2021 Highschool Big Data Challenge: Paving the path to true equality and equal access in education
Bibliographic record
Abstract
STEM Fellowship’s High School Big Data Challenge is an inquiry-driven experiential learning program that provides students an opportunity to learn and apply the fundamentals of data science – a crucial skill set for a young researcher in the digital age – through independent research projects. The COVID-19 pandemic disrupted high school education, at the same time creating a “fertile ground” for interdisciplinary, student-driven STEM education. This year, we invited students to explore issues of Equality and Equal Access in Education and to suggest their own evidence-based solutions, using Open Data and the principles of Open Science. Students explored many topics, ranging from using machine learning to find hidden socioeconomic factors in access to education, to the efficacy of various modes of instruction. We developed in-depth learning modules designed to lead the student from zero-knowledge to an elementary working proficiency in data science. The students learn a broad range of data analytics tools and programming languages which are useful for uncovering hidden patterns, trends in structured and unstructured data. Some of the tools the students learnt and used includes Python, R, LaTeX, and machine learning. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, Digital Science, Kimberly Foundation, SCWST, CISCO Academy, Canadian Science Publishing, Faculty of Science UofC. It has been a privilege for us to witness the analytical capabilities of the next generation of students firsthand, and we are certain all entrants will continue to demonstrate excellence in their respective careers.
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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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.066 | 0.027 |
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".