Grade 4 Students' Development of Research Skills Through Inquiry-Based Learning Projects
Bibliographic record
Abstract
Researchers like Harada, Yoshina, Donham, Bishop, Kuhlthau, and Oberg have pointed out the benefits for students to move from rote to inquiry learning. However, "the norm in many classrooms remains teaching practice that results in rote learning and regurgitated facts." In recent years, the Hong Kong government's Education Bureau has put inquiry-based learning as the first emphasis under the new General Studies curriculum for primary schools with the objective of "creating more learning space by removing obsolete content, allowing more time for inquiry-based learning." Many schools are now attempting to incorporate this mode of learning into their curriculum. This study reports on two phases of IBL projects undertaken by 141 grade 4 students, each phase lasting for two to three months. The projects were led by general studies teachers and heavily supported by Chinese-language teachers, the information technology teacher, and the school librarian. Through analyzing the lesson plans, in-class exercises, homework assignments, written reports, presentations by students, and data collected through surveys and interviews, this article focuses specifically on the role of the general studies teachers in guiding students through the inquiry process. It also analyzes the students' development of knowledge and research skills, as well as students' and parents' perceptions of the projects.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".