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Qin Ni; Yifei Mi; Yonghe Wu; Liang He; Yuhui Xu; Bo Zhang – IEEE Transactions on Learning Technologies, 2024
Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this…
Descriptors: Cognitive Style, Electronic Learning, Prediction, Identification
Natasha Arthars; Kate Thompson; Henk Huijser; Steven Kickbusch; Samuel Cunningham; Gavin Winter; Roger Cook; Lori Lockyer – Australasian Journal of Educational Technology, 2024
Assessing group work formatively in higher education poses a significant challenge. The complexity of evaluating individual contributions is compounded by the lack of efficient and effective methods for tracking, analysing and assessing individual engagement and contributions, which can impede timely feedback and the development of group work…
Descriptors: Formative Evaluation, Cooperative Learning, College Students, Student Evaluation
Paul Prinsloo; Mohammad Khalil; Sharon Slade – British Journal of Educational Technology, 2024
Students' physical and digital lives are increasingly entangled. It is difficult to separate students' "digital" well-being from their offline well-being given that artificial intelligence increasingly shapes both. Within the context of education's fiduciary and moral duty to ensure safe, appropriate and effective digital learning spaces…
Descriptors: Educational Technology, Technology Uses in Education, Well Being, Artificial Intelligence
Jenay Robert – EDUCAUSE, 2024
Increasingly, data collection and analysis are core functions of higher education institutions. However, an EDUCAUSE QuickPoll revealed that just one in four (25%) of respondents believed the structure of data functions at their institution was ideal for their analytics needs, and only 16% of respondents indicated that their institutional data…
Descriptors: Higher Education, Learning Analytics, Data Collection, Privacy
Parkes, Sarah; Benkwitz, Adam; Bardy, Helen; Myler, Kerry; Peters, John – Higher Education Research and Development, 2020
Universities are now compelled to attend to metrics that (re)shape our conceptualisation of the student experience. New technologies such as learning analytics (LA) promise the ability to target personalised support to profiled 'at risk' students through mapping large-scale historic student engagement data such as attendance, library use, and…
Descriptors: Learning Analytics, Higher Education, Educational Objectives, Foreign Countries
Silvia García-Méndez; Francisco de Arriba-Pérez; Francisco J. González-Castaño – International Association for Development of the Information Society, 2023
Mobile learning or mLearning has become an essential tool in many fields in this digital era, among the ones educational training deserves special attention, that is, applied to both basic and higher education towards active, flexible, effective high-quality and continuous learning. However, despite the advances in Natural Language Processing…
Descriptors: Higher Education, Artificial Intelligence, Computer Software, Usability
Khan, Md Akib Zabed; Polyzou, Agoritsa – International Educational Data Mining Society, 2023
Academic advising plays an important role in students' decision-making in higher education. Data-driven methods provide useful recommendations to students to help them with degree completion. Several course recommendation models have been proposed in the literature to recommend courses for the next semester. One aspect of the data that has yet to…
Descriptors: Course Selection (Students), Learning Analytics, Academic Advising, Decision Making
Bonner, Euan; Garvey, Kevin; Miner, Matthew; Godin, Sam; Reinders, Hayo – Innovation in Language Learning and Teaching, 2023
This paper reports on the development and piloting of Classmoto, an online application designed to measure learner engagement. The application enables teachers to collect real-time analytics of student social, affective, and cognitive engagement. The results are immediately visible to the teacher. We investigated and reported on the engagement…
Descriptors: Foreign Countries, Learner Engagement, English (Second Language), Second Language Learning
Dollinger, Mollie; Lodge, Jason – Educational Media International, 2019
The growing practice of students as partners (SaP) has sparked numerous conversations in higher education about the roles students do and should play in shaping the future. SaP scholars contend that by engaging with students in meaningful partnership, underpinned by values such reciprocity, students can have deeper and more meaningful learning…
Descriptors: Learning Analytics, Partnerships in Education, Student Role, Teacher Student Relationship
West, Deborah; Luzeckyj, Ann; Toohey, Danny; Vanderlelie, Jessica; Searle, Bill – Australasian Journal of Educational Technology, 2020
Increasingly learning analytics (LA) has begun utilising staff- and student-facing dashboards capturing visualisations to present data to support student success and improve learning and teaching. The use of LA is complex, multifaceted and raises many issues for consideration, including ethical and legal challenges, competing stakeholder views and…
Descriptors: College Faculty, College Administration, Ethics, Student Attitudes
Aburizaizah, Saeed Jameel – Journal of Education and Learning, 2021
For many justifications, the collection, analysis, and use of educational data are central to the evaluation and improvement of students' progress and learning outcomes. The use of data in educational evaluation and decision making are expected to span all layers--from the institution, teachers, students, and classroom levels, providing a…
Descriptors: Data Use, Decision Making, Progress Monitoring, Learning Analytics
Whalley, Brian; France, Derek; Park, Julian; Mauchline, Alice; Welsh, Katharine – Higher Education Pedagogies, 2021
The concept of the Fourth Industrial Revolution is related to a ubiquitously connected, pervasively proximate (UCaPP) world and its response to COVID-19. Pedagogies need to be aligned with institutional 'quality education' and changes in the nature of the undergraduate student intake to formulate a 'Future Educational System'. Considerations…
Descriptors: Individualized Instruction, Active Learning, COVID-19, Pandemics
Orchard, Ryan K. – Journal of Educational Technology Systems, 2019
Learning management systems (LMS) allow for a variety of ways in which online multiple-choice assessments ("tests") can be configured, including the ability to allow for multiple attempts and options for which of and how the attempts will count. These options are usually chosen according to the instinct of the instructor; however, LMS…
Descriptors: Integrated Learning Systems, Data Use, Electronic Learning, Assignments
Williamson, Ben – British Journal of Educational Technology, 2019
Digital data are transforming higher education (HE) to be more student-focused and metrics-centred. In the UK, capturing detailed data about students has become a government priority, with an emphasis on using student data to measure, compare and assess university performance. The purpose of this paper is to examine the governmental and commercial…
Descriptors: Foreign Countries, Higher Education, Technology Uses in Education, Data Analysis
Dollinger, Mollie; Cox, Sarah; Eaton, Rebecca; Vanderlelie, Jessica; Ridsdale, Sam – Journal of Interactive Media in Education, 2020
This article will explore usage patterns and perceptions of online learning support among university students. As higher education expands to include increasingly diverse student cohorts, alternative online-supported learning services have gained attention as a mechanism to support student success. However, there is a paucity of research regarding…
Descriptors: Student Diversity, Electronic Learning, Academic Support Services, College Students
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