Learning analytics (LA) is an important area of research in technology-enhanced learning that has emerged during the last decade. In earlier years, several systematic reviews have been conducted that focused on the theories behind LA or on empirical studies that utilised LA-based methods to improve learning and teaching processes in higher education. However, to date, there has been no systematic review of papers that have adopted a software perspective to report on the many forms of learning analytics software (LAS) that have been developed, despite these being used more frequently than before in higher education to support learning and teaching processes. To fill this gap, this paper presents a systematic review of LAS with the aim of critically scrutinising the ways in which the use of interactive software in real-world settings may both support students in improving their academic performance and assist teachers in various pedagogical practices. A thematic analysis of 75 articles was conducted, resulting in the identification of three categories of LAS: at-risk student identification software; self-regulation software; and collaborative learning software. For each of these categories, we analysed (i) the embedded functionality; (ii) the stakeholder (teacher and student) for which the functionality is intended; (iii) the analytical and visualisation approaches implemented; and (iv) the limitations of the software that require future attention. Based on the findings of our review, we propose future directions for the development of LAS.

This paper proposes a novel approach that employs learning analytics techniques combined with explainable machine learning to provide automatic and intelligent actionable feedback that supports students self-regulation of learning in a data-driven manner. Prior studies within the field of learning analytics predict students’ performance and use 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 automatically provides data-driven recommendations for action. The underlying predictive model effectiveness of the proposed approach is evaluated, with the results demonstrating 90 per cent accuracy.

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.

Self-regulated learning is an essential skill that can help students plan, monitor, and reflect on their learning in order to achieve their learning goals. However, in situations where there is a lack of effective feedback and recommendations, it becomes challenging for students to self-regulate their learning. In this paper, we propose an explainable AI-based approach to provide automatic and intelligent feedback and recommendations that can support the self-regulation of students' learning in a data-driven manner, with the aim of improving their performance on their courses. Prior studies have predicted students' performance and have used these predicted outcomes as feedback, without explaining the reasons behind the predictions. Our proposed approach is based on an algorithm that explains the root causes behind a decline in student performance, and generates data-driven recommendations for taking appropriate actions. The proposed approach was implemented in the form of a dashboard to support self-regulation by students on a university course, and was evaluated to determine its effects on the students' academic performance. The results revealed that the dashboard significantly enhanced students' learning achievements and improved their self-regulated learning skills. Furthermore, it was found that the recommendations generated by the proposed approach positively affected students' performance and assisted them in self-regulation

Providing personalized support to students during courses is essential to facilitate them in their desired learning goals and reduce the dropout rate. Although teachers can play an effective role in providing personalized support, achieving individual-level assistance for massive courses becomes challenging. To overcome this challenge, this paper proposes a transformer-based approach that first models students’ knowledge of various course concepts based on their performance in various assessed tasks. Afterwards, the students’ concept-wise knowledge level derived from the models is combined with the available course material, leading to the generation of personalized concept-wise exercises by employing fine-tuned Text-to-Text Transfer Transformer (T5) architecture. These generated exercises help students to improve their knowledge about different course concepts. The proposed approach has been evaluated with various university courses to determine its quality, utility and effects on students’ academic performance. The evaluation results revealed that teachers and students were satisfied with the quality of the generated exercises, and these were found to be helpful for students to improve their concept-wise understanding. Furthermore, the generated exercises positively impacted students’ academic performance.