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In recent years, studies have shown that machine learning significantly improves student performance and retention and reduces the risk of student dropout and withdrawal. However, there is a lack of empirical research reviews focusing on the application of machine learning to predict student performance in terms of learning engagement and self-efficacy and exploring their relationships. Hence, this paper conducts a systematic research review on the application of machine learning in higher education from an empirical research perspective.

This systematic review examines the application of machine learning (ML) in higher education, focusing on predicting student performance, engagement and self-efficacy. The review covers empirical studies from 2016 to 2024, utilizing a PRISMA framework to select 67 relevant articles from major databases.

The findings show that ML applications are widely researched and published in high-impact journals. The primary functions of ML in these studies include performance prediction, engagement analysis and self-efficacy assessment, employing various ML algorithms such as decision trees, random forests, support vector machines and neural networks. Ensemble learning algorithms generally outperform single algorithms regarding accuracy and other evaluation metrics. Common model evaluation metrics include accuracy, F1 score, recall and precision, with newer methods also being explored.

First, empirical research literature was selected from only four renowned electronic journal databases, and the literature was limited to journal articles, with the latest review literature and conference papers published in the form of conference papers also excluded, which led to empirical research not obtaining the latest views of researchers in interdisciplinary fields. Second, this review focused mainly on the analysis of student grade prediction, learning engagement and self-efficacy and did not study students’ risk, dropout rates, retention rates or learning behaviors, which limited the scope of the literature review and the application field of machine learning algorithms. Finally, this article only conducted a systematic review of the application of machine learning algorithms in higher education and did not establish a metadata list or carry out metadata analysis.

The review highlights ML’s potential to enhance personalized education, early intervention and identifying at-risk students. Future research should improve prediction accuracy, explore new algorithms and address current study limitations, particularly the narrow focus on specific outcomes and lack of interdisciplinary perspectives.

This study aims to develop a low-cost additive-subtractive hybrid machine equipped with reverse scanning to fabricate high-precision dental surgical guides. The major focus of developing the hybrid additive manufacturing technology is to achieve clinical precision of dental tools at an affordable price.

The designed machine consists of a self-developed vat-photopolymerization-based 3D printer that can fabricate dental surgical guides. The 3D printer is integrated with a self-developed 3D scanner that will analyze the fabricated part and evaluate the dimensional discrepancies. Based on the data provided by the scanner, the integrated secondary milling process will successfully machine the part to meet the clinical precision and standard.

The efficacy of the newly developed hybrid machine is demonstrated with the fabrication of complex part, lower and upper dental surgical guides with the mean dimensional deviations of 198.1, 136.6 and 117.9 µm, respectively. The integration of the secondary scanning and machining system successfully enhanced the mean dimensional deviation of upper and lower guides by 11.88% and 28.75%, respectively. Furthermore, this study also benchmarked the dimensional accuracies achieved by this low-cost technology with the high-end commercial 3D printers. The overall cost of the machine is evaluated to be $2,399.

This paper proposes a novel hybrid additive manufacturing process with integrated reverse scanning and machining modules to fabricate high-precision dental guides. The developed machine is a low-cost alternative to the existing high-end commercial counterparts. The developed machine has the potential to make endodontic treatments more affordable.

Autoethnography as a methodology has proved increasingly attractive to higher education researchers in recent years, particularly those in marginalized positions. This article examines the extant research literature, focusing on the origins and meaning of the approach, how it has been applied in practice and the issues and critiques that have been raised. It concludes that collaborative forms of autoethnography probably offer the best way forward.

The paper investigates factors that contribute to small and medium enterprises (SMEs) surviving major exogenous shocks. Global crises such as the coronavirus disease 2019 (COVID-19) have increasingly heightened scholarly interest in post-crises responses. However, studies that compare the relative responses to external shocks and the outcomes for SMEs operating in different institutional settings are limited. We examine the relative degrees of success European and African SMEs experienced in avoiding the worst consequences of the COVID-19 pandemic.

We use the resource dependency theory (RDT) with variants of institutional theory since the RDT has been shown in practice to have greater explanatory power when used in this way. We augment our framework with a feminist theory dimension. To test our hypotheses, we apply regression analyses using cross-sectional data from the World Bank Enterprise Surveys (WBES), which include responses from private enterprises in 26 European and eight African countries. We construct our data by combining the COVID-19 follow-up surveys with relevant firm-specific characteristics from the WBES conducted in 2020–2021 using firm-specific unique IDs. After removing the firms with missing observations, the sample number of SMEs is 15,178.

