Embedding Analytical Thinking in Motion Kinematics: Arduino Timing Sensors in Predict-Observe-Explain Instruction
DOI:
https://doi.org/10.15294/jpii.v15i3.47007Keywords:
Analytical Thinking, Arduino-based sensor , Motion Kinematics , Predict-Observe-Explain instruction , Resource-constrained educationAbstract
Physics education in resource-limited contexts faces a persistent dual challenge, namely a shortage of affordable, high-precision laboratory equipment and an instructional culture that privileges rote memorization over genuine higher-order thinking. This gap is particularly acute in rural Thai schools, where students consistently underperform on national science assessments. This study investigates the impact of integrating affordable Arduino-based sensors with Predict-Observe-Explain (POE) instruction on the development of analytical thinking among ninety-four Grade 10 students from six rural schools in Nakhon Phanom, Thailand, and on their satisfaction with that instruction. The study employed a convergent mixed-methods design, combining a one-group pretest-posttest quasi-experimental structure with directed qualitative content analysis, implemented through a twelve-hour intervention across four motion kinematics concepts. Analytical thinking was assessed using the Mixed-Methods Analytical Thinking Test (MM-ATT), a validated 30-item instrument aligned with Marzano’s New Taxonomy and covering classification, categorization, error analysis, generalization, and specification, and satisfaction was measured with a 15-item questionnaire spanning learning process quality, innovation characteristics, and perceived learning outcomes. Quantitative results showed a statistically significant improvement in analytical thinking, with a very large effect size and a moderate normalized gain. Qualitative analysis of student artifacts documented a structural cognitive shift from rote memorization and tautological reasoning toward evidence-based diagnosis and mathematical modeling. Satisfaction ratings placed the experience at the highest level, with the clarity afforded by the Observation phase rated highest of all items. Embedding an affordable Arduino timing sensor within POE instruction therefore develops analytical thinking across all five dimensions and is well received by students, although the twelve-hour sequence proved insufficient for most to transfer kinematic principles to novel contexts. Affordable open-source technology embedded within a theoretically grounded constructivist framework offers a scalable model for physics education in resource-constrained settings.
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