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Abdulkadir Kara; Eda Saka Simsek; Serkan Yildirim – Asian Journal of Distance Education, 2024
Evaluation is an essential component of the learning process when discerning learning situations. Assessing natural language responses, like short answers, takes time and effort. Artificial intelligence and natural language processing advancements have led to more studies on automatically grading short answers. In this review, we systematically…
Descriptors: Automation, Natural Language Processing, Artificial Intelligence, Grading
Qiao, Chen; Hu, Xiao – IEEE Transactions on Learning Technologies, 2023
Free text answers to short questions can reflect students' mastery of concepts and their relationships relevant to learning objectives. However, automating the assessment of free text answers has been challenging due to the complexity of natural language. Existing studies often predict the scores of free text answers in a "black box"…
Descriptors: Computer Assisted Testing, Automation, Test Items, Semantics
Sami Baral; Eamon Worden; Wen-Chiang Lim; Zhuang Luo; Christopher Santorelli; Ashish Gurung; Neil Heffernan – Grantee Submission, 2024
The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research have explored methodologies to enhance the effectiveness of feedback to students in various ways. Recent developments in Large Language Models (LLMs) have extended their utility in enhancing automated…
Descriptors: Automation, Scoring, Computer Assisted Testing, Natural Language Processing
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)
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
C. H., Dhawaleswar Rao; Saha, Sujan Kumar – IEEE Transactions on Learning Technologies, 2023
Multiple-choice question (MCQ) plays a significant role in educational assessment. Automatic MCQ generation has been an active research area for years, and many systems have been developed for MCQ generation. Still, we could not find any system that generates accurate MCQs from school-level textbook contents that are useful in real examinations.…
Descriptors: Multiple Choice Tests, Computer Assisted Testing, Automation, Test Items
Somers, Rick; Cunningham-Nelson, Samuel; Boles, Wageeh – Australasian Journal of Educational Technology, 2021
In this study, we applied natural language processing (NLP) techniques, within an educational environment, to evaluate their usefulness for automated assessment of students' conceptual understanding from their short answer responses. Assessing understanding provides insight into and feedback on students' conceptual understanding, which is often…
Descriptors: Natural Language Processing, Student Evaluation, Automation, Feedback (Response)
Binglin Chen – ProQuest LLC, 2022
Assessment is a key component of education. Routine grading of students' work, however, is time consuming. Automating the grading process allows instructors to spend more of their time helping their students learn and engaging their students with more open-ended, creative activities. One way to automate grading is through computer-based…
Descriptors: College Students, STEM Education, Student Evaluation, Grading
Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models
Sebastian Gombert; Aron Fink; Tornike Giorgashvili; Ioana Jivet; Daniele Di Mitri; Jane Yau; Andreas Frey; Hendrik Drachsler – International Journal of Artificial Intelligence in Education, 2024
Various studies empirically proved the value of highly informative feedback for enhancing learner success. However, digital educational technology has yet to catch up as automated feedback is often provided shallowly. This paper presents a case study on implementing a pipeline that provides German-speaking university students enrolled in an…
Descriptors: Automation, Student Evaluation, Essays, Feedback (Response)
Mike Richards; Kevin Waugh; Mark A Slaymaker; Marian Petre; John Woodthorpe; Daniel Gooch – ACM Transactions on Computing Education, 2024
Cheating has been a long-standing issue in university assessments. However, the release of ChatGPT and other free-to-use generative AI tools has provided a new and distinct method for cheating. Students can run many assessment questions through the tool and generate a superficially compelling answer, which may or may not be accurate. We ran a…
Descriptors: Computer Science Education, Artificial Intelligence, Cheating, Student Evaluation
McCarthy, Kathryn S.; Allen, Laura K.; Hinze, Scott R. – Grantee Submission, 2020
Open-ended "constructed responses" promote deeper processing of course materials. Further, evaluation of these explanations can yield important information about students' cognition. This study examined how students' constructed responses, generated at different points during learning, relate to their later comprehension outcomes.…
Descriptors: Reading Comprehension, Prediction, Responses, College Students
Lu, Chang; Cutumisu, Maria – International Educational Data Mining Society, 2021
Digitalization and automation of test administration, score reporting, and feedback provision have the potential to benefit large-scale and formative assessments. Many studies on automated essay scoring (AES) and feedback generation systems were published in the last decade, but few connected AES and feedback generation within a unified framework.…
Descriptors: Learning Processes, Automation, Computer Assisted Testing, Scoring
Lu, Owen H. T.; Huang, Anna Y. Q.; Tsai, Danny C. L.; Yang, Stephen J. H. – Educational Technology & Society, 2021
Human-guided machine learning can improve computing intelligence, and it can accurately assist humans in various tasks. In education research, artificial intelligence (AI) is applicable in many situations, such as predicting students' learning paths and strategies. In this study, we explore the benefits of repetitive practice of short-answer…
Descriptors: Test Items, Artificial Intelligence, Test Construction, Student Evaluation
Malik, Kaleem Razzaq; Mir, Rizwan Riaz; Farhan, Muhammad; Rafiq, Tariq; Aslam, Muhammad – EURASIA Journal of Mathematics, Science & Technology Education, 2017
Research in era of data representation to contribute and improve key data policy involving the assessment of learning, training and English language competency. Students are required to communicate in English with high level impact using language and influence. The electronic technology works to assess students' questions positively enabling…
Descriptors: Knowledge Management, Computer Assisted Testing, Student Evaluation, Search Strategies
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