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Peter Organisciak; Selcuk Acar; Denis Dumas; Kelly Berthiaume – Grantee Submission, 2023
Automated scoring for divergent thinking (DT) seeks to overcome a key obstacle to creativity measurement: the effort, cost, and reliability of scoring open-ended tests. For a common test of DT, the Alternate Uses Task (AUT), the primary automated approach casts the problem as a semantic distance between a prompt and the resulting idea in a text…
Descriptors: Automation, Computer Assisted Testing, Scoring, Creative Thinking
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David W. Brown; Dean Jensen – International Society for Technology, Education, and Science, 2023
The growth of Artificial Intelligence (AI) chatbots has created a great deal of discussion in the education community. While many have gravitated towards the ability of these bots to make learning more interactive, others have grave concerns that student created essays, long used as a means of assessing the subject comprehension of students, may…
Descriptors: Artificial Intelligence, Natural Language Processing, Computer Software, Writing (Composition)
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Lonneke Boels; Enrique Garcia Moreno-Esteva; Arthur Bakker; Paul Drijvers – International Journal of Artificial Intelligence in Education, 2024
As a first step toward automatic feedback based on students' strategies for solving histogram tasks we investigated how strategy recognition can be automated based on students' gazes. A previous study showed how students' task-specific strategies can be inferred from their gazes. The research question addressed in the present article is how data…
Descriptors: Eye Movements, Learning Strategies, Problem Solving, Automation
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Anand Jeyaraj – Journal of Information Systems Education, 2024
A significant activity in the business analytics process is enrichment, which deals with acquiring and combining data from external sources. While different strategies for enrichment are possible, it can be accomplished more efficiently through automation using Python scripts. Since business students may not be immersed in technology skills and…
Descriptors: Scaffolding (Teaching Technique), Business Administration Education, Data Analysis, Programming Languages
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Duo Liu; Lei Wang; Terry Tin-Yau Wong; R. Malatesha Joshi – Journal of Research in Reading, 2024
Background: Rapid automatised naming (RAN) has been found to predict children's reading and arithmetic abilities. However, the underlying mechanisms for its involvement in the two abilities are not clear. This study examines how RAN shared variances with domain-general and domain-specific abilities in predicting reading and arithmetic in Chinese…
Descriptors: Grade 3, Elementary School Students, Foreign Countries, Automation
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Xiaoqin Shi; Xiaoqing Wang; Wei Zhang – Language Testing in Asia, 2024
Automatic Speech Scoring (ASS) has increasingly become a useful tool in oral proficiency testing for Second Language (L2) learners. However, limited studies investigate the alignment of ASS indices with the Complexity, Accuracy, and Fluency (CAF)--the three dimensions in evaluating L2 speakers' oral proficiency, and the subsequent impact indices…
Descriptors: Speech Communication, Oral Language, Language Proficiency, Scoring
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Qi Wang; Shengquan Yu – Interactive Learning Environments, 2024
Learning resources are quite important for online learning while resource provision based on algorithms could not address learners' ubiquitous needs well. Moreover, the structure and content of resources are pre-defined which makes the "Structure" and "Content" coupled closely and could not easily adjust when learners' needs…
Descriptors: Electronic Learning, Educational Resources, Automation, Models
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Wai Tong Chor; Kam Meng Goh; Li Li Lim; Kin Yun Lum; Tsung Heng Chiew – Education and Information Technologies, 2024
The programme outcomes are broad statements of knowledge, skills, and competencies that the students should be able to demonstrate upon graduation from a programme, while the Educational Taxonomy classifies learning objectives into different domains. The precise mapping of a course outcomes to the programme outcome and the educational taxonomy…
Descriptors: Artificial Intelligence, Engineering Education, Taxonomy, Educational Objectives
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Mikyung Kim Wolf; Saerhim Oh – Language Learning & Technology, 2024
With the increased rigor of academic standards, high expectations of academic writing skills have been imposed on students in U.S. K-12 schools. For English learner (EL) students who cope with the dual challenges of learning rigorous subject matters and developing their English language proficiency simultaneously, extra support and effective…
Descriptors: Middle School Students, English Language Learners, Feedback (Response), Academic Language
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Das, Syaamantak; Mandal, Shyamal Kumar Das; Basu, Anupam – Contemporary Educational Technology, 2020
Cognitive learning complexity identification of assessment questions is an essential task in the domain of education, as it helps both the teacher and the learner to discover the thinking process required to answer a given question. Bloom's Taxonomy cognitive levels are considered as a benchmark standard for the classification of cognitive…
Descriptors: Classification, Difficulty Level, Test Items, Identification
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Sahu, Archana; Bhowmick, Plaban Kumar – IEEE Transactions on Learning Technologies, 2020
In this paper, we studied different automatic short answer grading (ASAG) systems to provide a comprehensive view of the feature spaces explored by previous works. While the performance reported in previous works have been encouraging, systematic study of the features is lacking. Apart from providing systematic feature space exploration, we also…
Descriptors: Automation, Grading, Test Format, Artificial Intelligence
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Thaker, Khushboo; Zhang, Lei; He, Daqing; Brusilovsky, Peter – International Educational Data Mining Society, 2020
Assessment plays a vital role in learning, as it provides both instructors and students with feedback on the overall effectiveness of their teaching or learning. However, when a student fails to correctly answer certain questions in an assessment (such as a quiz), the student needs specific recommendations that are tailored to their learning needs…
Descriptors: Automation, Textbooks, Electronic Learning, Artificial Intelligence
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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)
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Keller-Margulis, Milena A.; Mercer, Sterett H.; Matta, Michael – Reading and Writing: An Interdisciplinary Journal, 2021
Existing approaches to measuring writing performance are insufficient in terms of both technical adequacy as well as feasibility for use as a screening measure. This study examined the validity and diagnostic accuracy of several approaches to automated text evaluation as well as written expression curriculum-based measurement (WE-CBM) to determine…
Descriptors: Writing Evaluation, Validity, Automation, Curriculum Based Assessment
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Tsiakmaki, Maria; Kostopoulos, Georgios; Kotsiantis, Sotiris; Ragos, Omiros – Journal of Computing in Higher Education, 2021
Predicting students' learning outcomes is one of the main topics of interest in the area of Educational Data Mining and Learning Analytics. To this end, a plethora of machine learning methods has been successfully applied for solving a variety of predictive problems. However, it is of utmost importance for both educators and data scientists to…
Descriptors: Active Learning, Predictor Variables, Academic Achievement, Learning Analytics
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