The swelling use of computerized learning, accompanied by the rapid growth of information technology has become a surge of interest in the research community. Consequently, several technologies have been developed to maintain and promote computerized learning. In this study, we provided an in-depth analysis of two of the prominent computerized learning systems i.e., Intelligent Tutoring System (ITS) and Affective Tutoring System (ATS). An ITS is one of the training software systems, which use intelligent technologies to provide personalized learning content to students based on their learning needs with the aim of enhancing the individualized learning experience. Recently, researchers have demonstrated that the affect or emotional states of a student have an impact on the overall performance of his/her learning, which introduces a new trend of ITS development termed as ATS, which is the extended research of the previous one. Although there have been several studies on these tutoring systems, however, none of them has comprehensively analyzed both systems, particularly the transition from ITS to ATS. Therefore, this study examines these two tutoring systems more inclusively with regards to their architectures, models, and techniques and approaches used by taking into consideration the related researches conducted between 2014 to 2019. A crucial finding from the study is that ATS can be a promising tutoring system for the next generation learning environment by affiliating proper emotion recognition channels, along with computational intelligence approaches. Finally, this study concludes with research challenges and possible future directions and trends.

The devastating COVID-19 pandemic forced academia to go virtual. Educational institutions around the world have stressed online learning programs in the aftermath of the pandemic. However, because of insufficient access to Information Communication Technology (ICT), a substantial number of students failed to harness the opportunity of online learning. This study explores the latent digital divide exhibited during the COVID-19 pandemic while online learning activities are emphasised among students of Bangladesh. It also investigates the digital divide exposure and the significant underlying drivers of the divide. In order to accomplish this, a cross-sectional survey was employed to collect quantitative data, mixed with open-ended questions were utilised to gather qualitative information from the student community. The findings revealed that despite the majority of students having physical access to ICT, only 32.5% of students could attend online classes seamlessly. In total, 34.1% reported data prices as the critical barrier, while 39.8% identified poor network infrastructure as the significant barrier preventing them from participating in online learning activities. Although most students possess physical access to various devices and the Internet, they face the first-level digital divide due to the quality of access and price of cellular Internet. Consequently, they fail to take advantage of physical access, resulting in the third-level digital divide (utility gap), which submerges them into a digital divide cycle. The primary objective of this study is to explore the underlying issues of the digital divide among Bangladeshi students. This will assist relevant stakeholders (e.g., the Bangladesh government, educational institutions, and researchers) to gain necessary insights and theoretical understanding of underlying limitations and emergent directions to arrange adequate support for underprivileged students to undertake essential online learning activities.

Spatial Crowdsourcing (SC) is a new paradigm of crowdsourcing applications. Unlike traditional crowdsourcing, SC outsources tasks to distributed potential workers, and those who accept the task are required to travel to a predefined location to complete it. Currently, the primary aim of SC is to maximize the number of matched tasks or to minimize the travelling distances of the workers. However, less focus is given in matching the right tasks to the right workers, particularly in a heterogeneous tasks environment. To address this lacking, our work provides an efficient framework for selecting optimal workers for every task with various specification (geographical proximity, domain types, and expiration times), based on workers’ attributes (task domain-specific knowledge, expertise or performance history, distance to task location, and task workload distribution). We introduce the use of Bayesian Network in modelling and selecting optimal workers, and use k-medoids partitioning technique for tasks clustering and scheduling. Our experimental results on both synthetic and real-world large datasets have shown that our proposed approach has outperformed other baseline approaches, in terms of low average error rate and fast execution time.

Dental impression tray is frequently used in dentistry to record the patient’s oral structure for clinical oral diagnosis and treatment planning. Manual procedure of taking impressions is costly, time-consuming, and additionally, no research has been done on selecting dental impression tray from dental arch images using computer vision in real-life scenarios. In this spirit, an intelligent model is proposed based on computer vision and machine learning to select appropriate dental impression trays from maxillary arch images. A dataset of 52 patients’ maxillary arch images have been acquired and various sets of features such as colors, textures, and shapes of the images were extracted to better characterize the maxillary arch images. Considering the importance of the features in describing the maxillary arch object and to improve the classification performance, a method based on multi-feature fusion with ensemble classifier is proposed. Besides, the performance of a deep learning based multilayer perceptron neural network is also investigated. The proposed multi-feature fusion with ensemble classifier attained 92.31% precision, 91.75% recall, 91.75% accuracy, respectively, on the dataset, which clearly establishes the feasibility of the proposed model. An illustration of a real-life application of the proposed model is also provided.

Student dropout rates continue to present major difficulties for educational institutions, leading to academic, operational, and financial impacts. Educational Data Mining (EDM) methods, particularly those combining clustering techniques with predictive models, have demonstrated potential in identifying at-risk students early and accurately. This systematic review explores how cluster-based prediction models have been applied in educational contexts to enhance student performance prediction. A total of sixty-one relevant studies published between 2010 and 2025 were selected and analysed using PRISMA guidelines. The review focuses on the clustering techniques used, how these are integrated with predictive models, and what types of student data are involved. Key findings show that cluster-based models help capture behavioural and academic differences among students, which enables educational institutions to provide more adaptable support. The review also highlights challenges related to generalisability, scalability, and ethical concerns, especially when applying models across different institutions or datasets. The main contribution of this study is the identification of how clustering can be used not only to segment student populations but also to improve prediction accuracy by tailoring models to each subgroup. This review contributes to the literature by emphasising the practical benefits of cluster-based predictive modelling and providing clear directions for further studies aimed at reducing student dropout through targeted support.

Learning can generally be categorised into three domains, which include cognitive (thinking), affective (emotions or feeling) and psychomotor (physical or kinesthetic). In the learner model, acknowledging the affective aspects of learning is important for a range of learner outcomes, including motivation, persistence, and engagement. Learners’ affective states can be detected using physical (e.g. cameras) and physiological sensors (e.g., EEG) in online learning. Although these detectors demonstrate high accuracy, they raise privacy concerns for learners and present challenges in deploying them on a large scale to larger groups of students or in classroom settings. Consequently, researchers have designed an alternative method that can recognise students’ affective states at any point during online learning from their interaction with a computer-based learning platform (i.e. intelligent tutoring systems) without using any sensors. Existing sensor-free affect detectors however, are less accurate and not directly generalisable to other domains and systems. This research focuses on developing generalisable sensor-free affect detectors to identify students’ frustration during online learning using machine learning classifiers. The detectors were built by identifying minimal optimal features associated with frustration from the high-dimensional feature space through a series of experiments on a real-world students’ affective dataset, which are generalisable across various learning platforms and domains. To evaluate their accuracy and generalisability, the detectors’ performance was validated on two independent datasets collected from different educational institutions. The experimental results show that cost-sensitive Bayesian classifiers can achieve higher affect detection accuracies with a small number of generalisable features compared to other classifiers.