School Laboratories with Low-Cost Sensors in Lower Secondary Education: A Scoping Review of Inquiry, Data Literacy, and Scientific Argumentation
DOI:
https://doi.org/10.64747/ycszwm44Keywords:
low-cost sensors, scientific inquiry, data literacy, scientific argumentation, secondary educationAbstract
Introduction: Low-cost sensors allow school questions to generate data series on temperature, light, noise, motion, pH, or air quality. Yet measurements alone do not ensure inquiry or scientific thinking: instrument validity, role distribution, and scaffolding for uncertainty shape what students learn. Objective: To map evidence published through February 28, 2026, on school laboratories and investigations using low-cost sensors and their relationships with inquiry, data literacy, and scientific argumentation in lower secondary education and adjacent age groups. Methods: A JBI-oriented scoping review was reported using PRISMA-ScR. Ten historical Crossref searches retrieved 600 records; 515 were unique, 24 passed the automated filter, and 16 reports were added through targeted citation and DOI tracing. Forty reports were assessed and 20 sources were included. Design, sample, device, teacher role, outcomes, equity, and limitations were extracted. Results: School evidence clustered around air quality, environment, pH, and physical computing. Pre-post studies suggested gains in knowledge, confidence, or conceptions, but were small, non-randomized, and multicomponent. Citizen science increased authenticity and participation; its educational quality depended on students asking questions, inspecting data, discussing anomalies, and communicating conclusions. Technical sources showed that calibration, drift, and environmental conditions prevent treating a sensor reading as exact truth. Inquiry and argumentation frameworks emphasized teacher guidance, alternative explanations, and explicit uncertainty. Conclusions: Sensors are educationally productive when treated as objects of critique rather than automatic data collectors. Responsible design connects questions, calibration, metadata, visualization, competing explanations, argument construction, and participation audits. Current evidence supports feasibility and plausible mechanisms, but not a general causal effect or Ecuadorian outcomes.
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