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Uto, Masaki; Aomi, Itsuki; Tsutsumi, Emiko; Ueno, Maomi – IEEE Transactions on Learning Technologies, 2023
In automated essay scoring (AES), essays are automatically graded without human raters. Many AES models based on various manually designed features or various architectures of deep neural networks (DNNs) have been proposed over the past few decades. Each AES model has unique advantages and characteristics. Therefore, rather than using a single-AES…
Descriptors: Prediction, Scores, Computer Assisted Testing, Scoring
Shin, Jinnie; Gierl, Mark J. – Journal of Applied Testing Technology, 2022
Automated Essay Scoring (AES) technologies provide innovative solutions to score the written essays with a much shorter time span and at a fraction of the current cost. Traditionally, AES emphasized the importance of capturing the "coherence" of writing because abundant evidence indicated the connection between coherence and the overall…
Descriptors: Computer Assisted Testing, Scoring, Essays, Automation
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
Casabianca, Jodi M.; Donoghue, John R.; Shin, Hyo Jeong; Chao, Szu-Fu; Choi, Ikkyu – Journal of Educational Measurement, 2023
Using item-response theory to model rater effects provides an alternative solution for rater monitoring and diagnosis, compared to using standard performance metrics. In order to fit such models, the ratings data must be sufficiently connected in order to estimate rater effects. Due to popular rating designs used in large-scale testing scenarios,…
Descriptors: Item Response Theory, Alternative Assessment, Evaluators, Research Problems
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
Christopher D. Wilson; Kevin C. Haudek; Jonathan F. Osborne; Zoë E. Buck Bracey; Tina Cheuk; Brian M. Donovan; Molly A. M. Stuhlsatz; Marisol M. Santiago; Xiaoming Zhai – Journal of Research in Science Teaching, 2024
Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning--a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the…
Descriptors: Middle School Students, Competence, Science Process Skills, Persuasive Discourse
Shinta Estri Wahyuningrum; Gilles van Luijtelaar; Augustina Sulastri; Marc P. H. Hendriks; Ridwan Sanjaya; Tom Heskes – SAGE Open, 2024
Visual Reproduction is a condition to measure Visual Spatial Memory as one of the cognitive domains commonly used to measure visuo-spatial memory. Geometric figures serve as stimulus material, and probands have to reproduce the figures from memory through a hand drawing. The scoring of the drawing has subjective elements. This study aims to…
Descriptors: Automation, Scores, Geometry, Visual Aids
LaFlair, Geoffrey T.; Langenfeld, Thomas; Baig, Basim; Horie, André Kenji; Attali, Yigal; von Davier, Alina A. – Journal of Computer Assisted Learning, 2022
Background: Digital-first assessments leverage the affordances of technology in all elements of the assessment process--from design and development to score reporting and evaluation to create test taker-centric assessments. Objectives: The goal of this paper is to describe the engineering, machine learning, and psychometric processes and…
Descriptors: Computer Assisted Testing, Affordances, Scoring, Engineering
Murad, Dina Fitria; Heryadi, Yaya; Isa, Sani Muhamad; Budiharto, Widodo – Education and Information Technologies, 2020
The recommender system has gained research attention from education research communities mainly due to two main reasons: increasing needs for personalized learning and big data availability in the education sector. This paper presents a hybrid user-collaborative, rule-based filtering recommendation system for education context. User profiles are…
Descriptors: Automation, Online Systems, Electronic Learning, Prediction
Joshua Kloppers – International Journal of Computer-Assisted Language Learning and Teaching, 2023
Automated writing evaluation (AWE) software is an increasingly popular tool for English second language learners. However, research on the accuracy of such software has been both scarce and largely limited in its scope. As such, this article broadens the field of research on AWE accuracy by using a mixed design to holistically evaluate the…
Descriptors: Grammar, Automation, Writing Evaluation, Computer Assisted Instruction
Olivera-Aguilar, Margarita; Lee, Hee-Sun; Pallant, Amy; Belur, Vinetha; Mulholland, Matthew; Liu, Ou Lydia – ETS Research Report Series, 2022
This study uses a computerized formative assessment system that provides automated scoring and feedback to help students write scientific arguments in a climate change curriculum. We compared the effect of contextualized versus generic automated feedback on students' explanations of scientific claims and attributions of uncertainty to those…
Descriptors: Computer Assisted Testing, Formative Evaluation, Automation, Scoring
Myers, Matthew C.; Wilson, Joshua – International Journal of Artificial Intelligence in Education, 2023
This study evaluated the construct validity of six scoring traits of an automated writing evaluation (AWE) system called "MI Write." Persuasive essays (N = 100) written by students in grades 7 and 8 were randomized at the sentence-level using a script written with Python's NLTK module. Each persuasive essay was randomized 30 times (n =…
Descriptors: Construct Validity, Automation, Writing Evaluation, Algorithms
Yang, Albert C. M.; Chen, Irene Y. L.; Flanagan, Brendan; Ogata, Hiroaki – Educational Technology & Society, 2021
Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students' learning process. Text marking is an essential learning skill in reading. In this…
Descriptors: Grading, Computer Assisted Testing, Automation, Artificial Intelligence
Sterett H. Mercer; Joanna E. Cannon – Grantee Submission, 2022
We evaluated the validity of an automated approach to learning progress assessment (aLPA) for English written expression. Participants (n = 105) were students in Grades 2-12 who had parent-identified learning difficulties and received academic tutoring through a community-based organization. Participants completed narrative writing samples in the…
Descriptors: Elementary School Students, Secondary School Students, Learning Problems, Learning Disabilities
Correnti, Richard; Matsumura, Lindsay Clare; Wang, Elaine; Litman, Diane; Rahimi, Zahra; Kisa, Zahid – Reading Research Quarterly, 2020
Despite the importance of analytic text-based writing, relatively little is known about how to teach to this important skill. A persistent barrier to conducting research that would provide insight on best practices for teaching this form of writing is a lack of outcome measures that assess students' analytic text-based writing development and that…
Descriptors: Writing Evaluation, Writing Tests, Computer Assisted Testing, Scoring