Listening to students : a study of elementary students' engagement in mathematics through the lens of imaginative education
Bibliographic record
Abstract
This dissertation investigates the problem of student engagement in elementary mathematics through the particular theoretical framework of imaginative education (IE) (Egan, 1997, 2005). The question at the centre of this study is what the use of IE and imaginative lesson planning frameworks means to children and for their engagement in elementary mathematics. For this study, five intermediate-aged elementary students were tracked through a unit of shape and space (geometry). The unit, framed with the binary opposites of vision and blindness, asked students how they might come to understand shape and space as a sighted and as a visually impaired person. Thus a humanized perspective was brought to the learning of mathematics. After the unit the five focus students took part in an individual and a whole-group semi-structured interview with the teacher/researcher. The study used qualitative instrumental case study methods; data sources included students’ mathematics journals, activity pages, transcripts of audio- and videotaped semi-structured individual and group interviews, a teacher/researcher diary, and a detailed unit overview and lesson plans. The study gathered rich descriptive data focused on bringing out the students’ perspective of their experience. Results indicate that the students demonstrated positive engagement with mathematics and that the IE theory, which utilized the students’ imaginations and affective responses, allowed multiple access points through which to connect with the mathematical concepts. Three conclusions of the study were that the students expanded their mathematical awareness through making a variety of connections, they were able to develop self-confidence in their learning of mathematics by using emotions and imagination, and they were able to use cognitive tools, particularly a sense of wonder, to engage with mathematics. The dissertation concludes with a discussion of implications and recommendations in four areas, including the need for further research in different educational contexts, in the interaction of imagination and affective responses, and into characteristics of mathematical engagement such as self-confidence. Recommendations for how future pedagogical practice might use the IE theory and embrace the expansion of students’ perspectives in classroom practice bring the dissertation to a close.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".