Experiential Learning through the Internet of Things in Primary STEM Education: Evidence from a Paired-Sample Pre-Test–Post-Test Study

Internet of Things (IoT) technologies are increasingly proposed as a vehicle for hands-on, data-rich STEM learning, yet empirical evidence from primary-school settings remains limited. Grounded in Kolb's experiential learning theory and Bandura's self-efficacy theory, this study examined whether self-reported computational thinking self-efficacy (CT-SE), learning attitude towards STEM (LAT-STEM), and programming self-efficacy (P-SE) changed following participation in an IoT-based STEM activity. A one-group pre-test–post-test design was used with 60 matched respondents: 45 primary-school pupils (mean age 11.09 years) and 15 adult observers (mean age 40.47 years). The 25-item instrument (CT-SE, 10 items; LAT-STEM, 8 items; P-SE, 7 items) used a five-point agreement scale and was internally consistent at both occasions (full-scale α = .92 pre, .95 post). Paired-samples t-tests showed a significant increase in the overall composite from M = 3.08 to M = 3.64 (mean difference 0.56, 95% CI [0.38, 0.74], t(59) = 6.38, p < .001, dz = 0.82). All three constructs increased, most strongly for P-SE (dz = 0.94) and least for LAT-STEM (dz = 0.36). At item level, 22 of 25 items increased at the unadjusted .05 level; after Holm correction for multiple comparisons, 16 items remained significant, all in the CT-SE and P-SE sections, whereas no LAT-STEM item survived correction. The LAT-STEM items had the highest baseline means, consistent with a ceiling constraint. Gains did not differ significantly between pupils and adult observers or between genders, although subgroup analyses were underpowered. The findings describe change over time rather than a causally established effect, but they show that a short IoT-based activity is associated with measurable shifts in pupils' self-efficacy beliefs and indicate that attitudinal outcomes shift least.

Internet of Things; primary STEM education; experiential learning; computational thinking self-efficacy; pre-test–post-test design

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