POSTER: What work habits are being assessed across Canada?
Bibliographic record
Abstract
Introduction While academic achievement is associated with employment, educational and social opportunities, it is not the only predictor. Factors such as perseverance, collaboration, and self-regulation are independently associated with positive life outcomes (e.g., Borghans, et al., 2008, Duckworth, et al., 2007). Furthermore, it is both desirable and possible to develop these capacities in students (Heckman, Pinto, & Savelyev 2013; Levin 2012). Perhaps in response to this awareness, educators are expected to help students develop not just curricular knowledge, but also skills and behaviours considered essential for success in the modern workplace (Conference Board of Canada, 2014). Within the Canadian context, educators report on ‘work habits’ as well as grades. While there is a national consensus that teachers should assess and report upon elements of student performance beyond achievement, there has been no study describing what work habits are being assessed, nor how they are reported upon. Objectives The purpose of this research is to determine the: work habits teachers are expected to measure and report upon across Canada; grade level and regional differences in the assessment and reporting of work habits; commonalities and differences in the assessment and reporting of work habits. Design/Methodology This research will analyse publicly accessible documents and websites from ministries of education and urban school boards from all 10 provinces. Policy documents and report cards will be scrutinized to determine which learning skills and work habits are assessed in different jurisdictions, and how they are reported upon (e.g. comments, letter grades, or descriptors). Further analysis will examine grade level differences in the assessment and reporting of these skills and habits, with an eye to finding common trends and themes across Canada. Document analyses will be supplemented by telephone interviews to provide additional context. Results Data collection is ongoing. Conclusion To be determined after data collection and analysis are complete. Bibliography Borghans, L., Duckworth, A. L., Heckman, J. J., & Bas, t. W. (2008). The economics and psychology of personality traits. Journal of Human Resources, 43(4), 972-1059. Conference Board of Canada (2014). Employability Skills 2000+. Retrieved from, http://www.conferenceboard.ca/Libraries/educ_public/esp2000.sflb. Accessed Jan. 13, 2015. Duckworth, A. L., Peterson, C., Matthews, M.D., and Kelly, D.R. (2007) Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology , Vol 92(6), Jun 2007, 1087-1101. Heckman, J., Pinto, R., and Savelyev, P. (2013). Understanding the Mechanisms through Which an Influential Early Childhood Program Boosted Adult Outcomes. American Economic Review , 103(6), 2052-86. Levin, H. (2012). More than just test scores. Prospects 42(3), 269–284.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".