Collaborative Learning and Skills of Problem-based Learning: A Case of Nigerian Secondary Schools Chemistry Students
Bibliographic record
Abstract
The role of chemistry in the development of any society cannot be overemphasized. Chemistry students are therefore expected to have improved learning and acquisition of problem solving skills to facilitate the expected development in modern society. Problem-based learning is a student-centered pedagogy which helps students develop problem solving skills and improved knowledge through collaborative and self-directed learning under teacher’s guide. It is supported by Cognitive and Constructive psychologists. However the learning process in Nigeria does not produce students with the required skills and knowledge, because of traditional instructions by teachers, poor learning environment and inadequate learning facilities. The purpose of this study is to investigate the roles of teachers and students in development of collaborative learning and skills in Nigerian Secondary Schools. The study used a qualitative approach with explanatory design. Fifteen (15) chemistry students and a teacher were purposefully selected from one Senior Secondary School (16 years) as participants. The participants received 6 weeks of PBL lessons using a topic purification of water. The researchers collected data during the intervention process through observations field notes and interviews after the PBL lessons were conducted. The data were transcribed, triangulated and analyzed using content analysis. The results showed that the students have improved learning and acquisition of problem solving skills including communication, teamwork and high-order thinking skills due effective collaborative learning activities among them. It is therefore recommended that collaborative learning approach be introduced in Nigerian Secondary Schools.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".