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Graves, Scott L., Jr.; Johnson, Kyanna; Phillips, Shanye; Jones, Mark; Jacobs, Marcel – Psychology in the Schools, 2023
Research has indicated that school aged (6-16 years) versions of cognitive assessment instruments have varying levels of linguistic demand, which could impact assessment results for placement purposes. However, a significant limitation to this research is the fact that it has not been replicated with cognitive assessment instruments used in…
Descriptors: Cognitive Measurement, Preschool Children, Cognitive Tests, Intelligence Tests
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Kwok, Man Lung Jonathan; Kwong, Raymond – Education & Training, 2023
Purpose: This study aims to identify the antecedents of learning from mistakes within the student group work setting. Specifically, this study provides empirical evidence of how team-member exchange (TMX) positively leads to learning from mistakes via person-focused interpersonal citizenship behaviours (ICBs) and of the boundary condition of…
Descriptors: Undergraduate Students, Error Correction, Foreign Countries, Cooperative Learning
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Man Kit Lee, Stephen; Liu, Hey Wing; Tong, Shelley Xiuli – Scientific Studies of Reading, 2023
Purpose: Dyslexia is characterized by its diverse causes and heterogeneous manifestations. Chinese children with dyslexia exhibit orthographic, phonological, and semantic deficits across character and radical levels when writing. However, whether character dictation can be used to distinguish children with dyslexia from their typically developing…
Descriptors: Foreign Countries, Dyslexia, Disability Identification, Artificial Intelligence
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Ashaye, Olusoyi Richard; Mahmoud, Ali B.; Munna, Afzal Sayed; Ali, Nurhanisah – Higher Education, Skills and Work-based Learning, 2023
Purpose: This paper focusses on demonstrating the role of social media engagement and considering emotional intelligence (hereafter EI) as a critical concept to successful employment, mainly when individuals fail to reach the desired employment despite "meeting" the role requirements. Design/methodology/approach: The authors adopted a…
Descriptors: Foreign Countries, Young Adults, College Students, Emotional Intelligence
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Dai, Yun – Research in Science Education, 2023
While technology advancement and scientific innovation have created new topics and fields of inquiry in STEM education, external content experts such as university scientists/researchers have been increasingly involved to enhance K-12 teachers' disciplinary understandings and professional development (PD). However, few studies have scrutinized…
Descriptors: Elementary School Teachers, Scientists, Science Instruction, Educational Practices
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Rugh, Michael Sze-hon; Capraro, Mary Margaret; Capraro, Robert Michael – Electronic Journal of e-Learning, 2023
The Dynamic and Interactive Mathematical Expressions (DIME) Map system automatically generates DIME maps, which are personalizable and manipulable concept maps that allow students to interact with the mathematical concepts contained in any portable document format (PDF) textbook or document. A teacher can automatically upload a PDF textbook…
Descriptors: Self Efficacy, Artificial Intelligence, Concept Mapping, Technology Integration
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Bitzenbauer, Philipp – Contemporary Educational Technology, 2023
Large language models, such as ChatGPT, have great potential to enhance learning and support teachers, but they must be used with care to tackle limitations and biases. This paper presents two easy-to-implement examples of how ChatGPT can be used in physics classrooms to foster critical thinking skills at the secondary school level. A pilot study…
Descriptors: Physics, Science Instruction, Teaching Methods, Computer Software
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Umer, Rahila; Susnjak, Teo; Mathrani, Anuradha; Suriadi, Lim – Interactive Learning Environments, 2023
Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…
Descriptors: Prediction, Student Behavior, Models, Academic Achievement
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Fu, Eugene Yujun; Ngai, Grace; Leong, Hong Va; Chan, Stephen C. F.; Shek, Daniel T. L. – Education and Information Technologies, 2023
As a high-impact educational practice, service-learning has demonstrated success in positively influencing students' overall development, and much work has been done on investigating student learning outcomes from service-learning. A particular direction is to model students' learning outcomes in the context of their learning experience, i.e., the…
Descriptors: Service Learning, Prediction, Outcomes of Education, Artificial Intelligence
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Li, Jiansheng; Li, Linlin; Zhu, Zhixin; Shadiev, Rustam – Education and Information Technologies, 2023
A discussion forum is an indispensable part of a massive open online course (MOOC) environment as it enables knowledge construction through learner-to-learner interaction such as discussion of solutions to assigned problems among learners. In this paper, a machine prediction model is built based on the data from the MOOC forum and the depth of…
Descriptors: MOOCs, Discussion, Prediction, Models
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Lee, Chia-An; Huang, Nen-Fu; Tzeng, Jian-Wei; Tsai, Pin-Han – IEEE Transactions on Learning Technologies, 2023
Massive open online courses offer a valuable platform for efficient and flexible learning. They can improve teaching and learning effectiveness by enabling the evaluation of learning behaviors and the collection of feedback from students. The knowledge map approach constitutes a suitable tool for evaluating and presenting students' learning…
Descriptors: Artificial Intelligence, MOOCs, Concept Mapping, Student Evaluation
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Leitner, Maxyn; Greenwald, Eric; Wang, Ning; Montgomery, Ryan; Merchant, Chirag – International Journal of Artificial Intelligence in Education, 2023
Artificial Intelligence (AI) permeates every aspect of our daily lives and is no longer a subject reserved for a select few in higher education but is essential knowledge that our youth need for the future. Much is unknown about the level of AI knowledge that is age and developmentally appropriate for high school, let alone about how to teach AI…
Descriptors: Instructional Design, Game Based Learning, High School Students, Artificial Intelligence
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Eegdeman, Irene; Cornelisz, Ilja; Meeter, Martijn; van Klaveren, Chris – Education Economics, 2023
Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for…
Descriptors: Foreign Countries, Vocational Schools, Dropout Characteristics, Dropout Prevention
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Shiao, Yi-Tzone; Chen, Cheng-Huan; Wu, Ke-Fei; Chen, Bae-Ling; Chou, Yu-Hui; Wu, Trong-Neng – Smart Learning Environments, 2023
In recent years, initiatives and the resulting application of precision education have been applied with increasing frequency in Taiwan; the accompanying discourse has focused on identifying potential applications for artificial intelligence and how to use learning analytics to improve teaching quality and learning outcomes. This study used the…
Descriptors: Foreign Countries, Dropout Prevention, Models, Sustainability
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Wang, Faming; King, Ronnel B.; Chai, Ching Sing; Zhou, Ying – International Journal of Educational Technology in Higher Education, 2023
Despite the importance of artificial intelligence (AI) for university students to thrive in the future workplace, few studies have been conducted to assess and foster their intentions to learn AI. Guided by the situated expectancy-value theory, this study adopted both variable- and person-centered approaches to explore the role of supportive…
Descriptors: Artificial Intelligence, College Students, Intention, Student Attitudes
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