Computation is foundational to learning many mathematics concepts, as well as a functional skill in everyday life. Yet students with autism spectrum disorder (ASD) often have challenges in learning computation skills. The current study aimed to provide quantitative and descriptive analyses of single-case experimental studies on computation…

Computer programming is essential nowadays but still challenging to learn due to its invisible thinking. Current programming environments are mostly designed for operating on computers directly to learn concrete programming, but this approach lacks the support for clarifying learners' thinking processes. On the other hand, using paper and pens…

Background and Context: In order to fully include learners with visual impairments in early programming education, it is necessary to gain insight into specificities regarding their experience of and approach to abstract computational concepts. Objective: In this study, we use the model of the layers of abstraction to explore how learners with…

Perceptual learning theory suggests that perceptual grouping in mathematical expressions can direct students' attention toward specific parts of problems, thus impacting their mathematical reasoning. Using in-lab eye tracking and a sample of 85 undergraduates from a STEM-focused university, we investigated how higher-order operator position (HOO;…

A growing body of research is focusing on integrating artificial intelligence (AI) and computational thinking (CT) to enhance student learning outcomes. Many researchers have designed instructional activities to achieve various learning goals within this field. Despite the prevalence of studies focusing on instructional design and student learning…

The generation of meaning in discourse is regarded as the process of knowledge-building, which can be analyzed by semantic waves. Combining the deep learning systems and polynomial fitting method, we present a method to compute multimodal semantic waves. With this method, four representative examples are discussed for the knowledge-building of…

Due to its task-based, gamified, and interactive features, unplugged programming activity has been widely employed in education and teaching as an activity away from electronic screens and other digital devices. There is ongoing debate over how to help K-9 pupils develop their computational thinking through unplugged programming activities. Based…

Facilitating Computer Science Unplugged (CS-unplugged) activities with reflective thinking activities lead students to in-depth reviews of their decisions and think of ways to solve the CS-unplugged problems. This study aims to evaluate the instruction enriched with reflective thinking activities to develop computational thinking skills. The study…

This study aims to examine the effect of augmented reality-assisted programming education supported by participatory design on the basic and computational thinking skills of preschoolers. In the study, a pretest-posttest single-group semi-experimental design, which is one of the quantitative research methods, was used. The study group of the…

Limited research has been conducted on the influence of computational thinking (CT) dispositions on students' mathematics performance and engagement at the secondary or junior high school level. This study aims to bridge this research gap by developing CT-integrated mathematics lessons that incorporate CT-focused problem-solving and modeling in…

This qualitative case study investigated how computational models can help students engage in scientific practice and influence their emotional, epistemic, and conceptual aspects. Twenty-four sixth-graders were guided to conduct scientific practices as they predicted and modified the computational models on food web using StarLogo Nova. Three…

Background: A resurgence in teaching coding in primary school classrooms has led to a pedagogical swing towards using physical computing and coding to develop students' use of algorithms, computational thinking, and problem-solving skills. Two obstacles impede the optimal development of these objectives: the availability of a suitable pedagogy and…

Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…

Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…

Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational…