Use of AI tools in learning platforms and the role of feedback for learning

Open Access
Conference Proceedings
Authors: Martin KröllKristina Burova-Keßler

Abstract: The digital transformation in the world of work has profound effects on the processes of career orientation and the transition between school and work. Together with international partners from Bulgaria, Germany, Greece, Spain, Italy and Hungary, a digital mentoring concept to secure the employability of young people has been or is being investigated in the three-year EU project "Career 4.0". The focus is on the further development of a personal development plan with the help of which the young people can reflect on their future employment opportunities. Compared to other teaching-learning situations, this is a learning process that is open to development without a predetermined true or false, as is usually the case with mathematical tasks, for example. This places special demands on the mentors when it comes to assessing which forms of feedback are particularly beneficial for the young people and which prove to be less beneficial.Within the framework of the EU project, empirical studies were carried out which came to the conclusion that the quality of the feedback that mentors give to mentees is assessed very dif-ferently by these groups of participants. The mentees see considerable potential for improve-ment when it comes to the quality of the feedback from the mentors. In contrast, the mentors themselves are not as critical of their activities in giving feedback. Over 60 mentees and over 30 mentors have participated in the empirical study so far.The starting point for the study is the meta-analysis of the research team around Hattie et al. (2016). They differentiate between the following forms of feedback: (1) task-related, (2) pro-cess-related, (3) self-regulation-related and (4) person- or self-related feedback. According to the evaluation of their meta-analysis, the second and third forms of feedback have the greatest effect on learning outcomes.Furthermore, scientific studies have shown that the acceptance of feedback depends on numerous influencing factors, which can be assigned to four areas: Characteristics of (1) the feedback message, (2) the feedback source, (3) the feedback recipient and (4) the feedback context. The effect of feedback can be related to three levels, following the psychology of lear-ning: (1) cognitive (e.g. closing competence gaps), (2) metacognitive (e.g. supporting self-assessment and self-awareness) and (3) motivational level (e.g. promoting readiness). How the feedback recipients (here: the young people) ultimately deal with the feedback also depends on their causal attribution, i.e. which reasons they see as causal for their progress or the failure of their actions. If, for example, they attribute their inadequate task performance to environmental factors, e.g. difficult and unfair tasks or disproportionate time pressure, or if they see the reasons in themselves, e.g. in their lack of commitment or insufficient skills, this has very different effects on the effects of the feedback. Among other things, this can lead to a "self-esteem distortion" if, for example, negative results are primarily attributed to external circumstances. The research project is also investigating the extent to which AI tools can help to make feed-back even more effective and efficient for learners. In order to provide IT and AI solutions (such as adaptive learning systems, learning analytics, intelligent CBR recommendation sys-tems) to support the giving of feedback, e.g. with the help of a learning platform, it is advantageous and necessary to make the feedback process transparent by using a process mo-delling approach and to work out individual process steps.Hattie, J. & Timperley, H. (2007): The Power of Feedback, in: Review of Educational Research Vol. 77, No. 1, 81-112.London, M. & McFarland, L. (2010): Assessment Feedback. In J. Farr & N. Tippins (Hrsg.), Employee Selection (S. 417-436). New York, London: Routledge.Narciss, S. (2013). Designing and Evaluating Tutoring Feedback Strategies for digital learning environments on the basis of the Interactive Tutoring Feedback Model. Digital Education Review, (23), 7–26.

Keywords: Forms of the Feedback, Learning platform, AI tools

DOI: 10.54941/ahfe1001504

Cite this paper: