Evaluating the Impact of Smart Learning-Based Inquiry on Enhancing Digital Literacy and Critical Thinking Skills
Bibliographic record
Abstract
The erosion of essential competencies required for the development of digital literacy and critical thinking skills in the 21st century-attributable to factors such as underutilization of the internet for educational purposes, students' limited abilities in digital operations, information gathering, communication, collaboration, and a deficiency in critical analysis or selection of information-necessitates innovative educational strategies.The smart learning-based inquiry (SLBI) strategy, an advancement from traditional inquiry methods, aims to deepen understanding of learning concepts and problem-solving through digital technology application.This research evaluated the efficacy of the SLBI strategy in augmenting students' digital literacy and critical thinking abilities.Employing a quasiexperimental design with pretest-posttest control groups, the study utilized critical thinking tests and digital literacy questionnaires for data collection.Instrument validity was assessed using the Karl Pearson moment product test formula, yielding r values ranging from 0.465 to 0.724 for the test instrument and 0.556 to 0.945 for the questionnaire.Reliability was verified through Cronbach's alpha, with r values of 0.945 for the test instrument and 0.805 for the questionnaire.Descriptive and inferential statistical analyses were conducted to ascertain the strategy's effectiveness post-implementation. Results indicated that the SLBI strategy, encompassing six stages (orientation, conceptualization, investigation, designing digital reports, reflection, and publishing), significantly improved digital literacy (effect size 2.548, categorized as large) and critical thinking skills (effect size 1.504, also categorized as large) relative to traditional inquiry learning methods.These findings suggest that educators should consider incorporating SLBI strategies to enhance learning outcomes.Furthermore, the research opens avenues for future studies to explore the applicability of SLBI in fostering other competencies such as creative thinking, communicative skills, and learning motivation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".