An Analysis of Mathematics Education Students’ Skills in the Process of Programming and Their Practices of Integrating It into Their Teaching
Bibliographic record
Abstract
Recent developments in technology have changed the learner’s profile and the learning outcomes. Today, with the emergence of higher-order thinking skills and computer literacy skills, teaching through traditional methods is likely to fail to achieve the learning outcomes. That is why; teachers and teacher candidates are expected to have computer literacy skills. Programming is the main focus of this study since it is an important part of computer literacy. The study aims to analyze mathematics education students’ skills in the process of programming and their practices of integrating it into their teaching. The participants of the study are 42 third grade students of an Elementary Mathematics Education Program of a state university in Turkey. Within the study in which theory and practice was carried out simultaneously, the participants were taught the basics of programming and the algorithms with C programming language. The teacher candidates put the theoretical knowledge into practice using the visual programming application by MIT App Inventor at the computer laboratory. In addition, they used the MIT App Inventor visual programming environment to develop programs they will use in teaching mathematics in groups. Given the component of teaching of programming during this process, it is considered that the process of teaching in question will be effective in planning the teaching process of future studies. The reason is that not only it analyses the development of the variables used in this study but also because it takes into consideration the opinions of teacher candidates.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".