This paper presents a comprehensive analysis of student behaviour on Moodle, a widely used Learning Management System (LMS), in a large university setting with over 2000 students. The study investigates the distributi...
This paper presents a comprehensive analysis of student behaviour on Moodle, a widely used Learning Management System (LMS), in a large university setting with over 2000 students. The study investigates the distribution of student access to the LMS relative to the deadlines of quizzes (self-checked). Further, it explores the correlation between student engagement with course materials and their academic performance, finding that students with higher grades tend to access course materials earlier and more frequently before quiz deadlines. The paper also examines the distribution of two different student engagement with quizzes across different weekdays, highlighting distinct patterns between weekdays and weekends, and a unique trend on Saturdays due to lecture schedules for two study types - full-time and part-time. These findings provide valuable insights into student learning behaviours on large LMS, with potential implications for enhancing teaching strategies and improving student outcomes. The paper contributes to the growing body of research on learning analytics and offers a foundation for future studies in this area.
Brain age estimation involves predicting an individual’s biological age from their brain images, which offers valuable insights into the aging process and the progression of neurodegenerative diseases. Conducting lar...
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Due to inconvenience and time wasting factor in traditional trolleys employed in shopping malls, markets and shopping complexes etc, we have come up with the novel solution named as RoboTrolley i.e. an intelligent cus...
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Traditional methods of sensitive data protection are static and reactive, which makes them often inadequate in the dynamic and always-shifting cyber threat environment of today. The paper proposes a dynamic and adapta...
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During the years 2020, 2021, and partially 2022, the COVID-19 virus ran rampant across the globe, causing devastating effects on the masses. Using data mining techniques, we explored factors linked to severe cases of ...
Activation-based conditional inference (ActInf) combines conditional reasoning and ACT-R, a cognitive architecture developed to formalize human rea-soning, and therewith provides a powerful inference formalism which m...
Synthesis of bulletproof strategies in imperfect information scenarios is a notoriously hard problem. In this paper, we suggest that it is sometimes a viable alternative to aim at "reasonably good" strategie...
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Machine learning is a branch of artificial intelligence (AI) that trains a machine to think as humans. Machine learning algorithms need large amounts of data for high-value predictions which helps to get better decisi...
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This work-In-Progress employs Participatory Action Research (PAR), with an emphasis on collaboration, reflexivity and democratic process, to develop sustainable learning/transferable skills (e.g. evaluative judgment) ...
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The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingfirm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new ***...
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The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingfirm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new *** techniques for identifying and detecting worker performance in industrial applications are based on computer vision *** widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is *** the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction *** suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker *** proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work *** addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed *** an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalfindings revealed that the suggested model could accurately recognize the actions of construction ***,we examined the impact of window size and sensor position on the identification efficiency of the proposed method.
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