Harnessing Microlearning for Effective Professional Development to Enhance Early Years Alphabet Instruction
Bibliographic record
Abstract
Learning the alphabet is often regarded in North American contexts as a quintessential accomplishment of kindergarten (Evans et al., 2006; Foulin, 2005), but teaching and learning the alphabet are complex. The literature demonstrates that educators struggle to effectively teach the alphabet, in part because evidence-based research about supporting foundational literacy skills is not making its way into their practice. The obstacles to educators accessing professional learning that will support them in changing their practice are varied but include a lack of guidance on and consistent access to evidence-based teacher-friendly resources, as well as a lack of funding and release time (Bill & Melinda Gates Foundation, 2014; Ontario Human Rights Commission, 2022). However, the global COVID-19 pandemic provided an impetus for digital innovation, opening the doors to potentially reshape the professional development landscape for educators and offer opportunities for technological advancements and innovative learning approaches like microlearning. This study investigated how technologically-mediated professional learning modules enhanced Ontario educators’ knowledge of evidence-based alphabet instruction. An extensive analysis of the existing literature on alphabet-knowledge learning and instruction was conducted to determine validated research-based content and practices. This analysis informed the development of four microlearning modules, which 16 Canadian teachers then tested. Participating teachers completed pre- and post-intervention surveys and a two-month follow-up survey. Responses to post-intervention survey questions showed that participants overwhelmingly liked the microlearning format. Half of the respondents felt they were applying course content in their teaching. Thematic analysis indicated that: 1) teachers recalled information about tangible materials more than information about teaching methods; and 2) teachers experience a variety of time tensions related to professional learning. The findings present the opportunity to redefine alphabet teaching, span the theory-practice divide in early years literacy instruction, and offer innovative solutions to traditional professional development challenges.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".