A Cluster-based Personalized Item Recommended Approach on the Educational Assessment System

Abstract

Personalized item recommendation enables the educational assessment system to make deliberate efforts to perform appropriate assessment strategies that fit the needs, purposes, preferences, and interests of individual teachers. This study presents a dynamically personalized item-recommendation approach that is based on clustering in-serve teachers with assessment compiling interest and preference characteristics to recommend available, best-fit candidate items to support teachers to construct their classroom assessment. A two-round assessment constructing activity was being adopted to collect and extract these teacher’ assessment knowledge (item selected preference behaviors), and through the designed item-recommendation mechanism to facilitate IKMAAS [1] to recommend proper items to meet different individual in-serve teachers. To evaluate the effectiveness and usability for the cluster-based personalized item-recommendation, the assessment system log analysis and the questionnaire collected from participating teachers’ perceptions were being used. The results showed the proposed item-recommendation approach based on clustered teachers’ assessment knowledge can effectively improve their educational assessment construction.