Publication Date
In 2025 | 5 |
Since 2024 | 14 |
Since 2021 (last 5 years) | 36 |
Since 2016 (last 10 years) | 47 |
Since 2006 (last 20 years) | 55 |
Descriptor
Automation | 56 |
Formative Evaluation | 56 |
Feedback (Response) | 36 |
Computer Assisted Testing | 20 |
Artificial Intelligence | 18 |
Scoring | 14 |
Foreign Countries | 13 |
Natural Language Processing | 13 |
Student Evaluation | 12 |
Technology Uses in Education | 11 |
Summative Evaluation | 10 |
More ▼ |
Source
Author
Belur, Vinetha | 2 |
Daniel F. McCaffrey | 2 |
Danielle S. McNamara | 2 |
Hendrik Drachsler | 2 |
Ioana Jivet | 2 |
Jessica Nastal | 2 |
Jill Burstein | 2 |
Lee, Hee-Sun | 2 |
Liu, Ou Lydia | 2 |
Lynette Hazelton | 2 |
Mulholland, Matthew | 2 |
More ▼ |
Publication Type
Education Level
Audience
Teachers | 1 |
Location
Pennsylvania | 4 |
Germany | 3 |
Australia | 2 |
Canada | 2 |
Illinois | 2 |
Italy | 2 |
Philippines | 2 |
South Korea | 2 |
Spain | 2 |
Algeria | 1 |
Asia | 1 |
More ▼ |
Laws, Policies, & Programs
Assessments and Surveys
Test of English as a Foreign… | 1 |
What Works Clearinghouse Rating
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
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
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
Wallace N. Pinto Jr.; Jinnie Shin – Journal of Educational Measurement, 2025
In recent years, the application of explainability techniques to automated essay scoring and automated short-answer grading (ASAG) models, particularly those based on transformer architectures, has gained significant attention. However, the reliability and consistency of these techniques remain underexplored. This study systematically investigates…
Descriptors: Automation, Grading, Computer Assisted Testing, Scoring
Adelina Asmawi; Md. Saiful Alam – Discover Education, 2025
In the evolving techno-educational landscape, it is crucial to reimagine transformative pedagogies based on techno-teacher collaboration to revolutionize teaching effectiveness and efficiency. Although the cutting-edge generative AI tool, Chat GPT, is speculated to be a revolutionary CALL (computer-assisted language learning) tool for teaching…
Descriptors: Reading Instruction, Teaching Methods, Computer Assisted Instruction, Instructional Effectiveness
Marrone, Rebecca; Cropley, David H.; Wang, Z. – Creativity Research Journal, 2023
Creativity is now accepted as a core 21st-century competency and is increasingly an explicit part of school curricula around the world. Therefore, the ability to assess creativity for both formative and summative purposes is vital. However, the "fitness-for-purpose" of creativity tests has recently come under scrutiny. Current creativity…
Descriptors: Automation, Evaluation Methods, Creative Thinking, Mathematics Education
Samantha Yanosko; Grant Valentine; Matthew W. Liberatore – Chemical Engineering Education, 2025
An interactive textbook for a material and energy balances course measured over 1,300 reading interactions and hundreds of auto-graded problems per student each term. Specifically, seven cohorts and 601 students completed over 700,000 reading interactions and 150,000 auto-graded problems. Median reading participation was over 93%. Median correct…
Descriptors: Chemical Engineering, Textbooks, Computer Uses in Education, Grading
Barczak, Andre L. C.; Mathrani, Anuradha; Han, Binglan; Reyes, Napoleon H. – Educational Technology Research and Development, 2023
An important course in the computer science discipline is 'Data Structures and Algorithms' (DSA). "The coursework" lays emphasis on experiential learning for building students' programming and algorithmic reasoning abilities. Teachers set up a repertoire of formative programming exercises to engage students with different programmatic…
Descriptors: Computer Assisted Testing, Automation, Computer Science Education, Programming
Ariely, Moriah; Nazaretsky, Tanya; Alexandron, Giora – International Journal of Artificial Intelligence in Education, 2023
Machine learning algorithms that automatically score scientific explanations can be used to measure students' conceptual understanding, identify gaps in their reasoning, and provide them with timely and individualized feedback. This paper presents the results of a study that uses Hebrew NLP to automatically score student explanations in Biology…
Descriptors: Artificial Intelligence, Algorithms, Natural Language Processing, Hebrew
Danielle S. McNamara; Panayiota Kendeou – Grantee Submission, 2022
We propose a framework designed to guide the development of automated writing practice and formative evaluation and feedback for young children (K-5 th grade) -- the early Automated Writing Evaluation (early-AWE) Framework. e-AWE is grounded on the fundamental assumption that e-AWE is needed for young developing readers, but must incorporate…
Descriptors: Writing Evaluation, Automation, Formative Evaluation, Feedback (Response)
Danielle S. McNamara; Panayiota Kendeou – Assessment in Education: Principles, Policy & Practice, 2022
We propose a framework designed to guide the development of automated writing practice and formative evaluation and feedback for young children (K-5th grade) -- the early Automated Writing Evaluation (early-AWE) Framework. e-AWE is grounded on the fundamental assumption that e-AWE is needed for young developing readers, but must incorporate…
Descriptors: Writing Evaluation, Automation, Formative Evaluation, Feedback (Response)
Jiseung Yoo; Jisun Park; Minsu Ha; Chelcea Mae Lagmay Darang – SAGE Open, 2024
In the context of formative assessment in classrooms, the incorporation of automated evaluation (AE) systems and teachers' interactions with them hold significant importance. This study aimed to investigate the cognitive processes of pre-service teachers as they engaged with an AE system. We developed an unsupervised learning-based AE system, the…
Descriptors: Preservice Teachers, Cognitive Processes, Automation, Supervision
Saida Ulfa; Ence Surahman; Agus Wedi; Izzul Fatawi; Rex Bringula – Knowledge Management & E-Learning, 2025
Online assessment is one of the important factors in online learning today. An online summary assessment is an example of an open-ended question, offering the advantage of probing students' understanding of the learning materials. However, grading students' summary writings is challenging due to the time-consuming process of evaluating students'…
Descriptors: Knowledge Management, Automation, Documentation, Feedback (Response)
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
Moriah Ariely; Tanya Nazaretsky; Giora Alexandron – Journal of Research in Science Teaching, 2024
One of the core practices of science is constructing scientific explanations. However, numerous studies have shown that constructing scientific explanations poses significant challenges to students. Proper assessment of scientific explanations is costly and time-consuming, and teachers often do not have a clear definition of the educational goals…
Descriptors: Biology, Automation, Individualized Instruction, Science Instruction