Search Results (133)

While prior work has explored learner behavior using learning management systems (LMS) data, few studies provide week-level clustering validated against external engagement labels. To understand and assist students in online learning platforms and environments, this study presents a week-level engagement profiling framework for e-learning environments, utilizing K-means clustering and label-based validation. Leveraging log data from 127 students over a 13-week course, 44 activity-based features were engineered to classify student engagement into high, moderate, and low levels. The optimal number of clusters (k = 3) was identified using the elbow method and assessed through internal metrics, including a silhouette score of 0.493 and R² of 0.80. External validation confirmed strong alignment with pre-labeled engagement levels based on activity frequency and weighting. The clustering approach successfully revealed distinct behavioral patterns across engagement tiers, enabling a nuanced understanding of student interaction dynamics over time. Regression analysis further demonstrated a significant association between engagement levels and academic performance, underscoring the model’s potential as an early warning system for identifying at-risk learners. These findings suggest that clustering based on LMS behavior offers a scalable, data-driven strategy for improving learner support, personalizing instruction, and enhancing retention and academic outcomes in digital education settings such as MOOCs. Full article

This paper shows that artificial intelligence is fundamentally transforming college curricula by enabling data-driven personalization, which enhances student outcomes and better aligns educational programs with evolving workforce demands. Specifically, predictive analytics, machine learning algorithms, and natural language processing were applied here, grounded in constructivist learning theory and Human–Computer Interaction principles, to evaluate student performance and identify at-risk students to propose personalized learning pathways. Results indicated that the AI-based curriculum achieved much higher course completion rates (89.72%) as well as retention (91.44%) and dropout rates (4.98%) compared to the traditional model. Sentiment analysis of learner feedback showed a more positive learning experience, while regression and ANOVA analyses proved the impact of AI on enhancing academic performance to be real. Therefore, the learning content delivery for each student was continuously improved based on individual learner characteristics and industry trends by AI-enabled recommender systems and adaptive learning models. Its advantages notwithstanding, the study emphasizes the need to address ethical concerns, ensure data privacy safeguards, and mitigate algorithmic bias before an equitable outcome can be claimed. These findings can inform institutions aspiring to adopt AI-driven models for curriculum innovation to build a more dynamic, responsive, and learner-centered educational ecosystem. Full article

Physical and mental health show strong associations with health-related behavior. University students are one of the at-risk groups who are in a vulnerable transition phase from adolescence to adulthood, significantly affecting their health-related lifestyle. This study aims to identify different groups of university students with homogeneous health-related behavior, considering their dietary habits, physical activity, alcohol and tobacco consumption, mental well-being and lifestyle change motives. For data collection, an anonymous, closed-ended paper-and-pencil questionnaire was administered to a sample of 171 university students. Data analysis was performed using descriptive statistics, a t-test for independent samples, a chi-square test, Spearman correlation and hierarchical cluster analyses (Ward’s method, Dendrogram). On average, students reported good health (M = 4.84), including daily physical activity (M = 31.35 min) and regular consumption of fruits (M = 4.02) and vegetables (M = 4.19). The hierarchical cluster analysis revealed two distinct patterns among the students: “Caring for a healthy lifestyle” (N = 69) and “Physically inactive with poor mental well-being” (N = 62). Better health-related behavior was found among male students enrolled in higher study years (p ≤ 0.01). These findings provide new insights into the different patterns of health-related behavior among university students that require targeted health promotion actions. Universities should develop and implement courses in healthy lifestyles and sustain them in the curricula. Full article

The COVID-19 pandemic significantly disrupted higher education worldwide, imposing strict isolation measures, transitioning learning online, and exacerbating existing social and economic inequalities. This literature review examines the pandemic’s impact on the mental health of college students, with a focus on those belonging to minority groups, including racial, ethnic, migrant, gender, sexuality-based, and low-income populations. While elevated levels of anxiety, depression, and loneliness were observed across all students, findings indicate that LGBTQ+ and low-income students faced the highest levels of psychological distress, due to compounded stressors such as family rejection, unsafe home environments, and financial insecurity. Racial and ethnic minority students reported increased experiences of discrimination and reduced access to culturally competent mental healthcare. International and migrant students were disproportionately affected by travel restrictions, legal uncertainties, and social disconnection. These disparities underscore the need for higher education institutions to implement targeted, inclusive mental health policies that account for the unique needs of at-risk student populations during health crises. Full article

