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Aaron Salinas; Jeannette T. Crenshaw; Richard E. Gilder; Glenn Gray – Journal of American College Health, 2024
Background: Primary care providers are qualified to treat, diagnose, and manage common mental health issues like anxiety and depression. Anxiety and depression are common among college age students, with the average age of onset occurring in one's late teens to early 20s. Screening tools are commonly used to recognize patients who may be at risk…
Descriptors: Screening Tests, Depression (Psychology), Anxiety, Primary Health Care
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Peter Curtis; Brett Moffett; David A. Martin – Australian Primary Mathematics Classroom, 2024
In this article, the authors explore how the 3C Model can be used to integrate other curriculum areas with mathematics, namely digital technologies. To illustrate the model, they provide a practical example of a teaching sequence. T he 3C Model is designed to create opportunities for applying reasoning and problem-solving skills and learning…
Descriptors: Models, Computer Software, Problem Solving, Mathematics Instruction
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Mohammed Jebbari; Bouchaib Cherradi; Soufiane Hamida; Abdelhadi Raihani – Education and Information Technologies, 2024
With the advancements in technology and the growing demand for online education, Virtual Learning Environments (VLEs) have experienced rapid development in recent years. This demand was especially evident during the COVID-19 pandemic. The incorporation of new technologies in VLEs provides new opportunities to better understand the behaviors of…
Descriptors: MOOCs, Algorithms, Computer Simulation, COVID-19
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Laura Froehlich; Sebastian Weydner-Volkmann – Journal of Learning Analytics, 2024
Educational disparities between traditional and non-traditional student groups in higher distance education can potentially be reduced by alleviating social identity threat and strengthening students' sense of belonging in the academic context. We present a use case of how Learning Analytics and Machine Learning can be applied to develop and…
Descriptors: Learning Analytics, Electronic Learning, Distance Education, Equal Education
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Brady Nash – Journal of Literacy Research, 2024
Scholars have long recognized that reading in digital spaces requires unique skills, strategies, and competencies in comparison to those needed for reading printed text. In recent years, the ubiquity of social media and algorithmically targeted content has radically changed the nature of online reading and meaning making. Technological changes…
Descriptors: Digital Literacy, Critical Literacy, Media Literacy, Reading Instruction
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Karolína Dockalová Burská; Jakub Rudolf Mlynárik; Radek Ošlejšek – Education and Information Technologies, 2024
In cyber security education, hands-on training is a common type of exercise to help raise awareness and competence, and improve students' cybersecurity skills. To be able to measure the impact of the design of the particular courses, the designers need methods that can reveal hidden patterns in trainee behavior. However, the support of the…
Descriptors: Computer Science Education, Information Security, Computer Security, Training Methods
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Siu-Cheung Kong; Wei Shen – Interactive Learning Environments, 2024
Logistic regression models have traditionally been used to identify the factors contributing to students' conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected…
Descriptors: Student Characteristics, Predictor Variables, Comprehension, Computation
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Ean Teng Khor; Dave Darshan – International Journal of Information and Learning Technology, 2024
Purpose: This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course. Design/methodology/approach: The exploration and visualisation of the…
Descriptors: Prediction, Academic Achievement, Electronic Learning, Artificial Intelligence
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Shu-Hsuan Chang; Po-Jen Kuo; Jia Xin Kao; Lee-Jen Yang – Interactive Learning Environments, 2024
With the development of education technology, Smart classroom has evolved to version 2.0. Currently, the meta-analysis literature on the effects of smart classroom-based instruction on academic achievement ignores the impact of technological changes and time on the effect sizes. This study incorporated the impact of technological changes and time,…
Descriptors: Educational Technology, Technology Integration, Instructional Effectiveness, Academic Achievement
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Madeline Day Price; Erin Smith; R. Alex Smith – International Journal of Education in Mathematics, Science and Technology, 2024
Storylines exist about the types of learners who participate and excel in mathematics. To understand how AI chatbots participate in such storylines, we examined ChatGPT's feedback to different learners' mathematical writing in an exploratory study. Learners included academic labels, like gifted and special education, and race/ethnicity, like Black…
Descriptors: Mathematics Education, Artificial Intelligence, Story Telling, Student Characteristics
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Ben Soussia, Amal; Labba, Chahrazed; Roussanaly, Azim; Boyer, Anne – International Journal of Information and Learning Technology, 2022
Purpose: The goal is to assess performance prediction systems (PPS) that are used to assist at-risk learners. Design/methodology/approach: The authors propose time-dependent metrics including earliness and stability. The authors investigate the relationships between the various temporal metrics and the precision metrics in order to identify the…
Descriptors: Performance, Prediction, Student Evaluation, At Risk Students
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Chanaa, Abdessamad; El Faddouli, Nour-eddine – International Journal of Information and Communication Technology Education, 2022
Massive open online courses (MOOCs) have evolved rapidly in recent years due to their open and massive nature. However, MOOCs suffer from a high dropout rate, since learners struggle to stay cognitively and emotionally engaged. Learner feedback is an excellent way to understand learner behaviour and model early decision making. In the presented…
Descriptors: MOOCs, Student Attitudes, Data Analysis, Electronic Learning
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Kalkan, Ömür Kaya – Measurement: Interdisciplinary Research and Perspectives, 2022
The four-parameter logistic (4PL) Item Response Theory (IRT) model has recently been reconsidered in the literature due to the advances in the statistical modeling software and the recent developments in the estimation of the 4PL IRT model parameters. The current simulation study evaluated the performance of expectation-maximization (EM),…
Descriptors: Comparative Analysis, Sample Size, Test Length, Algorithms
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Semiu Salawu; Jo Lumsden; Yulan He – International Journal of Bullying Prevention, 2022
A negative consequence of the proliferation of social media is the increase in online abuse. Bullying, once restricted to the playground, has found a new home on social media. Online social networks on their part have intensified efforts to tackle online abuse, but unfortunately, such is the scale of the problem that many young people are still…
Descriptors: Computer Mediated Communication, Bullying, Computer Software, Computational Linguistics
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Thomas Kraska – Journal of Chemical Education, 2022
An educational lattice model is proposed for the investigation of the influence of the density and indirectly of the pressure on the chemical equilibrium of the ideal gas phase reaction A [equilibrium] 2B. The model can be introduced by a board game simulating a stochastic process. This game can also be used to set up a corresponding computer…
Descriptors: Secondary School Students, Secondary School Science, Chemistry, Science Instruction
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