Abstract
Formative feedback has long been recognised as an effective tool for student learning, and researchers have investigated the subject for decades. However, the actual implementation of formative feedback practices is associated with significant challenges because it is highly time-consuming for teachers to analyse students’ behaviours and to formulate and deliver effective feedback and action recommendations to support students’ regulation of learning. This paper proposes a novel approach that employs learning analytics techniques combined with explainable machine learning to provide automatic and intelligent feedback and action recommendations that support student’s self-regulation in a data-driven manner, aiming to improve their performance in courses. Prior studies within the field of learning analytics have predicted students’ performance and have used the prediction status as feedback without explaining the reasons behind the prediction. Our proposed method, which has been developed based on LMS data from a university course, extends this approach by explaining the root causes of the predictions and by automatically providing data-driven intelligent recommendations for action. Based on the proposed explainable machine learning-based approach, a dashboard that provides data-driven feedback and intelligent course action recommendations to students is developed, tested and evaluated. Based on such an evaluation, we identify and discuss the utility and limitations of the developed dashboard. According to the findings of the conducted evaluation, the dashboard improved students’ learning outcomes, assisted them in self-regulation and had a positive effect on their motivation.
Highlights
Providing feedback is one of the many tasks that teachers perform to guide students towards increased learning and performance, and it is viewed as one of the most powerful practices to enhance student learning (Henderson et al, 2019)
46% of the feedback and questions were about action recommendations, 27% were about data utilisation, 17% were about machine learning approaches and the remaining were about design
We found that active participation in an learning management system (LMS), quiz score and assignment grade attributes were essential to determining student academic performance because they were positively correlated with student performance throughout the course
Summary
Providing feedback is one of the many tasks that teachers perform to guide students towards increased learning and performance, and it is viewed as one of the most powerful practices to enhance student learning (Henderson et al, 2019). To address these challenges, a number of studies within the fields of learning analytics (LA), artificial intelligence in education (AIED) and educational data mining (EDM) have investigated how students’ self-regulation could be supported through, for instance, dashboards that provide predictive student performance (Lakkaraju et al, 2015; Johnson et al, 2015; Kim et al, 2016; Marbouti et al, 2016; Akhtar et al, 2017; Chanlekha and Niramitranon 2018; Choi et al, 2018; Howard et al, 2018; Nguyen et al, 2018; Predić et al, 2018; Villamañe et al, 2018; Xie et al, 2018; Baneres et al, 2019; Bennion et al, 2019; Rosenthal et al, 2019; Nouri et al, 2019; D.) These studies employed various data mining, machine learning (ML), clustering and visualisation techniques on a diverse variety of learning management system (LMS) data sources to predict student success and failure in a course or in an entire academic year. Such feedback might be helpful to some extent, it does not provide any insightful information and actionable recommendations that could help students reach their desired academic performance (Baneres et al, 2019)
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