Coding as a Literacy Practice in Adult Learning Communities
Bibliographic record
Abstract
This study considered how computing courses for adult learners might be customized to effectively address their reasons for learning to read and write computer code. The view of coding as a literacy practice is the key theme in this study. Street’s (2006) ideological model of literacy along with the perspective of computational participation, are theoretical models used to explore coding as a literacy practice (Kafai & Burke, 2017). Through the vehicle of action research, this study focused on analyzing the delivery of an introductory web languages coding course for female immigrants. This study drew from both the student and teacher perspectives. The study used student feedback collected from online class survey questionnaires and semi-structured interviews. The study also incorporated the teacher’s field notes, a course summary report, and the Teaching Perspectives Inventory survey results (Collins & Pratt, 2011). Findings from this study include these areas of insights: 1) students’ views on the benefits of learning coding, 2) the language and communication challenges students faced, and 3) an overview of some effective teaching tools and approaches. Based on these findings, there is a discussion that considered possible issues related to student engagement in learning web language coding. Included are sections on implications for practice and future research.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".