To the 21st Century, and Beyond! Investigating the Practical Ways that Secondary School Teachers can Develop the “21st Century Competencies” in their Students
Bibliographic record
Abstract
In this study, I aimed to investigate the practical ways that secondary school teachers in different subject areas could better develop the 21st century competencies – critical thinking and problem solving; innovation, creativity, and entrepreneurship; self-directed learning; collaboration; communication; and citizenship – in their students, as outlined by the Ontario Ministry of Education (2016; 2017), as well as the challenges they faced in their pursuit of developing the competencies. While many of the competencies have been encouraged over the past century in education and promoted in policy documents, teachers still struggle with how to most effectively develop the competencies in their students, and I was interested in observing the day-to-day experiences and approaches of my participants. To collect the data used in this qualitative case study, I conducted interviews with eight participants, followed by over sixty hours of in-class observations and ongoing document analysis. To wrap up the data collection, I concluded with another round of interviews before narrowing my final data analysis to five of the eight participants. Data collection took place over the 2017 and 2018 school year at my workplace, an independent school called Gates Tech in the Greater Toronto Area. From my data collection, the research participants felt that there were a few keys to success when it came to developing the competencies in their students. These characteristics included the importance of a supportive school culture and administration, the importance of establishing trust in the classroom, the necessity of co-constructing knowledge with students, viewing the curriculum as flexible, focusing on bringing the outside world into the classroom, having access to (and the confidence to use) multiple technologies and flexible spaces, rethinking assessment practices, and continuing to learn and reflect as a teacher. Challenges faced by the teachers in this study included overload, a fear of failure, a fear of pushback from students and parents, the pressure to be “all-knowing,” and the ongoing pressures to produce high test scores. Ongoing research and support are recommended to better support teachers and students as they pursue the development of the competencies in an ever-changing world.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".