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Kitto, Kirsty; Hicks, Ben; Shum, Simon Buckingham – British Journal of Educational Technology, 2023
An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results…
Descriptors: Causal Models, Learning Analytics, Educational Theories, Artificial Intelligence
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Pouls, Claudia; Jeandarme, Inge – Journal of Mental Health Research in Intellectual Disabilities, 2023
Background: The ARMIDILO-S is advocated as a promising tool for assessing dynamic risk factors in sex offenders with intellectual disabilities (SOIDs). However, research remains scarce. The present study aimed to further validate this instrument in SOIDs. Method: The study prospectively followed 38 SOIDs for up to one year to test the accuracy of…
Descriptors: Test Reliability, Test Validity, Sexual Abuse, Criminals
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Sghir, Nabila; Adadi, Amina; Lahmer, Mohammed – Education and Information Technologies, 2023
The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice…
Descriptors: Prediction, Learning Analytics, Artificial Intelligence, Data Collection
Akmanchi, Suchitra; Bird, Kelli A.; Castleman, Benjamin L. – Annenberg Institute for School Reform at Brown University, 2023
Prediction algorithms are used across public policy domains to aid in the identification of at-risk individuals and guide service provision or resource allocation. While growing research has investigated concerns of algorithmic bias, much less research has compared algorithmically-driven targeting to the counterfactual: human prediction. We…
Descriptors: Academic Advising, Artificial Intelligence, Algorithms, Prediction
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Gökhan Demirhan – International Journal of Contemporary Educational Research, 2023
The purpose of this study is to determine the role of school culture types on perceived school leadership capacity. The study group of this research, consisted of 483 teachers. Descriptive statistics were used to analyze the data and Pearson Correlation Coefficients were calculated to determine the relationships between variables. Hierarchical…
Descriptors: School Culture, Prediction, Leadership, Teacher Attitudes
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Pham Sy Nam; Ngoc-Giang Nguyen; Hoa Anh Tuong; Ben Haas; Zsolt Lavicza; Yves Kreis – International Journal for Technology in Mathematics Education, 2023
Problem-based learning puts students in situations that suggest problems without providing instructions and available knowledge. Therefore, when using problem-based learning, students need to be flexible, self-disciplined, active and self-occupied with knowledge and turn the knowledge the teacher intends to impart into their knowledge. For locus…
Descriptors: Computer Software, Mathematics Instruction, Teaching Methods, Problem Based Learning
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Duygu F. Safak; Holger Hopp – Studies in Second Language Acquisition, 2023
This study investigates whether cross-linguistic differences affect how adult second language (L2) learners use different types of verb subcategorization information for prediction in real-time sentence comprehension. Using visual world eye-tracking, we tested if first language (L1) German and L1 Turkish intermediate-to-advanced learners of L2…
Descriptors: Linguistics, Adults, Second Language Learning, Verbs
Robin Clausen – Grantee Submission, 2023
Research over the monitoring of at-risk youth. Behavioral interventions have been the primary focus of this literature past two decades has focused on one aspect of dropout prevention: early identification an however, there is renewed emphasis placed on attendance and academic risk factors under ESSA. Recognizing a need to promote early…
Descriptors: Identification, Dropouts, At Risk Students, Prediction
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Melissa Bond; Hassan Khosravi; Maarten De Laat; Nina Bergdahl; Violeta Negrea; Emily Oxley; Phuong Pham; Sin Wang Chong; George Siemens – International Journal of Educational Technology in Higher Education, 2024
Although the field of Artificial Intelligence in Education (AIEd) has a substantial history as a research domain, never before has the rapid evolution of AI applications in education sparked such prominent public discourse. Given the already rapidly growing AIEd literature base in higher education, now is the time to ensure that the field has a…
Descriptors: Meta Analysis, Artificial Intelligence, Databases, Higher Education
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Lixiang Yan; Lele Sha; Linxuan Zhao; Yuheng Li; Roberto Martinez-Maldonado; Guanliang Chen; Xinyu Li; Yueqiao Jin; Dragan Gaševic – British Journal of Educational Technology, 2024
Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are…
Descriptors: Educational Technology, Artificial Intelligence, Natural Language Processing, Educational Innovation
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Lathifaturrahmah Lathifaturahmah; Toto Nusantara; Subanji Subanji; Makbul Muksar – Mathematics Teaching Research Journal, 2024
The capacity to generate prediction is indispensable in daily existence, particularly amidst the swift transformations that are occurring on a global scale. Therefore, this study aimed to analyze the levels of prediction ability among mathematics students when presented with data in graphs. A qualitative approach was adopted, involving 37…
Descriptors: Mathematics Instruction, Mathematics Skills, Prediction, COVID-19
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Gerardo Ibarra-Vazquez; Maria Soledad Ramirez-Montoya; Mariana Buenestado-Fernandez – IEEE Transactions on Learning Technologies, 2024
This article aims to study the performance of machine learning models in forecasting gender based on the students' open education competency perception. Data were collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. The analysis comprises 1) a study of the students' perceptions of knowledge, skills, and…
Descriptors: Gender Differences, Open Education, Cross Cultural Studies, Student Attitudes
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Jan Delcker; Joana Heil; Dirk Ifenthaler; Sabine Seufert; Lukas Spirgi – International Journal of Educational Technology in Higher Education, 2024
The influence of Artificial Intelligence on higher education is increasing. As important drivers for student retention and learning success, generative AI-tools like translators, paraphrasers and most lately chatbots can support students in their learning processes. The perceptions and expectations of first-years students related to AI-tools have…
Descriptors: Artificial Intelligence, Learning Processes, Higher Education, College Freshmen
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Isaac N. Treves; Jonathan Cannon; Eren Shin; Cindy E. Li; Lindsay Bungert; Amanda O'Brien; Annie Cardinaux; Pawan Sinha; John D. E. Gabrieli – Journal of Autism and Developmental Disorders, 2024
Some theories have proposed that autistic individuals have difficulty learning predictive relationships. We tested this hypothesis using a serial reaction time task in which participants learned to predict the locations of a repeating sequence of target locations. We conducted a large-sample online study with 61 autistic and 71 neurotypical…
Descriptors: Autism Spectrum Disorders, Adults, Learning Processes, Visual Perception
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Xiaoli Shu; Xiaobing Shu; Weihua Ouyang – Journal of Career Development, 2024
Based on career construction theory, this study explored the relationship and mediating mechanisms between career adaptability and secondary school students' academic engagement. Four hundred and eight secondary school students from three Chinese secondary schools were surveyed using a three-wave time-lag design. The study found that career…
Descriptors: Career Choice, Secondary School Students, Learner Engagement, Academic Achievement
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