Sustainable aquaculture increasingly relies on functional feed additives to enhance fish growth and health. This study investigated the effects of chromium chloride hexahydrate-supplemented sorghum-based diets on the growth performance, hematological, and biochemical responses of Ctenopharyngodon idella during a 90-day feeding trial. Four diets with graded Cr₂Cl₃.6H₂O levels (0, 0.3, 0.5 and 0.7 mg/kg in diet) were tested, and fish receiving moderate supplementation showed the most efficient growth and feed utilization. Hematological parameters, including hemoglobin, hematocrit, and white blood cell counts, and biochemical indicators such as serum protein, lipid profile, glucose, and liver enzymes, responded in a dose-dependent manner, suggesting potential effects on metabolic, renal, and hepatic function. Regression analysis indicated an optimal supplementation level (0.55mg/kg) that maximized growth and physiological benefits without adverse effects. These results demonstrate that moderate chromium inclusion in sorghum-based diets can enhance growth performance and overall fish health, providing a practical strategy for aquafeed optimization. This study highlights the potential of graded chromium supplementation within a sorghum-based basal diet to support sustainable and efficient aquaculture practices, providing insights for the formulation of functional feeds.

The increasing costs of fish feed in the aquafeed industry have highlighted the need to explore non-conventional carbohydrate sources that can sustain fish growth and health. Integrating carbohydrates with chromium compounds has emerged as a viable strategy to enhance energy efficiency and improve the overall health of fish. To explore this, sorghum was utilized as the carbohydrate source in a 90-day feeding trial designed to evaluate the impact of Cr₂Cl₃•6H₂O supplementation on the growth performance and metabolic parameters of Labeo rohita. Four experimental diets were formulated, containing 0, 0.3, 0.5, and 0.7 mg of Cr₂Cl₃•6H₂O per kilogram, labeled as D1, D2, D3, and D4, respectively. Different growth parameters, such as percentage weight gain, specific growth rate (SGR), protein efficiency ratio (PER), and body length, exhibited optimum values, while feed conversion ratio (FCR) recorded the lowest value at 0.3 mg Cr₂Cl₃•6H₂O/kg diet. Moreover, a significant quadratic relationship (p < 0.001) between Cr₂Cl₃•6H₂O supplementation and SGR identified an optimal inclusion level of 0.345 mg/kg in feed formulation. The digestive enzymes amylase and protease activity increased in D2 treatment. The liver cholesterol and triglyceride levels decreased at 0.3 mg Cr₂Cl₃•6H₂O/kg, while the highest level of high-density lipoprotein (HDL) and the lowest level of low-density lipoprotein (LDL) were observed at 0.5 mg Cr₂Cl₃•6H₂O/kg in the diet. The glycogen contents increased; urea and creatine contents decreased at 0.3 mg Cr₂Cl₃•6H₂O/kg. Insulin activity increased by addition of Cr. The enzymes PEP kinase and FDPase activity increased up to 0.5 mg while G6PDH at 0.3 mg Cr₂Cl₃•6H₂O/kg; then, activity of these enzymes decreased by further addition of Cr. The minimum value of SOD was at 0.3 mg Cr₂Cl₃•6H₂O/kg, ALT and AST values at 0.5 mg Cr₂Cl₃•6H₂O/kg diet while gill ATPase activity slightly increased by increasing the Cr in fish feed. The results indicated that the optimal inclusion level of Cr₂Cl₃•6H₂O in a sorghum-based diet was 0.3–0.5 mg/kg, which supported superior growth performance, enhanced digestibility, and improved metabolic efficiency in fish.

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.

The process of manually generating question and answer (QA) pairs for assessments is known to be a time-consuming and energy-intensive task for teachers, specifically in higher education. Several studies have proposed various methods utilising pre-trained large language models for the generation of QA pairs. However, it is worth noting that these methods have primarily been evaluated on datasets that are not specifically educational in nature. Furthermore, the evaluation metrics and strategies employed in these studies differ significantly from those typically used in educational contexts. The present discourse fails to present a compelling case regarding the efficacy and practicality of stated methods within the context of higher education. This study aimed to examine multiple QA pairs generation approaches in relation to their performance and the efficacy and constraints within the context of higher education. The various approaches encompassed in this study comprise pipeline, joint, multi-task approach. The performance of these approaches under consideration was assessed on three datasets related to distinct courses. The evaluation integrates three automated methods, teacher assessments, and real-world educational evaluations to provide a comprehensive analysis. The comparison of various approaches was conducted by directly assessing their performance using the average scores of different automatic metrics on three datasets. The results of the teachers and real educational evaluation indicate that the assessments generated were beneficial in enhancing the understanding of concepts and overall performance of students. The implications of the findings from this study hold significant importance in enhancing the efficacy of QA pair generation tools within the context of higher education.

