Learning analytics (LA) provides data-driven feedback that aims to improve learning and inform action. For learners, LA-based feedback may scaffold self-regulated learning skills, which are crucial to learning success. For teachers, LA-based feedback may help the evaluation of teaching effects and the need for interventions. However, the current development of LA has presented problems related to the cognitive, social-affective, and structural dimensions of feedback. In light of this, this position paper argues that attention needs to shift from the design of LA as a feedback product to one that facilitates a process in which both teachers and students play active roles in meaning-making. To this end, implications for feedback literacy in the context of LA are discussed.
@article{arxiv.2209.00879,
title = {Why Feedback Literacy Matters for Learning Analytics},
author = {Yi-Shan Tsai},
journal= {arXiv preprint arXiv:2209.00879},
year = {2023}
}
Comments
8 pages. Accepted at the 2022 International Conference of the Learning Sciences (ICLS). https://repository.isls.org/bitstream/1/8799/1/ICLS2022_27-34.pdf