Dataset :Computational thinking related in educational robotic-supported programming activities

Research Hypothesis:
This study hypothesizes that students’ computational thinking (CT) development in educational robotics-supported programming learning environments is influenced by both internal psychological factors (learning motivation, cognitive engagement, cognitive load, and prior programming knowledge) and external technology-related factors (task-technology fit, perceived usefulness, and perceived ease of use). The study integrates the Cognitive-Affective Theory of Multimedia Learning (CATLM), the Technology Acceptance Model (TAM), and the Task-Technology Fit (TTF) model into a unified theoretical framework.

Data Summary:
The dataset includes responses to 54 items measuring seven constructs:

Computational Thinking (CT)

Learning Motivation

Cognitive Load

Cognitive Engagement

Prior Programming Knowledge (measured via a Python programming test)

Perceived Usefulness (PU)

Perceived Ease of Use (PEU)

Task-Technology Fit (TTF)

All Likert-scale responses were rated on a 5-point scale (1 = strongly disagree, 5 = strongly agree). The Python programming test consists of 10 multiple-choice questions assessing basic programming skills (e.g., loops, conditions), scored from 0 to 10.

Data Collection Method:
The data were collected online using the Wenjuanxing platform in March 2025. Participants completed a structured questionnaire and a Python fundamentals test. All participants had prior experience with educational robotics in programming learning. The instruments used were adapted from validated scales and reviewed by experts. Participation was anonymous and voluntary, with informed consent obtained from all participants and guardians.

Notable Findings:

The proposed model explains 54.1% of the variance in computational thinking.

Learning motivation and task-technology fit are the strongest predictors of computational thinking.

Task-technology fit significantly influences perceived usefulness and ease of use, enhancing technology acceptance.

Prior programming knowledge reduces cognitive load and positively impacts computational thinking.

Cognitive engagement positively mediates learning outcomes, while cognitive load exerts a negative effect on engagement.

How to Interpret the Data:

Each row represents an individual student’s complete response.

Items are grouped by construct and follow a consistent coding scheme.

Higher scores reflect more positive perceptions or stronger abilities (e.g., higher CT or motivation).

Factor scores can be used to compute latent variables or replicate the structural equation modeling (PLS-SEM) and artificial neural network (ANN) analyses.

Potential Uses:

Reproducing or extending the SEM/ANN analyses presented in the study.

Exploring the relationships between cognitive, motivational, and technological factors in programming education.

Informing instructional design and technology integration strategies in STEM or robotics education contexts.