Self-regulated learning (SRL) is a cognitive ability with demonstrable significance in facilitating students’ ability to effectively strategize, monitor, and assess their own learning actions. Studies have indicated that a lack of selfregulated learning skills negatively impacts students’ academic performance. Effective data-driven feedback and action recommendations are considered crucial for SRL and significantly influence student learning and performance. However, the task of delivering personalised feedback to every student poses a significant challenge for teachers. Moreover, the task of identifying appropriate learning activities and resources for individualised recommendations poses a significant challenge for teachers, given the large number of students enrolled in most courses.

To address these challenges, several studies have examined how learning analytics-based dashboards can support students’ self-regulation. These dashboards offered several visualisations (as feedback) on student success and failure. However, while such feedback may be beneficial, it does not offer insightful information or actionable recommendations to help students improve academically. Explainable artificial intelligence (xAI) approaches have been proposed to explain such feedback and generate insights from predictive models, with a focus on the relevant actions a student needs to take to improve in ongoing courses. Such intelligent activities could be offered to students as data-driven behavioural change recommendations.

This thesis offers an xAI-based approach that predicts course performance and computes informative feedback and actionable recommendations to promote student self-regulation. Unlike previous research, this thesis integrates a predictive approach with an xAI approach to analyse and manipulate students’ learning trajectories. The aim is to offer detailed, data-driven actionable feedback to students by providing in-depth insights and explanations for the predictions provided by the approach. The technique provides students with more practical and useful knowledge compared to the predictions alone.

The proposed approach was implemented in the form of a dashboard to support self-regulation by students in university courses, and it 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.

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

When shopping for fashionable clothing items, consumers frequently experience indecision and struggle to make choices, resulting in a stalling of the purchasing process. In such scenarios, most often they need support of their friends from their social circle to choose suitable clothes for different events. To provide decision-making support, considerable research has focused on generating social-aware recommendations that incorporate input from the user’s social circle. However, there has been minimal research dedicated to develop and evaluate such systems that could assess the importance of social circles in producing social-aware fashion recommendations and identifying factors that might enhance these recommendations. This paper addresses these limitations by developing a Social Circle-Enhanced Fashion Recommendation (SCEFR) System that encompasses friends feedback to generate recommendations. The SCEFR system was evaluated by conducting a user study, comparing system-generated recommendations with user choices as rank correlation coefficients. The findings indicate that inputs from the social circle alone have limited potential in generating effective social-aware recommendations. However, when the user’s shopping preferences were shared with their social circle, the quality of these recommendations significantly improved, as evidenced by a qualitative analysis of user feedback. Furthermore, in comparative analysis with the state-of-the-art (SOTA) approaches of recommendation generation, the SCEFR system informed by user’s shopping preferences demonstrated superiority.

User experience, which encompasses users’ feelings and perceptions, is regarded as a key element in the evaluation of recommender systems. The existing literature extensively works on recommendation generation strategies with focus on the accuracy by considering objective aspects of the system. Although some of the current works considered subjective aspects of the recommendation systems from a user-centric perspective to evaluate the recommender system, however, a comprehensive analysis that could investigate factors to improve user experience was of limited focus. In this paper, we propose a methodology that provides a comprehensive multi-perspective analysis of a social-aware fashion recommender system and analyses the impact of user’s personal attributes and profiles on their experiences in various aspects of system use. A user study was conducted to realize the proposed methodology. The obtained insights highlighted that user experiences vary not only from the perspective of using a recommender system but also by varying their personal attributes (age, gender, hobby) and profiles.

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. 

Detection of epileptic seizures on the basis of Electroencephalogram (EEG) recordings is a challenging task due to the complex, non-stationary and non-linear nature of these biomedical signals. In the existing literature, a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning algorithms. Some characterizing features of epileptic and non-epileptic EEG signals overlap; therefore, it requires that analysis of signals must be performed from diverse perspectives. Few studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG signals. To pose the challenge mentioned above, in this paper, a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy classifiers. The proposed work extracts pattern features along with time-domain, frequency domain, and non-linear analysis of signals. It applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic seizures. The empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG dataset. It shows significant accuracy of 98% to 100% for normal vs. ictal classification cases while for three class classification of normal vs. inter-ictal vs. ictal accuracy reaches to above 97.5%. The obtained results for ten classification cases (including normal, seizure or ictal, and seizure-free or inter-ictal classes) prove the superior performance of proposed work as compared to other state-of-the-art counterparts.