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Personalized item recommendation enables the educational assessment system to make deliberate efforts to perform appropriate assessment strategies that t 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.

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