A Case Study of Using Machine Learning in K-12 Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it