Effect of algorithms’ multiple representations in the context of programming education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to compare the effect of different representations while teaching basic algorithmic concepts to novice programmers. Design/methodology/approach A learning activity was designed and mediated with two conceptually different learning environments, each one used by a different group. The first group used the learning environment “Visual Flowchart”, which enables the students to construct and examine an algorithm using visual representation based on actual flowchart objects. The second group used the software “Language Interpreter”, which allows the students to express an algorithms using pseudocode. Findings Analysis of results among the two groups showed no statistically significant differences in the students’ performance with respect to the tool they used to solve the activity, the school stream they followed in high school and their gender. Research limitations/implications The lack of difference among the two groups could be attributed to the non‐complicated nature of the given activity. In addition, longitudinal studies of the effect of the different representation in the frame of an introductory first semester academic course in computer science could further validate the results. Practical implications Two alternative learning environments aimed to support learning of basic programming skills. Originality/value Two alternative learning environments were presented and discussed in detail, aimed to support learning of basic programming skills. The conclusions of the present study are in contrast to the research that has taken place in the past which compared usage of flowcharts and pseudocode to educate novice programmers, and wider adoption of “flowcharts” was depicted.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it