Impact of Feedback Features on Students' Learning Strategies: A Systematic Literature Review

This Research Full Paper presents a systematic literature review on the impact of feedback features on students' subsequent learning strategies. Research indicates that providing quality feedback improves learning performance in higher education, namely in computing and engineering education. Framed by the self-regulated learning model, this enhancement results from the interplay of cognitive, metacognitive, motivational, and behavioral actions driven by feedback toward the learning goal. Such a combination of planned and selected actions is the learning strategy decision to direct the accomplishment of learning tasks. Insights of how feedback interventions lead to increased use of effective learning strategies have predominantly relied on qualitative data from self-reports. However, self-reports mainly reflect students' perceptions but cannot accurately capture the dynamic adjustments of learning strategies in the feedback process. With the growing use of learning management systems that can collect various learning analytics data, recent works have attempted to automatically generate personalized feedback based on mapping students' progress against pre-determined rules. Learning strategy alterations as a result of the various forms of feedback can be detected from the trace data. The findings of these studies provide evidence of how various feedback features are associated with the adjustment of learning strategies. This paper presents a systematic literature review that analyzes papers related to shifting learning strategies upon feedback provision to identify features that can trigger students' adoption of more effective learning strategies. The objective is to collect evidence to highlight feedback as more than information but a process to guide the proper use of learning strategies for better learning achievement. Our analysis shows a need for more studies to observe changes in learner actions due to feedback and discusses limitations in current works. With the rapid development of education data mining and deep learning models, the growing knowledge of feedback features can be potentially used with these computational models to generate learning advice to guide strategy changes for achieving better learning outcomes.