Our empirical findings support the theoretically posited positive effects of innovativeness, institutional connectedness and governance capability on SMEs’ survival in the face of external shocks. Further, we confirm the importance of firm-specific characteristics (financial status, size and age) for SMEs’ survival. Female-owned SMEs are more likely to suffer during COVID-19, especially in Africa. The results are more nuanced when we consider industry specificity and heterogeneity of government support.

Our article helps answer the theoretical (and policy-relevant) question of whether SMEs that are resilient to major exogenous shocks may share certain characteristics despite operating in different institutional environments. If that is so, then it may be that lessons from one continent may have at least some relevance for the other. Our approach’s broad value lies in its capacity to test the degree to which established bodies of theory developed in the Northern Hemisphere may be deployed in Africa, well beyond the contexts which provided their initial empirical basis. This paper also contributes to the literature on the effect of environmental-change shocks on entrepreneurship performance outcomes.

This study aims to investigate the impact of client size, client risk, client profitability, client complexity, audit reporting lag, client loss, audit firm size, and industry type on determining external auditor fees pre- and during the COVID-19 pandemic on the non-financial companies (53 industrial companies and 41 service companies) listed at the Amman Stock Exchange (ASE) for the period of 2017–2021. The initial number of observations started with 470 observations. The results of the regression analysis for the pooled sample revealed a significant positive impact for the factors of client size, client complexity, and audit firm size on external audit fees. The same results were found for the other two sub-samples. However, client loss and industry type indicated a significant negative impact on external audit fees, except for the pre-COVID-19 pandemic period for the loss and the COVID-19 pandemic period for the audit firm size. Moreover, client risk, audit report lag, and client profitability have an insignificant impact on external audit fees for all three samples. Furthermore, the study recommends the following: Allocate adequate resources for auditing and consider the influence of company size on audit fees, adjust audit budgets based on profitability and the potential complexity of financial statements, and analyze and communicate the level of complexity to auditors, especially for companies operating in multiple industries or with intricate structures.

This cross-sectional investigation explores the relative importance of specific psychological factors influencing adjustment in a sample of internationally mobile children and adolescents, commonly referred to as third culture kids (TCKs), living in Switzerland.

The study cohort comprised 126 participants aged 7–17 years, all of whom had experienced international relocations due to their parents' professional commitments. Participants were recruited from local and international schools in Switzerland, and data were collected through validated online questionnaires after obtaining informed consent. The study focused on proximal psychological factors, including emotion regulation strategies (such as cognitive reappraisal and emotion suppression), negative social cognition, resilience and perceived stress, assessing their impact on psychological adjustment. Outcomes were measured through indicators of well-being and mental health, including emotional and behavioral symptoms.

Commonality analysis was used to evaluate the relative contributions of each psychological factor to well-being and mental health in the sample. Perceived stress emerged as the most significant contributor to well-being, while resilience played a crucial buffering role against mental-health difficulties. These findings are consistent with Berry’s Acculturative Stress Model, which highlights the importance of managing stress in the context of cultural transitions. Additionally, the presence of well-established expatriate support networks in Switzerland may have mitigated the negative effects of stress, suggesting that similar patterns may not hold in less resource-rich environments.

The study offers important insights into the challenges faced by TCKs during international relocations, underscoring the need for further cross-cultural research. It remains essential to examine whether emotion regulation strategies such as cognitive reappraisal and resilience mechanisms function similarly in different cultural and socioeconomic contexts.

The findings also suggest the potential benefits of tailored psychotherapeutic interventions aimed at enhancing the well-being and adaptive capacity of TCKs.

Overall, this research contributes valuable knowledge to the understanding of the psychological mechanisms that can inform strategies for supporting TCKs as they navigate the complexities of global mobility.

This chapter delves into the biopsychosocial reasons behind offending behaviour. It provides an overview of key theories from the biological, psychological and social perspectives in psychology and criminology. The discussion includes different viewpoints on why people offend, focusing on Compassion-Focused Therapy and positive psychology. It explores how these approaches contribute to our understanding of offending behaviour.

This study classifies and estimates safety costs in Arctic shipping.

Based on the literature review, the paper details shipping prevention costs into five categories (infrastructure and facilities, measures, technologies, personnel and management) and groups marine accidents into injury and death, property damage, environmental damage and others.

The proposed classification and estimation allow for a comparison of prevention costs and accident expenses. Estimating safety expenses in the Arctic presents challenges, such as data inadequacies. However, the method has been tested with data provided by an Arctic ship operator.

Thus, shipping companies can verify the effectiveness of their investments and reorientate whenever necessary, becoming a decision-support system to best allocate safety investments. Combined with company safety performance, the tool can help identify the safety areas requiring enhanced attention.

This paper presents the first classification and a tool to assess safety costs in relation to Arctic shipping, potentially supporting safety investment decisions.