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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
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
Cömert, Zeynep; Samur, Yavuz – Interactive Learning Environments, 2023
Almost in every aspect of life, classification and categorization make it easier for humans to analyze complex structures and systems. In games, the classification of the players based on their demographics, behaviors, expectations and preferences of the game is important to increase players' motivation and satisfaction. Likewise, knowing the…
Descriptors: Classification, Student Characteristics, Models, Student Motivation
Eunsook Kim; Nathaniel von der Embse – Journal of Experimental Education, 2024
Using data from multiple informants has long been considered best practice in education. However, multiple informants often disagree on similar constructs, complicating decision-making. Polynomial regression and response-surface analysis (PRA) is often used to test the congruence effect between multiple informants on an outcome. However, PRA…
Descriptors: Congruence (Psychology), Information Sources, Best Practices, Regression (Statistics)
Singelmann, Lauren Nichole – ProQuest LLC, 2022
To meet the national and international call for creative and innovative engineers, many engineering departments and classrooms are striving to create more authentic learning spaces where students are actively engaging with design and innovation activities. For example, one model for teaching innovation is Innovation-Based Learning (IBL) where…
Descriptors: Engineering Education, Design, Educational Innovation, Models
Kim, Eunsook; von der Embse, Nathaniel – Educational and Psychological Measurement, 2021
Although collecting data from multiple informants is highly recommended, methods to model the congruence and incongruence between informants are limited. Bauer and colleagues suggested the trifactor model that decomposes the variances into common factor, informant perspective factors, and item-specific factors. This study extends their work to the…
Descriptors: Probability, Models, Statistical Analysis, Congruence (Psychology)
Mohd Fazil; Angelica Rísquez; Claire Halpin – Journal of Learning Analytics, 2024
Technology-enhanced learning supported by virtual learning environments (VLEs) facilitates tutors and students. VLE platforms contain a wealth of information that can be used to mine insight regarding students' learning behaviour and relationships between behaviour and academic performance, as well as to model data-driven decision-making. This…
Descriptors: Learning Analytics, Learning Management Systems, Learning Processes, Decision Making
El Aissaoui, Ouafae; El Alami El Madani, Yasser; Oughdir, Lahcen; El Allioui, Youssouf – Education and Information Technologies, 2019
Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes and behaviors through surveys and questionnaires. This approach presents several weaknesses including the lack of self-awareness of…
Descriptors: Classification, Cognitive Style, Models, Electronic Learning
Karimi, Hamid; Derr, Tyler; Huang, Jiangtao; Tang, Jiliang – International Educational Data Mining Society, 2020
Online learning has attracted a large number of participants and is increasingly becoming very popular. However, the completion rates for online learning are notoriously low. Further, unlike traditional education systems, teachers, if any, are unable to comprehensively evaluate the learning gain of each student through the online learning…
Descriptors: Online Courses, Academic Achievement, Prediction, Teaching Methods
Karwowski, Maciej – Creativity Theory and Action in Education, 2017
Schools have poor reputation among creativity researchers. Teachers' biases and implicit theories, disruptive behaviors among creative students, and the equivocal pattern of the relationship between creativity and school achievement all contribute to this fact. This chapter presents a new typological model of creativity and demonstrate how this…
Descriptors: Creativity, Teaching Methods, Behavior Problems, Correlation
Renshaw, Tyler L.; Roberson, Anthony J.; Hammons, Kelsie N. – School Mental Health, 2016
The present study used the 2009-2010 sample of the Health Behavior in School-Aged Children Survey (N = 12,642) to investigate the incremental validity of four competing bullying involvement classification schemas, which differ as a function of relative rates of endorsing victimization and perpetration behaviors at school: the standard four-group…
Descriptors: Bullying, Classification, Incidence, Mental Health
Khusniati, Miranita; Parmin; Sudarmin – Journal of Turkish Science Education, 2017
The implementation of local wisdom-based science learning model through reconstruction of indigenous science is hoped to improve student's conservationist character. Through this model, student will be familiarized with science and local wisdom to improve their existing conservationist characters. Characters measured in this study were…
Descriptors: Teaching Methods, Indigenous Knowledge, Science Instruction, Conservation (Environment)
Malkiewich, Laura; Baker, Ryan S.; Shute, Valerie; Kai, Shimin; Paquette, Luc – International Educational Data Mining Society, 2016
Educational games have become hugely popular, and educational data mining has been used to predict student performance in the context of these games. However, models built on student behavior in educational games rarely differentiate between the types of problem solving that students employ and fail to address how efficacious student problem…
Descriptors: Classification, Problem Solving, Educational Games, Models
Li, Yuntao; Fu, Chengzhen; Zhang, Yan – International Educational Data Mining Society, 2017
Since MOOC is suffering high dropout rate, researchers try to explore the reasons and mitigate it. Focusing on this task, we employ a composite model to infer behaviors of learners in the coming weeks based on his/her history log of learning activities, including interaction with video lectures, participation in discussion forum, and performance…
Descriptors: Online Courses, Mass Instruction, Student Behavior, Learning Activities
Ng, Kelvin H. R.; Hartman, Kevin; Liu, Kai; Khong, Andy W. H. – International Educational Data Mining Society, 2016
During the semester break, 36 second-grade students accessed a set of resources and completed a series of online math activities focused on the application of the model method for arithmetic in two contexts 1) addition/subtraction and 2) multiplication/division. The learning environment first modeled and then supported the use of a scripted series…
Descriptors: Word Problems (Mathematics), Mathematics Instruction, Arithmetic, Problem Solving