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Marwan, Samiha; Akram, Bita; Barnes, Tiffany; Price, Thomas W. – IEEE Transactions on Learning Technologies, 2022
Theories on learning show that formative feedback that is immediate, specific, corrective, and positive is essential to improve novice students' motivation and learning. However, most prior work on programming feedback focuses on highlighting student's mistakes, or detecting failed test cases after they submit a solution. In this article, we…
Descriptors: Feedback (Response), Formative Evaluation, Programming, Coding
Keuning, Trynke; van Geel, Marieke – IEEE Transactions on Learning Technologies, 2021
Although many schools in the Netherlands have purchased adaptive learning systems (ALSs) to reduce workload and improve differentiated instruction, the use of ALSs with teacher dashboards in the classroom does not in itself necessarily improve differentiated instruction. The question is, what skills and knowledge do teachers need to provide…
Descriptors: Foreign Countries, Individualized Instruction, Integrated Learning Systems, Educational Technology
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
Cano, Alberto; Leonard, John D. – IEEE Transactions on Learning Technologies, 2019
Early warning systems have been progressively implemented in higher education institutions to predict student performance. However, they usually fail at effectively integrating the many information sources available at universities to make more accurate and timely predictions, they often lack decision-making reasoning to motivate the reasons…
Descriptors: Progress Monitoring, At Risk Students, Disproportionate Representation, Underachievement
Facey-Shaw, Lisa; Specht, Marcus; van Rosmalen, Peter; Borner, Dirk; Bartley-Bryan, Jeanette – IEEE Transactions on Learning Technologies, 2018
In today's technological era, emerging educational technologies, such as digital badges, have shown the potential for fostering student learning. To examine the major considerations undergirding the design of digital badges used in an educational context, a conceptual literature review of multidisciplinary electronic databases oriented towards…
Descriptors: Recognition (Achievement), Educational Technology, Instructional Design, Mastery Learning
Bodily, Robert; Verbert, Katrien – IEEE Transactions on Learning Technologies, 2017
This article is a comprehensive literature review of student-facing learning analytics reporting systems that track learning analytics data and report it directly to students. This literature review builds on four previously conducted literature reviews in similar domains. Out of the 945 articles retrieved from databases and journals, 93 articles…
Descriptors: Literature Reviews, Usability, Information Sources, Design Preferences
An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Course
Baneres, David; Rodriguez-Gonzalez, M. Elena; Serra, Montse – IEEE Transactions on Learning Technologies, 2019
Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management…
Descriptors: Prediction, Feedback (Response), At Risk Students, College Freshmen
Drljevic, Neven; Wong, Lung Hsiang; Boticki, Ivica – IEEE Transactions on Learning Technologies, 2017
The paper provides a high-level review of the current state of techno-pedagogical design in Augmented Reality Learning Experiences (ARLEs). The review is based on a rubric constructed from the Meaningful Learning with ICT framework and the Orchestration Load reduction framework, providing, respectively, a view of primarily student- and primarily…
Descriptors: Simulated Environment, Computer Simulation, Educational Technology, Technology Uses in Education