Taking assessment as teaching and learning strategy for improving students’ e-Learning effectiveness Computers & Education, 54 (2010) 1157–1166 Tzu-Hua Wang Reporter : Yu Chih Lin

Outline  Introduction  Method  Conclusion  Suggestion

Introduction(1/3)  Combine 2 major instructional characteristics dynamic assessment a.Web-based dynamic assessment system (GPAM-WATA) b.Normal web-based test (N-WBT )  Based on assessment as teaching and learning strategy  Adopt the graduated prompt approach (GPA)

Introduction(2/3)  Design of GPAM-WATA  Two formats of dynamic assessment a)Sandwich format - Between the pre-test and the post-test b)Cake format - Administered in an individualized way

Introduction(3/3)  Argued that the domain-specific prior knowledge a)Impacted learner achievement b)Interacted with different phases of information processing c)Trouble in learning new information  Different designs and strategies to facilitate learning

Method(1/10)  3 elementary school teachers and the sixth grade students  Computer course - Nature and Life Technology  Randomly assigned to learn in two groups a)GPAM-WATA group b)N-WBT group

Method(2/10) GenderGroup N-WBTGPAM-WATASum Female Male Sum Participant distribution in the GPAM-WATA group and the N-WBT group

Method(3/10)  E-Learning materials are about Plant Photosynthesis A: Learning guide’ section B: Learning contents’ section C: Adobe Flash animation.

Method(4/10)  Graduated prompting assessment module system IP Instructional Prompts Item START END Item A B C D F E

Method(5/10)

Method(6/10)  Data collection a)Pre-test scores of the summative assessment b)Post-test scores of the summative assessment c)Scores of prior knowledge assessment

Method(7/10)  According to the scores of prior knowledge assessment a)Low-level prior knowledge groups b)Middle-level prior knowledge groups c)High-level prior knowledge groups

Method(8/10) SourceSSdfMSFvaluePostHoc^a Pre-test scores of the summative assessment ** Different types of Web-based assessment (A) ** GPAM-WATA > N-WBT** Different levels of prior knowledge (B) **Middle-level prior knowledge > low-level prior knowledge ** High-level prior knowledge > low-level prior knowledge ** AXB * Error Corrected total  Two-way ANCOVA

Method(9/10)  Different levels of prior knowledge GroupVariableLevelMean^a (std.error)F valuePostHoc^b GPAM-WATA (n = 58) Pre-test scores of the summative assessment 4.863* Different levels of prior knowledge Low-level prior Knowledge Middle-level prior Knowledge High-level prior knowledge (2.636) (1.947) (2.285) N-WBT (n = 58)Pre-test scores of the summative assessment ** Different levels of prior knowledge Low-level prior Knowledge Middle-level prior Knowledge High-level prior knowledge (2.404) (2.995) (2.907) 9.164**M > L* H > L**

Method(10/10)  Prior knowledge groups GroupVariableLevelMean^a (std. error) F valuePostHoc^b Low-level prior knowledge (n = 41) Pre-test scores of the summative assessment Different types of Web-based assessment GPAM-WATA N-WBT (3.005) (2.371) ** ** GPAM-WATA > N- WBT** Middle-level prior knowledge (n = 36) Pre-test scores of the summative assessment Different types of Web-based assessment GPAM-WATA N-WBT (2.125) (2.523) ** ** GPAM-WATA > N- WBT** High-level prior knowledge (n = 39) Pre-test scores of the summative assessment Different types of Web-based assessment GPAM-WATA N-WBT (2.206) (2.390) ** ** GPAM-WATA > N- WBT**

Conclusion  Construction of an assessment-centred e-learning environment  Graduated prompt approach is effective in facilitating learning  May not be generalizable to other subjects

Suggestion  Recommend teachers develop dynamic assessment items and IPs focusing on the learning contents  Investigate how the types and dimensions of prior knowledge affect learning effectiveness