Suicide is a major public health concern, and accurate risk assessment is essential for prevention. Slider-format questions offer a quick, intuitive, and accessible method to evaluate suicide-related dimensions. This study examines the reliability of slider-based items compared to standardized psychometric instruments when delivered via a mobile app. A total of 299 university students completed a digital self-report questionnaire using the AuxiliApp mobile platform. Participants answered validated scales assessing depression, psychological pain, suicidal ideation, anger, impulsivity, loneliness, and reasons for living, each presented in both traditional Likert and novel slider formats. Pearson correlations were used to evaluate the relationship between traditional and slider-based scores. All correlations were statistically significant (p < 0.001). Moderate correlations were found in most domains, including depression, psychological pain, suicidal ideation, loneliness, and key aspects of impulsivity and anger. Lower correlations appeared in subscales related to anger control and protective beliefs against suicide. Slider-based items demonstrated acceptable psychometric equivalence and concurrent validity compared to traditional scales. Their brevity and compatibility with mobile devices support their use in telehealth and digital mental health screening. While not a replacement for clinical evaluation, they may facilitate early detection and ongoing monitoring in at-risk populations. Full article

High dropout rates remain a significant challenge in Massive Open Online Courses (MOOCs), making early identification of at-risk students crucial. This study introduces a novel approach called In-Session Stacked Ensemble Learning for Dropout Prediction (ISELDP), which predicts student dropout during course sessions by combining multiple base learners—Adaptive Boosting (AdaBoost), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting—into a stacked ensemble with a Multi-Layer Perceptron (MLP) serving as the meta-learner. To optimise model performance, hyperparameters were tuned using Grid Search. The proposed method was evaluated under two scenarios using in-session student interaction data, one with imbalanced data and another with balanced data. Results demonstrate that ISELDP achieves an average accuracy of 88%, outperforming individual baseline models with improvements of up to 2% in accuracy and 2.4% in F1-score. Full article

Background: Lower back pain (LBP) is defined as any recurring lumbar pain between the rib cage and the buttocks present at the time of the study. This study investigated the point prevalence, associated risk factors, and degree of disability of LBP among medical students at Alfaisal University, Riyadh, Saudi Arabia. Methods: A cross-sectional study evaluated 331 medical students using the Oswestry Disability Index (ODI; used to gauge LBP degree of disability) supplemented with demographic and lifestyle questions. The respondents were mostly first-year, female, and between the ages of 17 and 21 years. Results: Analysis uncovered that Female students, extended durations of phone usage, and those who did not exercise were more likely to experience LBP (p < 0.001; p = 0.042; p = 0.001). A higher degree of disability was associated with participants older than 21 years, who used their devices for extended periods, and who slept less (β = 0.170, p = 0.006). While most students experienced LBP (73.4%), the ODI revealed that the majority were not deemed disabled (56.9%). Factors associated with LBP prevalence were not necessarily associated with a higher degree of disability per the ODI. Conclusions: LBP is highly prevalent among medical students, with several associated risk factors. Female medical students remain a significant at-risk group. These findings highlight the need for a broader intervention against LBP, such as ergonomic and lifestyle improvements that consider a multitude of factors. Full article

