Building safeguards against illegitimate access and authentication is a cornerstone for securing systems. Existing user authentication schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. A range of research proposals have suggested continuous multimodal biometric authentication (CMBA) systems as a reliable solution. Though contemporary authentication systems have the potential to change their current authentication scheme, there is a lack of critical analysis of current progress in the field to foster and influence practical solutions. This paper provides a systematic survey of existing literature on CMBA systems, followed by analysis to identify and discuss current research and future trends. The study has found that many diverse biometric characteristics are used for multimodal biometric authentication systems. The majority of the studies in the literature reviewed apply supervised learning approaches as a classification technique, and score level fusion is predominantly used as a fusion model. The review has determined however that there is a lack of comparative analysis on CMBA design in terms of combinations of biometric types (behavioural only, physiological only, or both), machine learning algorithms (unsupervised learning and semi-supervised learning), and fusion models. Most of the studies evaluated a CMBA system’s accuracy functionality, such as False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER). However, security, scalability and usability (user acceptance and satisfaction) are generally not addressed thoroughly even though these are key factors for system success in a real deployment. Furthermore, a CMBA system should be implemented and evaluated extensively on real data without restriction to prove that such systems are feasible.

Student performance predictive analysis has played a vital role in education in recent years. It allows for the understanding students’ learning behaviours, the identification of at-risk students, and the development of insights into teaching and learning improvement. Recently, many researchers have used data collected from Learning Management Systems to predict student performance. This study investigates the potential of clickstream data for this purpose. A total of 5341 sample students and their click behaviour data from the OULAD (Open University Learning Analytics Dataset) are used. The raw clickstream data are transformed, integrating the time and activity dimensions of students’ click actions. Two feature sets are extracted, indicating the number of clicks on 12 learning sites based on weekly and monthly time intervals. For both feature sets, the experiments are performed to compare deep learning algorithms (including LSTM and 1D-CNN) with traditional machine learning approaches. It is found that the LSTM algorithm outperformed other approaches on a range of evaluation metrics, with up to 90.25% accuracy. Four out of twelve learning sites (content, subpage, homepage, quiz) are identified as critical in influencing student performance in the course. The insights from these critical learning sites can inform the design of future courses and teaching interventions to support at-risk students.

Due to the exponential growth of online information, users are often welcomed with a huge range of products and services along with descriptions, reviews, and comments. Although this information available to users is valuable, at the same time, massive data sources confuse them to retrieve desired content, which is known as information overload. Recommender systems are examined as effective tools that play a vital role in filtering information and ultimately addressing the information overload problem. Various online platforms use recommendation systems to provide users with more relevant and personalized content. With the remarkable success of deep learning in the field of artificial intelligence, it procures much attention in the recommendation research area in recent years. The exiting literature on recommender system research commonly distinguishes between two main directions: rating prediction and top-N ranking. In this survey paper, we examine deep learning methodologies in the context of both rating prediction and top-N ranking recommendation approaches. Additionally, we investigate pre- and post-modeling critiques of recommender systems and provide insights into exiting benchmark datasets, feature learning analysis, and evaluation measuring techniques. In the end, we highlight the new generation recommender system trend with respective future research directions.

Student persistence and retention in STEM disciplines is an important yet complex and multi-dimensional issue confronting universities. Considering the rapid evolution of online pedagogy and virtual learning environments, we must rethink the factors that impact students’ decisions to stay or leave the current course. Learning analytics has demonstrated positive outcomes in higher education contexts and shows promise in enhancing academic success and retention. However, the retention factors in learning analytics practice for STEM education have not been fully reviewed and revealed. The purpose of this systematic review is to contribute to this research gap by reviewing the empirical evidence on factors affecting student persistence and retention in STEM disciplines in higher education and how these factors are measured and quantified in learning analytics practice. By analysing 59 key publications, seven factors and associated features contributing to STEM retention using learning analytics were comprehensively categorised and discussed. This study will guide future research to critically evaluate the influence of each factor and evaluate relationships among factors and the feature selection process to enrich STEM retention studies using learning analytics.

This study aimed to identify factors influencing student engagement in online and blended courses at one Australian regional university. It applied a data science approach to learning and teaching data gathered from the learning management system used at this university. Data were collected and analysed from 23 subjects, spanning over 5500 student enrolments and 406 lecturer and tutor roles, over a five-year period. Based on a theoretical framework adapted from Community of Inquiry (CoI) framework by Garrison et al. (2000), the data were segregated into three groups for analysis: Student Engagement, Course Content and Teacher Input. The data analysis revealed a positive correlation between Student Engagement and Teacher Input, and interestingly, a negative correlation between Student Engagement and Course Content when a certain threshold was exceeded. The findings of the study offer useful suggestions for future course design, and pedagogical approaches teachers can adopt to foster student engagement.