Effectiveness of Guided and Open Inquiry Instructional Strategies on Science Process Skills and Self-Efficacy of Biology Students in Osun State, Nigeria
Bibliographic record
Abstract
The study determined the effectiveness of guided and open inquiry instructional strategies on the science process skills of students taught Biology in senior secondary schools in Osun State, Nigeria. It also compared the self-efficacy of students taught Biology using guided inquiry with those taught Biology using open inquiry instructional strategies in senior secondary school in Osun State. The goal was to provide empirical information on the effectiveness of guided and open inquiry strategies on students’ learning outcome in Biology. The study adopted the non-equivalent pretest posttest quasi-experimental research design. Two research instruments were used to collect data for the study namely, (i) Biology Process Skills Observation checklist (BPSOC) and (ii) Self-efficacy Rating Scale (SeRS). Data collected were analyzed using appropriate inferential statistics of analysis of Covariance. The results showed that there was no significant difference in the science process skills of Biology students exposed to Open Inquiry and those exposed to guided inquiry strategy (F= 0.785, p>0.05). The results also showed that a significant difference existed in the self-efficacy of students taught using Guided Inquiry and Open Inquiry strategies (F = 11.64, p < 0.05) as those exposed to Open Inquiry had the better self-efficacy score than the other groups as shown in the mean difference between open and guided inquiry strategies. The study concluded that Open inquiry strategy was more effective in improving the self-efficacy of the respondents but was not effective in improving the science process skills of respondents in the study area.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".