Formative assessment strategies for students' conceptions—The potential of learning analytics

Abstract

Formative assessment is considered to be helpful in students' learning support and teaching design. Following Aufschnaiter's and Alonzo's framework, formative assessment practices of teachers can be subdivided into three practices: eliciting evidence, interpreting evidence and responding. Since students' conceptions are judged to be important for meaningful learning across disciplines, teachers are required to assess their students' conceptions. The focus of this article lies on the discussion of learning analytics for supporting the assessment of students' conceptions in class. The existing and potential contributions of learning analytics are discussed related to the named formative assessment framework in order to enhance the teachers' options to consider individual students' conceptions. We refer to findings from biology and computer science education on existing assessment tools and identify limitations and potentials with respect to the assessment of students' conceptions. Practitioner notes What is already known about this topic Students' conceptions are considered to be important for learning processes, but interpreting evidence for learning with respect to students' conceptions is challenging for teachers. Assessment tools have been developed in different educational domains for teaching practice. Techniques from artificial intelligence and machine learning have been applied for automated assessment of specific aspects of learning. What does the paper add Findings on existing assessment tools from two educational domains are summarised and limitations with respect to assessment of students' conceptions are identified. Relevent data that needs to be analysed for insights into students' conceptions is identified from an educational perspective. Potential contributions of learning analytics to support the challenging task to elicit students' conceptions are discussed. Implications for practice and/or policy Learning analytics can enhance the eliciting of students' conceptions. Based on the analysis of existing works, further exploration and developments of analysis techniques for unstructured text and multimodal data are desirable to support the eliciting of students' conceptions.