Effectiveness of the Technology-AI-Inquiry-Based-Linear (TAIL) Model in Teaching Earth Science Among Grade 10 Students
Mark Joseph S. Ariola
https://analista.in/10.71182/aijmr.2606.0401.2002
Abstract
Earth Science learning in secondary schools often suffers from abstract content, limited instructional scaffolds, and
variable student readiness. Philippine large-scale assessment data also indicate persistent science achievement gaps,
supporting the need for stronger classroom-level innovations. This quasi-experimental study used a non-equivalent
control group pretest-posttest design with Grade 10 students in one school in Agusan del Sur, Philippines, SY 2025–2026.
Two intact classes were assigned to an experimental group (TPCK-informed instruction, AI-supported tools, inquiry
cycles, linear instructional sequencing) and a control group (conventional instruction). A 40-item Earth Science Test was
administered before and after an 8-week intervention. Baseline equivalence was tested via an independent-samples t-
test. Posttest differences were analyzed using ANCOVA with pretest as a covariate. Effect sizes were reported using partial
eta squared and Cohen’s d. Baseline pretest performance did not significantly differ between groups, t(84) = 0.48, p =
.633, d = 0.10. After controlling for pretest scores, the experimental group significantly outperformed the control group,
F(1,83) = 34.52, p < .001, partial η2 = .294. Adjusted mean difference was 5.31 points (95% CI [3.43, 7.18]),
corresponding to a large effect (d = 0.93). The TAIL model produced statistically significant and educationally
meaningful gains in Earth Science achievement. Integrating teacher knowledge (TPCK), guided AI use, inquiry pedagogy,
and coherent sequencing can improve learning outcomes in secondary Earth Science.
Keywords: TPCK,
Artificial,
Intelligence in
Education,
Inquiry-Based
Learning,
Earth Science
Achievement,
Quasi-
Experimental
Design,
Secondary
Science
ANCOVA