The Impact of Online Algorithm Visualization on ICT Students’ Achievements in Introduction to Programming Course
Why this work is in the frame
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Bibliographic record
Abstract
Online Algorithm Visualization (OAV) is one of the recent developments in the instructional technology field that aims to help students handle difficulties faced when they begin to learn programming. This study aims to investigate the effect of online algorithm visualization on students’ achievement in the introduction to programming course. To achieve this goal, quantitative and qualitative investigations were conducted in a mixed method design. Participants of the study consisted of 40 ICT students who were taking the introduction to programming course for the first time in fall semester. Students were randomly assigned to treatment (n = 20) and control (n = 20) groups. During the first 4 weeks, the treatment group students participated in OAV. Concurrently, students in the control group were taught the semantics of programming and algorithm through traditional approaches. An achievement test consisting of six questions was used to measure ICT students’ performance in computer programming at the end of the introduction to programming course. An open-ended survey and semi-structured interviews were also used to gain qualitative insight. The quantitative data were analyzed using t-test and ANOVA statistical analysis. The qualitative data were analyzed using content analysis techniques. Results showed that the experimental group, for which OAV treatment was implemented, had a higher mean score than the control group, for which traditional methods were implemented. There was a significant mean difference between the experimental group (M = 51.85, SD = 20.34) and the control group (M = 38.75, SD = 12.86). In qualitative analysis, five themes emerged. Students highlighted that OAV contributed to their algorithmic thinking (28%) and progressive thinking abilities (7%), and allowed for explorative learning (7%). Although their reasons varied, most of the students perceived OAV as an engaging instructional tool for learning computer programming.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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