Montessori Grade 9 Students and Their Use of an Online Concept Mapping Website: A Case Study Exploration
Bibliographic record
Abstract
This study investigated the impact of an online concept mapping website (Concept Maps for Learning, or CMfL) designed to provide targeted formative feedback to students. The aims of this study were to determine the usefulness of CMfL for both teachers and students, as a tool for instruction and self-regulated learning. Additionally, the impacts of CMfL on supporting student learning were observed. This research site of this study was a Montessori high school, and the participating students were enrolled in the Ontario Grade 9 Academic Mathematics course. The educational philosophies deployed at the research site offered independence and flexibility to students with respect to how the Ontario Grade 9 Academic Mathematics course was approached, and therefore matched the self-regulated learning components of the study. This study measured student achievement across three milestones over the data collection period to analyse any cognitive impact that CMfL had on the participating students. Metacognitive impacts, as well as the students’ perception of usefulness of CMfL, were measured through surveys that were administered at the milestone points. Usefulness of CMfL from the teacher’s perspective was determined through interviews with the teacher. The participating students and teacher were also provided with the opportunity to provide feedback on how CMfL could be improved through the aforementioned surveys and interviews, respectively. The evidence collected over the study suggests that CMfL can be a useful tool for teaching and learning in a self-regulated environment, and that frequent engagement with CMfL may can support student learning. However, there is room for improvement that may increase student adoption and aid teaching strategy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".