Bibliographic record
Abstract
Learning to code has a reputation for being difficult (Gomes & Mendes, 2007; Jenkins, 2002), and requires a variety of skills, such as math and complex problem solving, that are challenging for many students (Foote, 2014; Gomes & Mendes, 2007; Jenkins, 2002)—especially for those students beginning a college program (Oblinger, 2003). Often, students experience high levels of anxiety even before a programming course has started (Jenkins, 2002). This is particularly true for students who are required to take a course in coding, but who do not plan to continue on to a career in this field. Anecdotally, these students find the fundamentals of code difficult, and often end up “hacking” their way through the course. One approach to addressing this anxiety that has been used with children and youth is to teach code using robotics (Kurebayashi, Kamada, & Kanemune, 2006; McGill, 2012). Learning to code using robotics was found to have many positive effects: a) it allows students to more easily connect individual lines of code to their result (Kurebayashi et al., 2006); b) it stimulates intrinsic motivation (Kurebayashi et al., 2006; McGill, 2012); and c) it increases overall student grades (McGill, 2012).However, there is little to no literature on this type of approach with adult learners. Therefore, the purpose of this study was to look at the effects of incorporating robotics in a playful context (Plass, Homer, & Kinzer, 2015), in a series of “Introduction to Coding” courses at the post secondary level.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".