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Blaženka Divjak; Barbi Svetec; Damir Horvat – Journal of Computer Assisted Learning, 2024
Background: Sound learning design should be based on the constructive alignment of intended learning outcomes (LOs), teaching and learning activities and formative and summative assessment. Assessment validity strongly relies on its alignment with LOs. Valid and reliable formative assessment can be analysed as a predictor of students' academic…
Descriptors: Automation, Formative Evaluation, Test Validity, Test Reliability
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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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Okan Bulut; Guher Gorgun; Seyma Nur Yildirim-Erbasli – Journal of Computer Assisted Learning, 2025
Background: Research shows that how formative assessments are operationalized plays a crucial role in shaping their engagement with formative assessments, thereby impacting their effectiveness in predicting academic achievement. Mandatory assessments can ensure consistent student participation, leading to better tracking of learning progress.…
Descriptors: Formative Evaluation, Academic Achievement, Student Participation, Learning Processes
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Jewoong Moon; Sheunghyun Yeo; Seyyed Kazem Banihashem; Omid Noroozi – Journal of Computer Assisted Learning, 2024
Background: Traditionally, understanding students' learning dynamics, collaboration, emotions, and their impact on performance has posed challenges in formative assessment. The complexity of monitoring and assessing these factors have often limited the depth and breadth of insights. Objectives: This study aims to explore the potential of…
Descriptors: Formative Evaluation, Nonverbal Communication, Outcomes of Education, Learning Analytics
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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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Ifenthaler, Dirk; Schumacher, Clara; Kuzilek, Jakub – Journal of Computer Assisted Learning, 2023
Background: Formative assessments are vital for supporting learning and performance but are also considered to increase the workload of teachers. As self-assessments in higher education are increasingly facilitated via digital learning environments allowing to offer direct feedback and tracking students' digital learning behaviour these…
Descriptors: Self Evaluation (Individuals), Economics Education, Business Administration Education, Faculty Workload
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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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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