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Gary K. W. Wong; Shan Jian; Ho-Yin Cheung – Education and Information Technologies, 2024
This study examined the developmental process of children's computational thinking using block-based programming tools, specifically algorithmic thinking and debugging skills. With this aim, a group of children (N = 191) from two primary schools were studied for two years beginning from the fourth grade, as they engaged in our block-based…
Descriptors: Thinking Skills, Skill Development, Computation, Algorithms
Shabnam Ara S. J.; Tanuja Ramachandriah; Manjula S. Haladappa – Online Learning, 2025
Predicting learner performance with precision is critical within educational systems, offering a basis for tailored interventions and instruction. The advent of big data analytics presents an opportunity to employ Machine Learning (ML) techniques to this end. Real-world data availability is often hampered by privacy concerns, prompting a shift…
Descriptors: Learning Analytics, Privacy, Artificial Intelligence, Regression (Statistics)
Ilja Cornelisz; Chris van Klaveren – npj Science of Learning, 2022
Longitudinal randomized controlled trials generally assign individuals randomly to interventions at baseline and then evaluate how differential average treatment effects evolve over time. This study shows that longitudinal settings could benefit from "Recurrent Individual Treatment Assignment" ("RITA") instead, particularly in…
Descriptors: Longitudinal Studies, Randomized Controlled Trials, Intervention, Assignments
Xia, Xiaona – Interactive Learning Environments, 2023
Interactive learning environments can generate massive learning behavior data and the support of learning behavior big data can ensure the completeness of data analysis and robustness of relationship verification. In this study, learning behaviors are divided into training set and testing set, BP neural network and recurrent Elman network are…
Descriptors: Interaction, Intervention, Student Behavior, Educational Environment
Chuan Cai; Adam Fleischhacker – Journal of Educational Data Mining, 2024
We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most…
Descriptors: College Students, Student Attrition, Dropouts, Potential Dropouts
Line Have Musaeus; Deborah Tatar; Peter Musaeus – Journal of Biological Education, 2024
Computational modelling is widely used in biological science. Therefore, biology students need to learn computational modelling. However, there is a lack of evidence about how to teach computational modelling in biology and what the effects are on student learning. The purpose of this intervention-control study was to investigate how knowledge in…
Descriptors: Computation, Models, High School Students, Biology
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
Rita Neves Rodrigues; Cecília Costa; Sónia Brito-Costa; Maryam Abbasi; Fernando Martins – Educational Process: International Journal, 2025
Background/purpose: The Computational Thinking ability has become a fundamental skill in the 21st century and has been integrated into educational curricula in various countries. For this curricular integration to be effective, it is essential that teachers are prepared to incorporate the development of this competency into their practices. In…
Descriptors: Thinking Skills, Preservice Teachers, Teacher Education Programs, Problem Solving
Construction and Analysis of a Decision Tree-Based Predictive Model for Learning Intervention Advice
Chenglong Wang – Turkish Online Journal of Educational Technology - TOJET, 2024
The rapid development of education informatization has accumulated a large amount of data for learning analytics, and adopting educational data mining to find new patterns of data, develop new algorithms and models, and apply known predictive models to the teaching system to improve learning is the challenge and vision of the education field in…
Descriptors: Decision Making, Prediction, Models, Intervention
Xing, Wanli; Pei, Bo; Li, Shan; Chen, Guanhua; Xie, Charles – Interactive Learning Environments, 2023
Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows…
Descriptors: Learning Analytics, Engineering Education, Prediction, Design
Nitesh Kumar Jha; Plaban Kumar Bhowmik; Kaushal Kumar Bhagat – Educational Technology Research and Development, 2024
A majority of research in Computational Thinking (CT) mainly focuses on teaching coding to school students. However, CT involves more than just coding and includes other skills like algorithmic thinking. The current study developed an Online Inquiry-based Learning Platform for Computational Thinking (CT-ONLINQ) that follows Inquiry-Based Learning…
Descriptors: Thinking Skills, Computer Science Education, Comparative Analysis, Problem Solving
Stott, Angela Elisabeth – Chemistry Education Research and Practice, 2023
The unit factor method, a generic strategy for solving any proportion-related problem, is known to be effective at reducing cognitive load through unit-cancellation providing step-by-step guidance. However, concerns have been raised that it can be applied mindlessly. This primarily quantitative prepost study investigates the efficacy of…
Descriptors: Chemistry, Science Instruction, Instructional Effectiveness, Teaching Methods
Xu Qin; Fan Yang – Grantee Submission, 2022
Causal inference regarding a hypothesized mediation mechanism relies on the assumptions that there are no omitted pretreatment confounders (i.e., confounders preceding the treatment) of the treatment-mediator, treatment-outcome, and mediator-outcome relationships, and there are no posttreatment confounders (i.e., confounders affected by the…
Descriptors: Simulation, Correlation, Inferences, Attribution Theory
Carol McDonald Connor; Henry May; Nicole Sparapani; Jin Kyoung Hwang; Ashley Adams; Taffeta S. Wood; Sarah Siegal; Cassidy Wolfe; Stephanie Day – Grantee Submission, 2022
Bringing effective, research-based literacy interventions into the classroom is challenging, especially given the cultural and linguistic diversity of today's classrooms. We examined the promise of Assessment-to-Instruction (A2i) technology redesigned to be used at scale to support teachers' implementation of the individualized student instruction…
Descriptors: Individualized Instruction, Kindergarten, Primary Education, Intervention
Carol McDonald Connor; Henry May; Nicole Sparapani; Jin Kyoung Hwang; Ashley Adams; Taffeta S. Wood; Sarah Siegal; Cassidy Wolfe; Stephanie Day – Journal of Educational Psychology, 2022
Bringing effective, research-based literacy interventions into the classroom is challenging, especially given the cultural and linguistic diversity of today's classrooms. We examined the promise of Assessment-to-Instruction (A2i) technology redesigned to be used at scale to support teachers' implementation of the individualized student instruction…
Descriptors: Individualized Instruction, Kindergarten, Primary Education, Intervention