Computational thinking (CT) is becoming increasingly important for K-12 science education, thus warranting new integrations of CT and science content. This intervention study integrated CT through unplugged, or handwritten, algorithmic explanations of natural selection. As students investigated natural selection in varying contexts (specific and…

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…

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…

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…

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…

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…

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…

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…

The study uses the partial least squares-structural equation modeling (PLS-SEM) algorithm to predict the factors affecting the programming performance (PPE) (low, high) of the students receiving computer programming education. The participants of the study consist of 763 students who received programming education. In the analysis of the data, the…

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…

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…

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…

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…

As emerging technologies are rapidly advancing as part of our societies and everyday life, it is crucial to include and empower all students in learning about computing and advanced technologies. These include technical capabilities of algorithms, such as the use of AI, that enable novel interactions between humans and their environment and give…

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…