The Impact of Project-Based Learning on Achievement and Student Views: The Case of AutoCAD Programming Course
Bibliographic record
Abstract
The aim of the study was to determine the impact of project-based learning on academic achievements of vocational school of higher education students and to investigate their views on the topic. In the study, a mixed descriptive design where qualitative and quantitative data were both collected and analyzed. The quantitative part was conducted with relational screening method and the qualitative part was conducted with descriptive analysis method. The study group included 13 freshmen students attending the vocational school of higher education, building inspection program in a university located in Eastern Anatolia region in Turkey during the 2016-2017 academic year spring semester and selected with convenience sampling method. The study was conducted during the 14 weeks long period where the related programming course was instructed. In the study, quantitative data were collected with an achievement test that measured the academic achievements of the students in AutoCAD programming course. The qualitative data were collected with a structured interview form designed to collect the student views on the related course. Quantitative data were analyzed with the t-test and the descriptive analysis method was used to analyze the qualitative data. In conclusion, it was determined that project-based learning had a positive impact on academic achievement. Furthermore, students expressed that they achieved meaningful learning as a result of the project-based learning application and the method was adequate for the instruction process, improved their interest in the course and related the content with daily life.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".