Bibliographic record
Abstract
In this paper, I argue that a lack of play and joy in classrooms could be due to our North American standardized education system, which emphasizes achievement outcomes. I argue that this system does not benefit the majority of students, nor the field of mathematics. Many students are negatively affected—both emotionally and academically—by a focus on results. Rather than outcome-driven pedagogy, a focus on learning to enjoy doing mathematics might change the conversation. A kinder, process-driven approach through mathematical play may spark enjoyable teaching and learning. Play (Gadamer, 1960/1989; Huizinga, 1944/1949) has the potential to absorb learners as they seek answers to fun yet challenging mathematics problems. The experience of flow is similar to that of play (Csikszentmihalyi, 2000); when playing, learners get a chance to practice and elaborate on their existing skills in manners that suspend notions of time. When the play releases them from its grasp, learners experience the joy from solving problems. Dewey (1916) considered play to be purposeful activity that sponsors a child’s growth. Teachers could capitalize on this for growth in learning. Learning to bring mathematical play into the classroom requires intention, an inviting attitude, knowledge of the types of problems that invoke play, and knowledge of how to connect playful problems to mathematical concepts and curriculum. Such engaging experiences with mathematics could sponsor joyful engagement in mathematics and an intrinsic desire to learn more.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".