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

Abstract

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.

Keywords:

• formative assessment
• data-based decision making
• assessment for learning
• diagnostic testing
• theoretical comparison

Acknowledgements

The authors would like to thank the many people who supported and inspired us on this endeavour. An earlier version of this paper was published in Fabienne van der Kleij’s doctoral dissertation (2013).

Additional information

Funding

This research was supported by Cito, Institute for Educational Measurement in the Netherlands, and the University of Twente in the Netherlands. The paper was presented at the 14th annual conference of the Association for Educational Assessment Europe (AEA-E), Paris 2013, with support from the AEA-E Kathleen Tattersall New Researcher Award. Further partial support in developing this paper was received from Australian Catholic University.