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

Tourist reviews are information sources for travelers to know about tourist places. Unfortunately, some reviews are irrelevant and become noisy data. Aspect-based sentiment classification methods have shown promise in suppressing the noise. However, not much research has been done on automatic aspect identification, and identification of implicit, infrequent and co-referential aspects, resulting in misclassifications. This paper presents a framework of aspect-based sentiment classification that will not only identify the aspects very efficiently but can perform classification task with high accuracy. The framework has been implemented as a mobile app that helps tourists find the best restaurant or hotel in a city, and performance has been evaluated by conducting experiments on real-world datasets with excellent results (85% identification and 90% classification).

Due to the large amount of opinions available on the websites, tourists are often overwhelmed with information and find it extremely difficult to use the available information to make a decision about the tourist places to visit. A number of opinion mining methods have been proposed in the past to identify and classify an opinion into positive or negative. Recently, aspect based opinion mining has been introduced which targets the various aspects present in the opinion text. A number of existing aspect based opinion classification methods are available in the literature but very limited research work has targeted the automatic aspect identification and extraction of implicit, infrequent, and coreferential aspects. Aspect based classification suffers from the presence of irrelevant sentences in a typical user review. Such sentences make the data noisy and degrade the classification accuracy of the machine learning algorithms. This paper presents a fuzzy aspect based opinion classification system which efficiently extracts aspects from user opinions and perform near to accurate classification. We conducted experiments on real world datasets to evaluate the effectiveness of our proposed system. Experimental results prove that the proposed system not only is effective in aspect extraction but also improves the classification accuracy.