Artificial intelligence (AI) has found applications across diverse sectors in recent years, significantly enhancing operational efficiencies and user experiences. Educational data mining (EDM) has emerged as a pivotal AI application to transform educational environments by optimizing learning processes and identifying at-risk students. This study leverages EDM within a Moroccan university (Hassan First, University Settat, Morocco) context to augment educational quality and improve learning. We introduce a novel “Hybrid approach” that synthesizes students’ historical academic records and their in-class behavioral data, provided by instructors, to predict student performance in initial coding courses. Utilizing a range of machine learning (ML) algorithms, our research applies multi-classification, data augmentation, and binary classification techniques to evaluate student outcomes effectively. The key performance metrics, accuracy, precision, recall, and F1-score, are calculated to assess the efficacy of classification. Our results highlight the long short-term memory (LSTM) algorithm’s robustness achieving the highest accuracy of 94% and an F1-score of 0.87 along with a support vector machine (SVM), indicating high efficacy in predicting student success at the onset of learning coding. Furthermore, the study proposes a comprehensive framework that can be integrated into learning management systems (LMSs) to accommodate generational shifts in student populations, evolving university pedagogies, and varied teaching methodologies. This framework aims to support educational institutions in adapting to changing educational dynamics while ensuring high-quality, tailored learning experiences for students. Full article

Rising mental health challenges among postsecondary students have increased the demand for scalable, ethical solutions. This paper presents the design, development, and safety evaluation of Luna, a GPT-4-based mental health chatbot. Built using a modular PHP architecture, Luna integrates multi-layered prompt engineering, safety guardrails, and referral logic. The Institutional Review Board (IRB) at the University of Detroit Mercy (Protocol #23-24-38) reviewed the proposed study and deferred full human subject approval, requesting technical validation prior to deployment. In response, we conducted a pilot test with a variety of users—including clinicians and students who simulated at-risk student scenarios. Results indicated that 96% of expert interactions were deemed safe, and 90.4% of prompts were considered useful. This paper describes Luna’s architecture, prompt strategy, and expert feedback, concluding with recommendations for future human research trials. Full article

Background: The suicide rate among Iraqis is rising, with many analysts attributing it to political instability, exposure to trauma, economic hopelessness, social stigma surrounding mental health as well as cultural and societal pressures. However, the prevalence of suicidal ideation and associated risk factors in Iraqi youth is unknown, requiring urgent attention and effective public health initiatives. Thus, the aim of this study was to explore rates of suicidal ideation and associated risk factors among female secondary school students in Baghdad, Iraq. Method: A cross-sectional study was conducted, utilising quantitative survey data collected in four girls’ secondary schools across Baghdad, Iraq, between August and December 2023. The survey consisted of questions relating to their demographic characteristics (age, gender, school) and a series of measures pertaining to participants’ levels of suicidal ideation, as well as factors commonly identified in the literature as predictors of suicide. Results: Four-hundred and two female participants took part. Participants were aged between 13 and 17 years (M = 15.50; SD = 1.22). In total 11.3% of the students scored in the at-risk range for suicidal behaviour and only 20.1% (n = 91) said they had not had some thoughts of suicide in the previous two weeks. Previous diagnoses of anxiety, high levels of depression and hopelessness, and poor quality of life were significant risk factors for suicidal ideation. On average, students reported moderate levels of depression and high levels of hopelessness. Conclusions: Female Iraqi secondary school students experience high levels of suicidality, alongside several other known risk factors for suicide ideation. However, a limitation of this study is that cross-sectional designs limit causal interpretation. Findings emphasise the importance of developing targeted school-based interventions to support students’ mental health. Increasing research and attention in this area is vital to not only improving the mental health of students in Iraq but also reducing the stigma around mental health and suicide. Future policies should include specific mental health support for those young people affected by conflict, displacement and family loss, integrating trauma-informed care into both mental health and educational services. Full article

