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Kelly Linden; Neil van der Ploeg; Noelia Roman – Journal of Higher Education Policy and Management, 2023
There is a small window of opportunity at the beginning of semester for a university to provide commencing students with timely and targeted support. However, there is limited information available on interventions that identify and support disengaged students from equity groups without using equity group status as the basis for the contact. The…
Descriptors: Learner Engagement, Identification, Intervention, Learning Analytics
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Abdelhafez, Hoda Ahmed; Elmannai, Hela – International Journal of Information and Communication Technology Education, 2022
Learning data analytics improves the learning field in higher education using educational data for extracting useful patterns and making better decisions. Identifying potential at-risk students may help instructors and academic guidance to improve the students' performance and the achievement of learning outcomes. The aim of this research study is…
Descriptors: Learning Analytics, Mathematics, Prediction, Academic Achievement
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Alcaraz, Raul; Martinez-Rodrigo, Arturo; Zangroniz, Roberto; Rieta, Jose Joaquin – IEEE Transactions on Learning Technologies, 2021
Early warning systems (EWSs) have proven to be useful in identifying students at risk of failing both online and conventional courses. Although some general systems have reported acceptable ability to work in modules with different characteristics, those designed from a course-specific perspective have recently provided better outcomes. Hence, the…
Descriptors: Prediction, At Risk Students, Academic Failure, Electronic Equipment
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Wells, Jason; Spence, Aaron; McKenzie, Sophie – Journal of Information Technology Education: Research, 2021
Aim/Purpose: This paper focuses on understanding undergraduate computing student-learning behaviour through reviewing their online activity in a university online learning management system (LMS), along with their grade outcome, across three subjects. A specific focus is on the activity of students who failed the computing subjects. Background:…
Descriptors: Student Participation, Undergraduate Students, At Risk Students, Academic Failure
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Li, Jiawei; Supraja, S.; Qiu, Wei; Khong, Andy W. H. – International Educational Data Mining Society, 2022
Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions. We propose the use of text mining to extract semantic, syntactic, and frequency-based features from…
Descriptors: Course Descriptions, Learning Analytics, Academic Achievement, Prediction
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Floris, Francesco; Marchisio, Marina; Sacchet, Matteo; Rabellino, Sergio – International Association for Development of the Information Society, 2020
Open Online Courses can serve different purposes: in the case of Orient@mente at the University of Torino, they aim at facilitating the transition from secondary to tertiary education with automatic evaluation tests that students can try in order to understand their capabilities in - and their attitude towards - certain disciplines, and with…
Descriptors: Learning Analytics, Academic Failure, Universities, Student Adjustment
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Bezerra, Luis Naito Mendes; Silva, Márcia Terra – International Journal of Distance Education Technologies, 2020
In the current context of distance learning, learning management systems (LMSs) make it possible to store large volumes of data on web browsing and completed assignments. To understand student behavior patterns in this type of environment, educators and managers must rethink conventional approaches to the analysis of these data and use appropriate…
Descriptors: Learning Analytics, Data Collection, Class Size, Online Courses
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Archer, Elizabeth; Prinsloo, Paul – Assessment & Evaluation in Higher Education, 2020
Assessment and learning analytics both collect, analyse and use student data, albeit different types of data and to some extent, for various purposes. Based on the data collected and analysed, learning analytics allow for decisions to be made not only with regard to evaluating progress in achieving learning outcomes but also evaluative judgments…
Descriptors: Learning Analytics, Student Evaluation, Educational Objectives, Student Behavior
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Georgakopoulos, Ioannis; Chalikias, Miltiadis; Zakopoulos, Vassilis; Kossieri, Evangelia – Education Sciences, 2020
Our modern era has brought about radical changes in the way courses are delivered and various teaching methods are being introduced to answer the purpose of meeting the modern learning challenges. On that account, the conventional way of teaching is giving place to a teaching method which combines conventional instructional strategies with…
Descriptors: Academic Failure, Blended Learning, Learner Engagement, Student Participation
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Delmas, Peggy M.; Childs, Tracey N. – Innovations in Education and Teaching International, 2021
With increasing financial pressures on colleges and universities, student retention and persistence have become high priorities. Faculty play an important role in student retention, particularly in providing early alerts. Early alert systems (EAS), which are mechanisms that allow faculty and professional staff to notify students and advisors that…
Descriptors: Educational Finance, Academic Persistence, College Faculty, Teacher Role