Barriers to Conducting Primary and Secondary Computing Education Research.
Bibliographic record
Abstract
Motivation.With the growth of primary and secondary computing education research (CER) comes challenges and barriers to conducting this research.Despite previous research investigating barriers to CER focused on diversity, equity and inclusion, there has not yet been an investigation of barriers to conducting CER through the equity lens of the Capacity, Access, Participation, and Experience (CAPE) Framework.Therefore, our project's objective for was to systematically examine the different levels of barriers that researchers in the primary and secondary CER community face when conducting research.Research Questions.Our research questions were: RQ1) What barriers do researchers face in the CER community when investigating the four components of CAPE (Capacity, Access, Participation, Experience)?andRQ2) What barriers do researchers face in the CER community when investigating marginalized groups in their research?Research Methods.We distributed a survey to over 1,500 authors of published CER that asked about the barriers they face when conducting research focused on K-12 computing education.Results.Using thematic analysis, we were able to identify 20 barriers researchers face.The most common themes were funding, time/timing, access to research populations, and lack of CER for administrators.Another interesting result is that funding is the greatest barrier faced by all involved in primary and secondary CER, regardless of role.Implications.Our findings provides insight into why there is minimal research studying certain 1 Gransbury, Heckman, McGill, DeLyser, Rosato ASEE 2024 topics and groups.To address these barriers, the CER community can focus on creating materials, workshops, and professional development initiatives to inform researchers about resources as well as methods for mitigating these barriers.
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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 | 0.170 | 0.293 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".