Educational data mining (EDM) is instrumental in the early detection of students at risk of academic underperformance, enabling timely and targeted interventions. Given that many undergraduate students face challenges leading to high failure and dropout rates, utilizing EDM to analyze student data becomes crucial. By predicting academic success and identifying at-risk individuals, EDM provides a data-driven approach to enhance student performance. However, accurately predicting student performance is challenging, as it depends on multiple factors, including academic history, behavioral patterns, and health-related metrics. This study aims to bridge this gap by proposing a deep learning model to predict student academic performance with greater accuracy. The approach combines a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) network to enhance predictive capabilities. To improve the model’s performance, we address key data preprocessing challenges, including handling missing data, addressing class imbalance, and selecting relevant features. Additionally, we incorporate optimization techniques to fine-tune hyperparameters to determine the best model architecture. Using key performance metrics such as accuracy, precision, recall, and F-score, our experimental results show that our proposed model achieves improved prediction accuracy of 97.48%, 90.90%, and 95.97% across the three datasets. Full article

The rapid increase in educational data from diverse sources such as learning management systems and assessment records necessitates the application of advanced analytical techniques to identify at-risk students and address persistent issues like dropout rates and academic underperformance. However, many existing models struggle with generalizability and fail to effectively manage data challenges such as class imbalance and missing data, leading to suboptimal predictive performance. This study proposes a hybrid deep learning model combining convolutional neural networks (CNN) and long short-term memory (LSTM) networks to improve the accuracy of student academic performance prediction and enable timely educational interventions. To improve the performance of the model, we incorporate feature selection techniques and optimization strategies to enhance reliability. We also address common preprocessing challenges such as missing data and data imbalance. The proposed model was evaluated on two benchmark datasets to ensure model generalization capability. The hybrid model achieved predictive accuracies of 98.93% and 98.82% on the two datasets, respectively, outperforming traditional machine learning models and standalone deep learning approaches across key performance metrics including accuracy, precision, recall, and F-score. Full article

School dropout in higher education remains a significant global challenge with profound socioeconomic consequences. To address this complex issue, educational institutions increasingly rely on business intelligence (BI) and related predictive analytics, such as machine learning and data mining techniques. This systematic review critically examines the application of BI and predictive analytics for analyzing and preventing student dropout, synthesizing evidence from 230 studies published globally between 1996 and 2025. We collected literature from the Google Scholar and Scopus databases using a comprehensive search strategy, incorporating keywords such as “business intelligence”, “machine learning”, and “big data”. The results highlight a wide range of predictive tools and methodologies, notably data visualization platforms (e.g., Power BI) and algorithms like decision trees, Random Forest, and logistic regression, demonstrating effectiveness in identifying dropout patterns and at-risk students. Common predictive variables included personal, socioeconomic, academic, institutional, and engagement-related factors, reflecting dropout’s multifaceted nature. Critical challenges identified include data privacy regulations (e.g., GDPR and FERPA), limited data integration capabilities, interpretability of advanced models, ethical considerations, and educators’ capacity to leverage BI effectively. Despite these challenges, BI applications significantly enhance institutions’ ability to predict dropout accurately and implement timely, targeted interventions. This review emphasizes the need for ongoing research on integrating ethical AI-driven analytics and scaling BI solutions across diverse educational contexts to reduce dropout rates effectively and sustainably. Full article

Digital pedagogies have significant potential to enhance classroom learning, and teachers are increasingly seeking ways to integrate these approaches. Combining video with drama provides students with opportunities to explore technology while expressing themselves through dramatic performance. This article presents a qualitative case study exploring the use of creative videomaking as a literacy strategy in an upper primary class at a high-diversity Australian school. The research explored different forms of literacy involved in collaborative videomaking, as well as benefits and challenges associated with this approach. Thematic analysis of observations, interviews, and student videos identified collaborative drama and videomaking as an engaging and inclusive pedagogy for diverse learners. Benefits included a strong focus on oral and visual communication and an authentic use of digital technologies. Written literacy would have benefitted from separate sessions targeting scriptwriting, however, and although critical digital topics captured students’ interest, these also needed more time than was allocated. Other challenges included increased self-consciousness for some students when recording their voices, limitations of filming in a classroom, and additional time needed for lesson preparation. Further findings showed drama strategies were particularly useful for improving at-risk students’ confidence and sense of identity as learners and speakers of English. Overall, integrating videomaking into literacy instruction effectively fostered multimodal and technological literacy, creativity, and identity for diverse students. Full article