Developing an assessment-centered e-Learning system for improving student learning effectiveness

This research used Web-based two-tier diagnostic assessment and Web-based dynamic assessment to develop an assessment-centered e-Learning system, named the ‘GPAM-WATA e-Learning system.’ This system consists of two major designs: (1) personalized dynamic assessment, meaning that the system automatically generates dynamic assessment for each learner based on the results of the pre-test of the two-tier diagnostic assessment; (2) personalized e-Learning material adaptive annotation, meaning that the system annotates the e-Learning materials each learner needs to enhance learning based on the results of the pre-test of the two-tier diagnostic assessment and dynamic assessment. This research adopts a quasi-experimental design, applying GPAM-WATA e-Learning system to remedial Mathematics teaching of the ‘Speed’ unit in an elementary school Mathematics course. 107 sixth-graders from four classes in an elementary school participated in this research (55 male and 52 female). With each class as a unit, they were divided into four different e-Learning models: (1) the personalized dynamic assessment and personalized e-Learning material adaptive annotation group (n 1⁄4 26); (2) the personalized dynamic assessment and non-personalized e-Learning material adaptive annotation group (n 1⁄4 28); (3) the nonpersonalized dynamic assessment and personalized e-Learning material adaptive annotation group (n 1⁄4 26); and (4) the non-personalized dynamic assessment and non-personalized e-Learning material adaptive annotation group (n 1⁄4 27). Before remedial teaching, all students took the prior knowledge assessment and the pre-test of the summative assessment and two-tier diagnostic assessment. Students then received remedial teaching and completed all teaching activities. After remedial teaching, all students took the post-test of the summative assessment and two-tier diagnostic assessment. It is found that compared to the e-Learning models without personalized dynamic assessment, e-Learning models with personalized dynamic assessment are significantly more effective in facilitating student learning achievement and improvement of misconceptions, especially for students with low-level prior knowledge. This research also finds that personalized e-Learning material adaptive annotation significantly affects the percentage of reading time students spend on the e-Learning materials they need to enhance learning. However, it does not appear to predict student learning achievement and improvement of misconceptions. 2013 Elsevier Ltd. All rights reserved.