJOR AND NON-MAJOR SUBJECTS

Abstract

A powerful pedagogical strategy has evolved in the age of developing educational technology: blended learning, which combines traditional instruction with online resources. In particular, it examines distinctions between major and non-major subjects in order to address the essential issue of assessment alignment in blend-ed learning. While investigations frequently evaluate the effectiveness of blended learning, few examine how examinations fit with its distinctive features. The assessment alignment in major and non-major subjects is compared in this study using the Correlation and Regression Tree (CART) model. Determining how assessments should align and how to make sure they measure topic mastery in this blended setting are among the key issues discussed. The study contributes to conversations about curriculum design and policy by assessing the literature and examining assessment procedures. The study offers knowledge that can be used to create evaluations for blended learning that are efficient while also promoting engagement among learners and academic success. Through this investigation, teachers receive tips on instructional design that will help them in the digital age to promote a unified learning experience across all topics.

Keywords

• blended learning
• correlation and regression tree algorithm
• curriculum development
• digital transformation.