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Brown, Michael – Teaching in Higher Education, 2020
Despite their increasingly widespread adoption in post-secondary education, scholars and practitioners know very little about the impact of digital data displays on instructors' sense-making and academic planning. In this manuscript, I report the results of comparative case studies of five different introductory physics instructors at three…
Descriptors: College Faculty, Learning Analytics, Introductory Courses, Physics
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Demmans Epp, Carrie; Phirangee, Krystle; Hewitt, Jim; Perfetti, Charles A. – Educational Technology Research and Development, 2020
From massive open online courses (MOOC) to the smaller scale use of learning management systems, many students interact with online platforms that are meant to support learning. Investigations into the use of these systems have considered how well students learn when certain approaches are employed. However, we do not fully understand how course…
Descriptors: Integrated Learning Systems, Student Experience, Learning Analytics, Online Courses
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Bailie, Jeffrey L. – Journal of Instructional Pedagogies, 2020
For close to three decades. the positive effects of online learner engagement in asynchronous discussions have been reported. Given the many positive effects of asynchronous discussion that have been conveyed in the literature, a preponderance of today's online courses include the activity as a part of the learning experience. It seems only…
Descriptors: Learning Analytics, Asynchronous Communication, Predictor Variables, Grades (Scholastic)
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Chen, Chen; Sonnert, Gerhard; Sadler, Philip M.; Sasselov, Dimitar D.; Fredericks, Colin; Malan, David J. – Distance Education, 2020
Participants' engagement in massive online open courses (MOOCs) is highly irregular and self-directed. It is well known in the field of television media that substantial parts of the audience tend to drop out at major episodic, or seasonal, closures, which makes creating cliff-hangers a crucial strategy to retain viewers (Bakker, 1993; Cazani,…
Descriptors: Online Courses, Dropouts, Learning Analytics, Dropout Rate
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Huang, Anna Y. Q.; Lu, Owen H. T.; Huang, Jeff C. H.; Yin, C. J.; Yang, Stephen J. H. – Interactive Learning Environments, 2020
In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model,…
Descriptors: Academic Achievement, Data Use, Learning Analytics, Classification
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Tempelaar, Dirk – Assessment & Evaluation in Higher Education, 2020
How can we best facilitate students most in need of learning support, entering a challenging quantitative methods module at the start of their bachelor programme? In this empirical study into blended learning and the role of assessment for and as learning, we investigate learning processes of students with different learning profiles.…
Descriptors: Learning Analytics, Formative Evaluation, Blended Learning, Undergraduate Students
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Broughan, Christine; Prinsloo, Paul – Assessment & Evaluation in Higher Education, 2020
Student data, whether in the form of engagement data, assignments or examinations, form the foundation for assessment and evaluation in higher education. As higher education institutions progressively move to blended and online environments, we have access to, not only more data than before, but also a greater variety of demographic and…
Descriptors: Learning Analytics, Student Centered Learning, Student Empowerment, Data Collection
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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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Mshayisa, Vusi Vincent; Basitere, Moses – Journal of Food Science Education, 2021
In STEM (science, technology, engineering, and mathematics) courses, undergraduate laboratory classes are vital for students to develop competencies such as critical observation, collaboration, critical thinking, technical, and problem-solving skills. Thus, for students to successfully acquire these competencies, preparation for laboratory classes…
Descriptors: Flipped Classroom, Teaching Methods, Student Attitudes, STEM Education
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Tempelaar, Dirk; Rienties, Bart; Nguyen, Quan – Educational Technology & Society, 2021
Precision education requires two equally important conditions: accurate predictions of academic performance based on early observations of the learning process and the availability of relevant educational intervention options. The field of learning analytics (LA) has made important contributions to the realisation of the first condition,…
Descriptors: Learning Analytics, Individualized Instruction, Blended Learning, Electronic Learning
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Kisling, Reid; Peterson, Andrew; Nisbet, Robert – Strategic Enrollment Management Quarterly, 2021
Data analytics is undergoing an evolution through effective data use to support both operational and learning analytics models. However, this evolution will require that institutional leaders transform their data systems to best support the needs of application modeling and use their intuition to help drive the development of better analytical…
Descriptors: Higher Education, Learning Analytics, Models, Instructional Leadership
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Mansouri, Taha; ZareRavasan, Ahad; Ashrafi, Amir – Journal of Information Technology Education: Research, 2021
Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student…
Descriptors: Cognitive Mapping, Models, Prediction, Performance Factors
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Nahar, Khaledun; Shova, Boishakhe Islam; Ria, Tahmina; Rashid, Humayara Binte; Islam, A. H. M. Saiful – Education and Information Technologies, 2021
Information is everywhere in a hidden and scattered way. It becomes useful when we apply Data mining to extracts the hidden, meaningful, and potentially useful patterns from these vast data resources. Educational data mining ensures a quality education by analyzing educational data based on various aspects. In this paper, we have analyzed the…
Descriptors: Learning Analytics, College Students, Engineering Education, Data Collection
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Monbec, Laetitia; Tilakaratna, Namala; Brooke, Mark; Lau, Siew Tiang; Chan, Yah Shih; Wu, Vivien – Assessment & Evaluation in Higher Education, 2021
This paper reports on an interdisciplinary pedagogical research project involving academic literacy experts and lecturers at a School of Nursing. Specifically, the paper focusses on the development of a data-driven analytical rubric to teach and assess critical reflections in year-one nursing. The purpose of the project was to support the teaching…
Descriptors: Interdisciplinary Approach, Nursing Education, Literacy, Academic Language
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Salonen, Arto O.; Tapani, Annukka; Suhonen, Sami – SAGE Open, 2021
Distance learning is rapidly gaining ground globally. In this case study, we focused on professional (vocational) teacher education (PTE) student online activity in a blended learning context. We applied learning analytics (LA) to identify students' (n = 19) online study patterns. Our key interest was in determining when and what kinds of online…
Descriptors: Blended Learning, Electronic Learning, Preservice Teachers, Preservice Teacher Education
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