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Onur Karademir; Daniele Di Mitri; Jan Schneider; Ioana Jivet; Jörn Allmang; Sebastian Gombert; Marcus Kubsch; Knut Neumann; Hendrik Drachsler – Journal of Computer Assisted Learning, 2024
Background: Teacher dashboards can help secondary school teachers manage online learning activities and inform instructional decisions by visualising information about class learning. However, when designing teacher dashboards, it is not trivial to choose which information to display, because not all of the vast amount of information retrieved…
Descriptors: Learning Analytics, Secondary School Teachers, Educational Technology, Design
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Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
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Richard Say; Denis Visentin; Annette Saunders; Iain Atherton; Andrea Carr; Carolyn King – Journal of Computer Assisted Learning, 2024
Background: Formative online multiple-choice tests are ubiquitous in higher education and potentially powerful learning tools. However, commonly used feedback approaches in online multiple-choice tests can discourage meaningful engagement and enable strategies, such as trial-and-error, that circumvent intended learning outcomes. These strategies…
Descriptors: Feedback (Response), Self Management, Formative Evaluation, Multiple Choice Tests
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Joshua Weidlich; Aron Fink; Ioana Jivet; Jane Yau; Tornike Giorgashvili; Hendrik Drachsler; Andreas Frey – Journal of Computer Assisted Learning, 2024
Background: Developments in educational technology and learning analytics make it possible to automatically formulate and deploy personalized formative feedback to learners at scale. However, to be effective, the motivational and emotional impacts of such automated and personalized feedback need to be considered. The literature on feedback…
Descriptors: Emotional Response, Student Motivation, Feedback (Response), Automation
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Kim, Min Kyu; McCarthy, Kathryn S. – Journal of Computer Assisted Learning, 2021
Summary writing is a useful instructional tool for learning. However, summary writing is a challenge to many students. This mixed-method study examined the potential of the Student Mental Model Analyzer for Research and Teaching (SMART) system to help students produce summaries that reflect key concepts and relations in a text. SMART uses the…
Descriptors: Writing Improvement, Formative Evaluation, Technology Integration, Schemata (Cognition)
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Luzhen Tang; Kejie Shen; Huixiao Le; Yuan Shen; Shufang Tan; Yueying Zhao; Torsten Juelich; Xinyu Li; Dragan Gaševic; Yizhou Fan – Journal of Computer Assisted Learning, 2024
Background: Learners' writing skills are critical to their academic and professional development. Previous studies have shown that learners' self-assessment during writing is essential for assessing their writing products and monitoring their writing processes. However, conducting practical self-assessments of writing remains challenging for…
Descriptors: Self Evaluation (Individuals), Formative Evaluation, Writing Assignments, Writing Skills
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Ackermans, Kevin; Rusman, Ellen; Nadolski, Rob; Specht, Marcus; Brand-Gruwel, Saskia – Journal of Computer Assisted Learning, 2021
Learners in the process of developing complex skills need a rich mental model of what such skills entail. Textual analytics rubrics (TR) are a widely used instrument to support formative assessment of complex skills, supporting feedback, reflection, and thus mental model development of complex skills. However, the textual nature of a rubric limits…
Descriptors: Video Technology, Scoring Rubrics, Cues, Formative Evaluation
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McLaughlin, T.; Yan, Z. – Journal of Computer Assisted Learning, 2017
This article is a review of literature on online formative assessment (OFA). It includes a narrative summary that synthesizes the research on the diverse delivery methods of OFA, as well as the empirical literature regarding the strong psychological benefits and limitations. Online formative assessment can be delivered using many traditional…
Descriptors: Formative Evaluation, Computer Assisted Testing, Delivery Systems, Achievement Gains
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McDonald, J.; Bird, R. J.; Zouaq, A.; Moskal, A. C. M. – Journal of Computer Assisted Learning, 2017
In large class settings, individualized student-teacher interaction is difficult. However, teaching interactions (e.g., formative feedback) are central to encouraging deep approaches to learning. While there has been progress in automatic short-answer grading, analysing student responses to support formative feedback at scale is arguably some way…
Descriptors: College Students, Health Sciences, Teacher Student Relationship, Large Group Instruction
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Llorens, A. C.; Vidal-Abarca, E.; Cerdán, R. – Journal of Computer Assisted Learning, 2016
The study includes two experiments to analyse the effects of automatic formative feedback designed to promote the transfer of self-regulation of strategic decisions in task-oriented reading (e.g. answering questions from an available text). Secondary-school students read and answered multiple-choice comprehension questions from two texts having…
Descriptors: Formative Evaluation, Feedback (Response), Reading Strategies, Experiments
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Paiva, R. C.; Ferreira, M. S.; Frade, M. M. – Journal of Computer Assisted Learning, 2017
The growth of the higher education population and different school paths to access an academic degree has increased the heterogeneity of students inside the classroom. Consequently, the effectiveness of traditional teaching methods has reduced. This paper describes the design, development, implementation and evaluation of a tutoring system (TS) to…
Descriptors: Intelligent Tutoring Systems, Mathematics Instruction, Mathematical Concepts, Mastery Learning
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Berlanga, A. J.; van Rosmalen, P.; Boshuizen, H. P. A.; Sloep, P. B. – Journal of Computer Assisted Learning, 2012
Learners, particularly lifelong learners, often find it difficult to determine the scope of their expertise. Formative feedback could help them do so. To use this feedback productively, it is essential to then suggest to them the remedial actions they need to overcome the gaps in their knowledge. This paper presents the design considerations of a…
Descriptors: Feedback (Response), Formative Evaluation, Assignments, Visual Aids