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Dapeng Qu; Ruiduo Li; Tianqi Yang; Songlin Wu; Yan Pan; Xingwei Wang; Keqin Li – IEEE Transactions on Learning Technologies, 2024
There are many important and interesting academic competitions that attract an increasing number of students. However, traditional student team building methods usually have strong randomness or involve only some first-class students. To choose more suitable students to compose a team and improve students' abilities overall, a competition-oriented…
Descriptors: Competition, Teamwork, Student Behavior, Methods
Soland, James – Educational Measurement: Issues and Practice, 2023
Most individuals who take, interpret, design, or score tests are aware that examinees do not always provide full effort when responding to items. However, many such individuals are not aware of how pervasive the issue is, what its consequences are, and how to address it. In this digital ITEMS module, Dr. James Soland will help fill these gaps in…
Descriptors: Student Behavior, Tests, Scores, Incidence
Jiaxian Ye; Lawrence Jun Zhang; Helen Dixon – Assessment in Education: Principles, Policy & Practice, 2025
Student agency is a key feature in feedback practices. Student feedback agency is generally defined as students' active engagement in the feedback process. Its conceptualisation has evolved from individualistic views, through unidirectional structure-agency perspectives, to more socially oriented approaches. However, this commentary argues that…
Descriptors: Personal Autonomy, Feedback (Response), Social Cognition, Students
Cortney DiRussa; Samantha Coyle-Eastwick; Britney Jeyanayagam – International Journal of Bullying Prevention, 2025
Bullying victimization is a school problem that warrants attention. While most work has focused on understanding bullies and victims, it is important that research explore how to promote bystander behavior during bullying as a mechanism to deter bullying in schools. Perceptions of the school climate may impact the likelihood of a student's…
Descriptors: Bullying, Intervention, Middle School Students, Prevention
Vikki Pollard; Christine Armatas – Online Learning, 2025
The Interactive, Constructive, Active, Passive (ICAP) Framework (Chi & Wylie, 2014) is used to review and develop active learning in higher education. It is a hierarchical model based on overt behaviours seen by the teacher in the classroom. This principle is acknowledged as a limitation, especially in the case of online modes of study. In…
Descriptors: Active Learning, Online Courses, Asynchronous Communication, Feedback (Response)
Caihong Feng; Jingyu Liu; Jianhua Wang; Yunhong Ding; Weidong Ji – Education and Information Technologies, 2025
Student academic performance prediction is a significant area of study in the realm of education that has drawn the interest and investigation of numerous scholars. The current approaches for student academic performance prediction mainly rely on the educational information provided by educational system, ignoring the information on students'…
Descriptors: Academic Achievement, Prediction, Models, Student Behavior
Juan D. Pinto; Luc Paquette – International Educational Data Mining Society, 2025
The increasing use of complex machine learning models in education has led to concerns about their interpretability, which in turn has spurred interest in developing explainability techniques that are both faithful to the model's inner workings and intelligible to human end-users. In this paper, we describe a novel approach to creating a…
Descriptors: Artificial Intelligence, Technology Uses in Education, Student Behavior, Models
Ken Rigby – International Journal of Bullying Prevention, 2024
This article examines alternative and supplementary ways in which theorists and researchers have sought to account for bullying behavior among students in schools. Contemporary explanations acknowledge the variety, complexity, and interactivity of both person and environmental factors in determining acts of bullying in schools. Two explanatory…
Descriptors: Bullying, Schools, Student Behavior, Models
Yikai Lu; Lingbo Tong; Ying Cheng – Journal of Educational Data Mining, 2024
Knowledge tracing aims to model and predict students' knowledge states during learning activities. Traditional methods like Bayesian Knowledge Tracing (BKT) and logistic regression have limitations in granularity and performance, while deep knowledge tracing (DKT) models often suffer from lacking transparency. This paper proposes a…
Descriptors: Models, Intelligent Tutoring Systems, Prediction, Knowledge Level
C. Rashaad Shabab – Teaching Mathematics and Its Applications, 2024
This paper applies the well-known cognitive bias of loss aversion from behavioural economics to student decisions over engagement with mathematically demanding coursework. This bias is shown to predict behaviour that is consistent with mathematics anxiety in a dynamic model of student engagement. It is shown that these forces can imply…
Descriptors: Mathematics Anxiety, Mathematics Instruction, Difficulty Level, Student Behavior
Rico-Juan, Juan Ramon; Sanchez-Cartagena, Victor M.; Valero-Mas, Jose J.; Gallego, Antonio Javier – IEEE Transactions on Learning Technologies, 2023
Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an…
Descriptors: Artificial Intelligence, Models, Student Behavior, Feedback (Response)
Khalida Parveen; Abdulelah A. Alghamdi; Nagwan Abdel Samee; Muhammad Shafiq – Journal of Educational Computing Research, 2025
As technology rapidly evolves, generative AI tools are increasingly integrated across various fields, including education. ChatGPT, a well-known language model developed by OpenAI, has gained significant importance in educational settings. This study employed a quantitative, cross-sectional survey design and employed the Unified Theory of…
Descriptors: Artificial Intelligence, Computer Uses in Education, College Students, Foreign Countries
Shuanghong Shen; Qi Liu; Zhenya Huang; Yonghe Zheng; Minghao Yin; Minjuan Wang; Enhong Chen – IEEE Transactions on Learning Technologies, 2024
Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In…
Descriptors: Student Behavior, Electronic Learning, Data Analysis, Models
Liang Tang; Nigel Bosch – International Educational Data Mining Society, 2025
Feature engineering plays a critical role in the development of machine learning systems for educational contexts, yet its impact on student trust remains understudied. Traditional approaches have focused primarily on optimizing model performance through expert-crafted features, while the emergence of AutoML offers automated alternatives for…
Descriptors: Artificial Intelligence, Design, Trust (Psychology), Student Attitudes
Bo Liang; Yali Xiong; Jin Yang; Anya Li; Yunqi Yang – SAGE Open, 2024
Entrepreneurial behavior has been substantially addressed in entrepreneurship literature, but the mechanisms by which social capital influences entrepreneurial behavior among college students remain unclear, especially the potential mediating and moderating interplay among them. Therefore, drawing on social capital theory and the…
Descriptors: Social Capital, Student Behavior, Entrepreneurship, College Students

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