Today, collaborative learning has become quite central as a method for learning, and over the past decades, a large number of studies have demonstrated the benefits from various theoretical and methodological perspectives. This study proposes a novel approach that utilises Natural Language Processing(NLP) methods, particularly pre-trained word embeddings, to automatically create homogeneous or heterogeneous groups of students in terms of knowledge and knowledge gaps expressed in assessments. The two different ways of creating groups serve two different pedagogical purposes: (1) homogeneous group formation based on students’ knowledge can support and make teachers’ pedagogical activities such as feedback provision more time efficient, and (2) the heterogeneous groups can support and enhance collaborative learning. We evaluate the performance of the proposed approach through experiments with a dataset from a university course in programming didactics.

Knowledge tracing—where a machine models the students’ knowledge as they interact with coursework—is a well-established area in the field of Artificial Intelligence in Education. In this paper, an ensemble approach is proposed that addresses existing limitations in question-centric knowledge tracing and achieves the goal of predicting future question correctness. The proposed approach consists of two models; one is Light Gradient Boosting Machine (LightGBM) built by incorporating all relevant key features engineered from the data. The second model is a Multiheaded-Self-Attention Knowledge Tracing model (MSAKT) that extracts historical student knowledge of future question by calculating their contextual similarity with previously attempted questions. The proposed model’s effectiveness is evaluated by conducting experiments on a big Kaggle dataset achieving an Area Under ROC Curve (AUC) score of 0.84 with 84% accuracy using 10fold cross-validation.

In this paper, we propose a counterfactual explanations-based approach to provide an automatic and intelligent recommendation that supports student's self-regulation of learning in a data-driven manner, aiming to improve their performance in courses. Existing work under the fields of learning analytics and AI in education predict students' performance and use the prediction outcome as feedback without explaining the reasons behind the prediction. Our proposed approach developed an algorithm that explains the root causes behind student's performance decline and generates data-driven recommendations for action. The effectiveness of the proposed predictive model that constitutes the intelligent recommendations is evaluated, with results demonstrating high accuracy.

Collaborative learning has grown more popular as a form of instruction in recent decades, with a significant number of studies demonstrating its benefits from many perspectives of theory and methodology. However, it has also been demonstrated that effective collaborative learning does not occur spontaneously without orchestrating collaborative learning groups according to the provision of favourable group criteria. Researchers have investigated different foundations and strategies to form such groups. However, the group criteria semantic information, which is essential for classifying groups, has not been explored. To capture the group criteria semantic information, we propose a novel Natural Language Processing (NLP) approach, namely using pre-trained word embedding. Through our approach, we could automatically form homogeneous and heterogeneous collaborative learning groups based on student’s knowledge levels expressed in assessments. Experiments utilising a dataset from a university programming course are used to assess the performance of the proposed approach.

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.

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.