Research on the Development of a Personalized Learning Assessment Model: Building Connections Between Knowledge Components and Cognitive Levels

Abstract

Assignment and examination are typical formative and summative assessment strategies in K-12 education. A large number of assessment data generated by learners offers an opportunity for personalized assessment. The research on assessment data has centered on large-scale reporting on aggregate level results, fewer studies have focused on student-level features. In this study, we tried to align Bloom’s taxonomy of educational objectives with learning assessment, and construct a personalized assessment model using the assignment and examination data based on the cognitive diagnostic assessment approach. The model includes three assessment dimensions including the achievement of educational objectives, the mastery level of knowledge components and risk detection. The model was validated using 2,600 online learning data from 50 senior high school students. The testing content includes one topic from algebra and another one from trigonometry. The results indicate that the model can help students make timely and targeted remedies of their learning gaps. There is a positive correlation between students' cognitive level and their mastery of knowledge components, and students with the same scores have different cognitive structures and knowledge structures, although they are at the same level in the traditional sense, they can find out the complementary intervals and increase the effective interaction. Assessment data is an explicit form of students' internal cognitive level, compared with a total score, teachers are more concerned about students' cognitive level and their mastery of specific knowledge, especially knowledge components with risks.