Fabienne van der Kleij

Effects of Feedback in a Computer-Based Learning Environment on Students’ Learning Outcomes A Meta-Analysis

In this meta-analysis, we investigated the effects of methods for providing item-based feedback in a computer-based environment on students’ learning outcomes. From 40 studies, 70 effect sizes were computed, which ranged from −0.78 to 2.29. A mixed model was used for the data analysis. The results show that elaborated feedback (EF; e.g., providing an explanation) produced larger effect sizes (0.49) than feedback regarding the correctness of the answer (KR; 0.05) or providing the correct answer (KCR; 0.32). EF was particularly more effective than KR and KCR for higher order learning outcomes. Effect sizes were positively affected by EF feedback, and larger effect sizes were found for mathematics compared with social sciences, science, and languages. Effect sizes were negatively affected by delayed feedback timing and by primary and high school. Although the results suggested that immediate feedback was more effective for lower order learning than delayed feedback and vice versa, no significant interaction was found.

The effectiveness of methods for providing written feedback through a computer-based assessment for learning: a systematic review

This study reviews literature regarding the effectiveness of different methods for providing written feedback through a computer-based assessment for learning. In analysing the results, a distinction is made between lower-order and higherorder learning. What little high-quality research is available suggests that students could benefit from knowledge of correct response (KCR) to obtain lower-order learning outcomes. As well, elaborated feedback (EF) seems beneficial for gaining both lower-order and higher-order learning outcomes. Furthermore, this study shows that a number of variables should be taken into account when investigating the effects of feedback on learning outcomes. Implications for future research are discussed.

Integrating data-based decision making, Assessment for Learning and diagnostic testing in formative assessment

Recent research has highlighted the lack of a uniform definition of formative assessment, although its effectiveness is widely acknowledged. This paper addresses the theoretical differences and similarities amongst three approaches to formative assessment that are currently most frequently discussed in educational research literature: data-based decision making (DBDM), Assessment for Learning (AfL) and diagnostic testing (DT). Furthermore, the differences and similarities in the implementation of each approach were explored. This analysis shows that although differences exist amongst the theoretical underpinnings of DBDM, AfL and DT, the combination of these approaches can create more informed learning environments. The thoughtful integration of the three assessment approaches should lead to more valid formative decisions, if a range of evidence about student learning is used to continuously optimise student learning.