Error identification in online mathematical learning processes in upper secondary education

1 September 2022 until 31 August 2026
Project member(s)
Gerben van der Hoek
Project type

Intelligent tutoring systems (ITSs) are used increasingly to support online learning processes in different disciplines. To be able to provide a student with effective feedback within an ITS, the granularity of the student’s input plays an important role: the more coarse-grained the input is, the more difficult it is to provide specific feedback. This practice-oriented research seeks to identify student strategies and errors based on minimal input. For mathematics, we propose to develop means to determine students’ errors based on coarse-grained input through model backtracking (MBT). With MBT, a task is modelled such that different errors can be distinguished in student answers.

The main research question is: How can online learning processes be improved though error identification in coarse-grained input using MBT in Grade 11 ‘havo wiskunde A’? We start with a design study implementing MBT for two cases, followed by testing in teaching experiments. We use two task-based interviews with ‘havo wiskunde A’ students (n=25 each) to study the validity of error detection trough MBT, improving the initial design. We then scale up towards testing in a statistical study in Grade 11 ‘havo wiskunde A’.

We use an experimental group receiving specific feedback through MBT and a control group (n=100 each). We use datamining on the interaction logs to catalogue different learning strategies together with an analysis of variance on pre- and posttest scores to determine whether MBT contributes to positive online learning experiences and outcomes for students.