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…

Focusing efficiently on potential weaknesses in the validity argument of writing assessments--such as writing subjectivity, content coverage, criteria vagueness, and raters' incompetence--has been shown to positively enhance teachers' overall writing assessment competence (AC). In this study, we propose a computational bootstrapping model of…

In this study, we used multivariate decoding methods to study processing differences between canonical (montring and count) and noncanonical finger numeral configurations (FNCs). While previous research investigated these processing differences using behavioral and event-related potentials (ERP) methods, conventional univariate ERP analyses focus…

In this paper, a key node mining algorithm of entropy-CRITIC combined weighted GRA-TOPSIS method is proposed, which is based on the network structure features. First, the method obtained multi-dimensional data of students' identities, seating relationships, social relationships, and so on to build a database. Then, the seating similarity among…

Purpose: Dyslexia is characterized by its diverse causes and heterogeneous manifestations. Chinese children with dyslexia exhibit orthographic, phonological, and semantic deficits across character and radical levels when writing. However, whether character dictation can be used to distinguish children with dyslexia from their typically developing…

Computational thinking (CT) skills of pre-service teachers have been explored extensively, but the effectiveness of CT training has yielded mixed results in previous studies. Thus, it is necessary to identify patterns in the relationships between predictors of CT and CT skills to further support CT development. This study developed an online CT…

Computational thinking (CT) is a fundamental ability for learners in today's society. Although CT assessments and interventions have been studied widely, little is known about CT predictions. This study predicted students' CT achievement in the ICILS 2018 using five machine learning models. These models were trained on the data from five European…

Aim/Purpose: This study aims to implement and evaluate a personalized digital learning environment (PDLE) that delivers differentiated instruction for enhancing computational thinking competencies through robotics education. Background: The background emphasizes the growing demand for computational thinking skills in the modern workforce and the…

In the digital age, computational thinking (CT)-based problem-solving skills have emerged as essential competencies. Particularly, students with intellectual disabilities need equal educational opportunities and high-quality informatics education to cultivate CT-based problem-solving skills. However, research on the enhancement of CT-based…

This study explores the current status of computational thinking (CT) skills among first-year university students and examines whether any differences exist across genders, geographic backgrounds and academic disciplines with programming learning experience given the growing interest in computational thinking (CT) in recent years. It also…

This study examines the effects of plugged and unplugged programming tools used in algorithm teaching at the K-12 level on student computational thinking skills and to determine whether gender is a factor in this process. The study group was designed with a control group pre-test--post-test; quasi-experimental model, that consisted of 109 students…

Commonly used methods to simulate the oxidation-reduction (redox) titration curves include the three-step method and the rigorous method. The simple three-step method simulates the redox titration curve with the assumption that the reaction is complete, which is widely used in undergraduate quantitative analysis courses. For the rigorous…

Every educator knows the sinking feeling of a lesson gone wrong. As teachers look around the room and realize that many of their students are just not getting it, they often feel like failures. However, the struggle students experience as they persevere through high-quality challenging tasks is not a sign of failure, but rather a key aspect of…

When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty. And, even if the most important modes can be found, it is difficult to evaluate their relative weights in…

Structural equation modeling (SEM) is being applied to ever more complex data types and questions, often requiring extensions such as regularization or novel fitting functions. To extend SEM, researchers currently need to completely reformulate SEM and its optimization algorithm -- a challenging and time-consuming task. In this paper, we introduce…