A Case Study of Using Machine Learning in K-12 Education
Bibliographic record
Abstract
This full Research-to-Practice paper evaluates a Machine Learning (ML) course as a strategy to introduce Artificial Intelligence (AI) in middle school. AI is a technology that is increasingly present in our daily lives, and it is important that K-12 schools offer their students some basic first steps in this universe. Nonetheless, most initiatives to introduce ML aim at higher education, in undergraduate computing programs, and school initiatives usually lack the use of hardware to learn ML. In this context, we designed and implemented an introductory workshop on AI and ML for middle school students on the fundamentals of AI using TinyML and Arduino, and we assessed their attitudes towards 21st Century skills. Results show some ways how middle school students are impacted with the presentation of ML concepts and practices by building small applications, in addition to providing practice grounding to future educational interventions using TinyML as a tool to familiarize K-12 students with ML. Survey results point to very few post-intervention changes regarding 21st Century skills. Learned lessons point to a need to increase the course workload for more significant changes in students' perceptions.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".