Making the Case for Teaching Social Emotional Learning through Children’s Literature
Bibliographic record
Abstract
In this presentation I propose children’s literature as an effective tool to teach social emotional learning in the classroom. Social emotional learning has increasingly become a focus in many Canadian schools. Social emotional learning has been defined in many ways but is usually accepted to be a combination of developing children’s abilities to self-regulate their feelings, understand and display empathy and make responsible choices. These are skills that can benefit children in a variety of ways that includes increasing self-esteem, reducing conflicts with peers or family members and developing resiliency. Although social emotional skills like empathy are essential for the adequate moral development of children, there is little communication as to the ways qualities like empathy can become teachable skills. This poses a challenge to classroom teachers who are not receiving training or professional development from Social Emotional Learning organizations or research bodies but wish to incorporate elements of the theory into their existing practice and routines. Although some organizations offer scripted lessons and programming to be used in the classroom, programs alone may not be enough. Locally developed resources created for peer-to-peer sharing amongst teachers allow for greater integration and have the added benefit of being customized for each teacher’s unique learning community. This presentation will focus on the integration of social emotional learning at the classroom level using a children’s literature approach. I share some of the resources I have created for this purpose.
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".