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Yibei Yin – International Journal of Web-Based Learning and Teaching Technologies, 2023
In order to study the big data of college students' employment, this paper takes the big data of college students' employment as the premise, analyzes the current employment data by establishing a DBN model, and puts forward relevant management measures, aiming to provide scientific basis for the management of graduates' employment data. The…
Descriptors: College Students, Student Employment, Data Analysis, Artificial Intelligence
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Senad Becirovic – Education and Information Technologies, 2024
This study aims to determine the factors influencing the efficient and successful use of LMS among university-level students. A multiperspective approach was performed using TAM3 and ISS framework to achieve the aforementioned aim. The survey was administered to 371 university students. Structural equation modeling (SEM) has been conducted to test…
Descriptors: College Students, Learning Management Systems, Accuracy, User Satisfaction (Information)
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Priya Patel; Harsh Pandya; Rajiv Ranganathan; Mei-Hua Lee – Journal of Motor Learning and Development, 2024
Manual exploratory behaviors during object interaction that form the basis of tool use behavior, are mostly qualitatively characterized in terms of their frequency and duration of occurrence. To fully understand their functional and clinical significance, quantitative movement characterization is needed alongside their qualitative analysis.…
Descriptors: Discovery Learning, Toys, Measurement Equipment, Object Manipulation
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Laurie O. Campbell; Thomas D. Cox – Journal of the Scholarship of Teaching and Learning, 2024
In higher education, generative chatbots have infiltrated teaching and learning. Concerns about how and if to utilize chatbots in the classroom are at the forefront of scholarly discussion. This quick-hit article presents a plan to teach learners about generative AI writing tools and their ethical use for writing purposes. As generative AI tools…
Descriptors: Research Projects, Writing Processes, Writing (Composition), Artificial Intelligence
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Greene, Nathaniel R.; Naveh-Benjamin, Moshe – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
Assessing the time course under which underlying memory representations can be formed is an important question for understanding memory. Several studies assessing item memory have shown that gist representations of items are laid out more rapidly than verbatim representations. However, for associations among items/components, which form the core…
Descriptors: Memory, Comprehension, Cognitive Processes, Visual Discrimination
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Yunting Liu; Shreya Bhandari; Zachary A. Pardos – British Journal of Educational Technology, 2025
Effective educational measurement relies heavily on the curation of well-designed item pools. However, item calibration is time consuming and costly, requiring a sufficient number of respondents to estimate the psychometric properties of items. In this study, we explore the potential of six different large language models (LLMs; GPT-3.5, GPT-4,…
Descriptors: Artificial Intelligence, Test Items, Psychometrics, Educational Assessment
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Qin Ni; Yifei Mi; Yonghe Wu; Liang He; Yuhui Xu; Bo Zhang – IEEE Transactions on Learning Technologies, 2024
Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this…
Descriptors: Cognitive Style, Electronic Learning, Prediction, Identification
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Peter Richtsmeier; Matthew Hopper; Sheri Vasinda; Hannah Krimm; Michelle Moore; Yu Zhang – Reading & Writing Quarterly, 2024
In young adults and adolescents, dyslexia typically is characterized by slow or laborious reading. These reading difficulties are underpinned at least partly by a phonological deficit that disrupts cognitive connections between spoken and written language. Prosodic stress is a phonological property of spoken language reflecting differences in…
Descriptors: Poetry, Syllables, Suprasegmentals, Dyslexia
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Thomas, Sujith; Srinivasan, Narayanan – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
In classification learning of artificial stimuli, participants learn the perfectly diagnostic dimension better than the partially diagnostic dimensions. Also, there is a strong preference for a unidimensional categorization based on the perfectly diagnostic dimension. In a different experimental procedure, called array-based classification task,…
Descriptors: Classification, Bayesian Statistics, Observational Learning, Preferences
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Jussi S. Jauhiainen; Agustin Bernardo Garagorry Guerra – Journal of Information Technology Education: Innovations in Practice, 2025
Aim/Purpose: This article investigates the process of identifying and correcting hallucinations in ChatGPT-4's recall of student-written responses as well as its evaluation of these responses, and provision of feedback. Effective prompting is examined to enhance the pre-evaluation, evaluation, and post-evaluation stages. Background: Advanced Large…
Descriptors: Artificial Intelligence, Student Evaluation, Writing Evaluation, Feedback (Response)
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Heng Zhang; Minhong Wang – Knowledge Management & E-Learning, 2024
With the fast development of artificial intelligence and emerging technologies, automatic recognition of students' facial expressions has received increased attention. Facial expressions are a kind of external manifestation of emotional states. It is important for teachers to assess students' emotional states and adjust teaching activities…
Descriptors: Artificial Intelligence, Models, Recognition (Psychology), Nonverbal Communication
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XinXiu Yang – International Journal of Information and Communication Technology Education, 2024
The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization)…
Descriptors: Prediction, Employment Patterns, College Students, Algorithms
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Gys-Walt Van Egdom; Iris Schrijver; Heidi Verplaetse; Winibert Segers – Interpreter and Translator Trainer, 2024
This article explores the impact of collaboration on target text quality in translator training. By comparing team translations with those by individual peers, and analysing the highest and lowest scoring teams, the authors aimed to understand the impact of collaboration on quality. The comparison indicates that translations in a skills lab…
Descriptors: Foreign Countries, College Students, Translation, Cooperative Learning
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Lee, Chansoon – Educational Measurement: Issues and Practice, 2022
Appropriate placement into courses at postsecondary institutions is critical for the success of students in terms of retention and graduation rates. To reduce the number of students who are misplaced, using multiple measures in placing students is encouraged. However, in practice most postsecondary schools utilize only a few measures to determine…
Descriptors: Classification, Models, Student Placement, College Students
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Melina Verger; Chunyang Fan; Sébastien Lallé; François Bouchet; Vanda Luengo – Journal of Educational Data Mining, 2024
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term…
Descriptors: Algorithms, Prediction, Bias, Classification
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