The Impact of Online Algorithm Visualization on ICT Students’ Achievements in Introduction to Programming Course
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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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".