INVESTIGATING THE INTERACTION BETWEEN E-TEST TYPE (ADAPTIVE VS. NON- ADAPTIVE) AND LEARNING STYLE (HOLISTIC VS. ANALYTICAL): IMPLICATIONS FOR SELF-EFFICACY AND STUDENTS' SATISFACTION WITH THE TEST AMONG EDUCATIONAL TECHNOLOGY STUDENTS

Abstract

This study is to examine how self-efficacy and test satisfaction among students studying educational technology are impacted by the type of electronic exam (adaptive vs. non-adaptive) and learning style (holistic vs. analytical). Recent trends toward the application of artificial intelligence in the development of intelligent assessment technologies that improve measurement accuracy and adjust assessments to the unique needs of each learner make the study significant. Using a 2x2 factorial experimental design, the study included a group of students studying educational technology who were categorized based on their learning preferences and exam types. When compared to non-adaptive exams, the results showed statistically significant differences in favor of adaptive exams, which raised students' academic satisfaction and sense of self-efficacy. In terms of efficiency and performance, analytical learners did better than holistic learners. Furthermore, when adaptive exams were combined with analytical learning styles, the interaction between exam type and learning style demonstrated a favorable combination effect that improved assessment effectiveness. These results are in line with current developments that highlight the value of implementing AI-based educational tools, including deep learning chatbots, which support individualized instruction and inspire exceptionally motivated pupils. In order to enhance educational quality and foster students' self-efficacy, the study suggests implementing adaptive assessment models in online learning environments and combining them with AI-powered personalized learning tools.