In HIKOF (highly informative feedback for digital learning) project, the evaluation study has been successfully completed, where feedback was provided for about 1000 students in a Teacher Education Course (from Psychology department). Students’ feedback on the feedback that was given to them was also collected to determine how comprehensive our feedback was, if it motivated them, and was helpful for them, and enhanced their learning progress.

Specifically, the evaluation study focuses on the use of machine learning for  the creation of personalized feedback that is both formative and summative. Data from a pilot study serves as a starting point for the evaluation to further optimize the AI technology for generating feedback. The evaluation study aims to investigate the effect of Highly Informative Automated Feedback (HIAF) on student performance and perception in relation to various tasks distributed throughout the semester. The effect of the innovative tasks on the performance and perception of the students is also examined. In addition, the acceptance of the automated feedback by the students is examined and the potential influence of innovative tasks and automated feedback on the learning behavior of the students in the online platform (Moodle) is examined.

The project is almost in its final year, where time will be spent on data analysis, determining how to make the feedback process sustainable after the project ends, and the team will be running workshops to transfer the gained knowledge into society and other universities as well. The team will also meet the industry partners at the end of March to discuss further steps.