Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 1829-1835· 0 citations· 19 references
Abstract
LMSs (Learning management systems) are widely used in educational institutions. They are software systems for cloud-based training that are offered locally, remotely, and on demand. As technological costs in higher education decrease, a significant obstacle to online learning is the high cost of creating its content. LMSs are very helpful to education during pandemics period. which significantly impacted worldwide education. In these situations, using LMSs in education offers clever substitutes for traditional classroom instruction and enables teachers to give specialized information, make use of different pedagogical approaches, and better engage their students in their studies. Globally, the current epidemic has caused unanticipated and quick transitions to remote learning and instruction, changes in both content and character. Academic performance largely depends on students' capacity to adapt and respond to disturbances. By focusing on how adaptability contributes to students' educational development and online learning, this study aims to identify factors affecting the adaptability of students in online learning scenarios. For these examinations, this paper suggests using MLTs (Machine Learning Techniques). The OLAMLTs (Online Learner Adaptability Assessment based on MLTs) suggested method evaluates aspects that influence online learners' adaptabilities to education while making recommendations for enhancements, and the recommendations achieve the highest classification accuracy when compared with other methods in evaluations.
In recent years, the rapid expansion of Virtual Learning Environments (VLEs)and online education
platforms has significantly transformed higher education.These education setups have introduced
new challenges for example increased student attrition and disengagement. Predicting student performance within these digital frameworks is essential to enabling timely interventions and improving
academic outcomes. This research addresses the prediction of high risk students by leveraging the
Open University Learning Analytics Dataset (OULAD) that comprises demographic, assessment,
and detailed engagement data for over 32,000 students. Recognizing gaps in the literature, this study
develops a robust ensemble based machine learning pipeline with Particle Swarm Optimization(PSO)
as a feature selection strategy. The results demonstrate that PSO reduced the feature space by approximately 40% while maintaining high predictive performance (F1 ≈ 0.937, AUC ≈ 0.980) in
comparison to Gini importance and Factor Analysis of Mixed Data(FAMD). Overall 94% accuracy
and AUC scores near 0.981 is achieved that is comparable to advanced deep learning benchmarks.
The inclusion of SHAP analysis improves the interpretation of the proposed scheme. This work contributes a balanced, explainable framework for educational early warning systems thereby, bridging
methodological gaps in comparative feature selection techniques and offering practical insights for
scalable deployment in online learning contexts.
Umair Abbasi, Saba Mahmood, Heba A. Fasihuddin et al.· Advances in Artificial Intel...· 0 citations
An ensemble model that combines three ML algorithms Random Forest, K-nearest Neighbors, and ADABOOST is proposed that is higher than the accuracies of the compared models and integrated through a voting mechanism.
H. Hassan, B. Mohammed, Sakar Omer Khdr et al.· 0 citations
The present paper explores some of the most important determinants of academic achievement and assesses several predictive models based on the Student Performance Factors (SPF) dataset and implies that the monitoring of attendance should become the central element of any academic early warning system.
Shang-Jia Wang· Mathematical Modeling and Al...· 0 citations
In the era of digital transformation, higher education institutions are producing enormous amounts of students' data, but many of them still do not fully utilize that data for educational insight to the benefit of student success. This gap is filled by this study developing and testing a theoretically informed AI-supported learning analytics framework in a pedagogically relevant way that goes beyond algorithmic optimization. The research design used was quantitative with a predictive correlational approach in which 1248 students from various institutes of higher education in Pakistan and abroad were involved. Data consists of LMS interaction logs, attendance records, assessment results, and self-regulation surveys that were validated. The proposed framework was tested using descriptive statistics, structural equation modeling and machine learning approaches for analyzing data. Results showed good predictive accuracy (82.4% for the identification of the at-risk students), and that self-regulated learning behaviors proved to be important mediators. The proportion of children with cycling, walking and active play increased significantly using AI-generated indicators, but demographic discrepancies remained. Practically, it recommends the universities how to implement in a responsible way that can support the value of teaching, student support, institutional decision-making, and ethical norms.
N. Aslam, Aniqa Naz, M. Nadeem et al.· Journal of Language, Literat...· 0 citations
It is suggested that ML-guided prompt routing can improve perceived relevance of instructional materials while remaining transparent and easy to calibrate and integrate interpretable ML predictions with prompt-engineered LLMs can automate individualized content generation and improve learning outcomes.
Petr Tsekoyev, T. Sembayev, Z. Nurbekova· AI@DTESI· 0 citations
In the context of the digital transformation of higher education, learning analytics technologies are a result of the personalization of learning and its improvement. In this context, the study of computer science is becoming increasingly relevant, where the development of algorithmic thinking and practical programming skills requires continuous and detailed monitoring of educational activities. The aim of the study is to develop and validate a model for predicting student educational outcomes based on learning analytics data within information science disciplines. The study utilizes educational data mining methods, machine learning algorithms (classification and regression), and statistical analysis of student interaction logs with learning management systems (LMS) and interactive programming environments. The study identified the most significant predictors of academic performance, including regular access to materials. The time spent completing practical assignments is recorded separately. The number of code compilation attempts and patterns of reference resource access form the basis for analyzing educational activity. The developed predictive model identifies at- risk students early. Accuracy in the early stages of the semester exceeds 85%. The integration of predictive analytics into the computer science curriculum shifts the focus from retrospective assessment to proactive pedagogical intervention. Individual educational trajectories are subject to timely adjustments. The study's results are applicable to the design of adaptive educational environments. They remain applicable to the development of pedagogical decision support systems and the improvement of computer science teaching methods at universities.
Nanish Khalidshaevna Nurmagomedova, B. Elezhbiev· ACCOUNTING AND CONTROL· 0 citations
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