Analyzing click data with AI: implications for student performance prediction and learning assessment

BackgroundAs the intersection of artificial intelligence (AI) and education deepens, predictive analytics using machine learning (ML) and deep learning (DL) models offer novel approaches to assessing student performance in online environments. However, challenges remain in accurately predicting high achievers and identifying students at risk due to limitations in traditional assessment models. This study explores the capabilities of these models in predicting academic achievement and highlights their potential role in reshaping educational assessment paradigms.ObjectivesTo evaluate the efficacy of various AI models—including Random Forest, XGBoost, and recurrent neural networks (RNNs)—in identifying at-risk students and differentiating levels of academic achievement, with an emphasis on inclusive and adaptive educational assessments. A key focus is on leveraging these models to create more inclusive and adaptive educational assessments.MethodsWe analyzed a dataset comprising interaction data from the Open University Learning Analytics Dataset (OULAD), which includes clickstream data on student interactions with course materials from over 32,000 students. The models were trained and evaluated using performance metrics such as accuracy, precision, recall, and F1-scores, specifically targeting predictions of student withdrawals and distinctions.ResultsThe models effectively identified students at risk of withdrawing, with the Random Forest model achieving an accuracy of 78.68% and deep learning models approximately 77%. However, accurately predicting high achievers posed challenges, suggesting a complex relationship between interaction data and academic success. This limitation underscores the need for more nuanced modeling approaches to improve predictions for top-performing students.ConclusionThis research demonstrates the promise of AI-driven models in enhancing educational assessments while also highlighting current limitations in capturing academic excellence. Our findings indicate a need for ongoing development of AI tools that are ethically designed and capable of supporting dynamic, inclusive assessment strategies. Future research should focus on incorporating additional factors, such as student motivation and study behaviors, to enhance predictive accuracy, particularly for high achievers. Such advancements can contribute to a more equitable and effective educational landscape

Coagulation Profile and Platelet Indices in Yemeni Adults with Type 2 Diabetes: A Cross-Sectional Study in Aden Governorate

Background: Type 2 diabetes mellitus (T2DM) induces a hypercoagulable state that increases thrombotic risk. Objective: This study evaluated coagulation parameters and platelet indices in Yemeni adults with T2DM and their correlation with glycemic control. Methods: A hospital-based cross-sectional study was conducted on 140 T2DM patients and 100 healthy controls from major hospitals in Aden Governorate between January and February 2025. Coagulation tests (PT, APTT) were performed using STA-R Evolution, and platelet indices (MPV, PDW) were analyzed via Sysmex XN-550. Glycemic control was assessed by HbA1c. Statistical analysis was performed using SPSS v26. Results: Most patients (86%) had poor glycemic control (HbA1c ≥ 7%). Diabetic patients demonstrated significantly prolonged prothrombin time (PT) compared with healthy controls (13.69 ± 1.74 sec vs. 12.10 ± 1.20 sec, p < 0.001), shortened APTT (31.38 ± 4.16 sec vs. 35.20 ± 3.50 sec, p < 0.001), and elevated MPV (9.03 ± 0.92 fL vs. 8.70 ± 1.10 fL, p = 0.015) and PDW (16.8 ± 2.1% vs. 15.2 ± 1.8%, p = 0.01) compared to controls. A strong positive correlation was found between HbA1c and MPV (r = 0.52, p < 0.001). An MPV cut-off > 11.5 fL predicted thrombotic risk with 78% sensitivity and 85% specificity (AUC = 0.82). Conclusion: Yemeni T2DM patients demonstrate significant hemostatic abnormalities strongly linked to poor glycemic control. MPV represents a cost-effective, accessible marker for thrombotic risk stratification. We propose its integration into routine diabetic care protocols in Yemen and similar resource-limited settings.