Toward Creating Software Architects Using Mobile Project-Based Learning Model (Mobile-PBL) for Teaching Software Architecture
Bibliographic record
Abstract
Project-based learning (PBL) promotes increased levels of learning, deepens student understanding of acquired knowledge, and improves learning motivation. Students develop their ability to think and learn independently through depending on themselves in searching for knowledge, planning, exploration, and looking for solutions to practical problems. Information availability, student engagement, and motivation to learn all increase with mobile learning. The teaching process may be enhanced by combining the two styles. This paper proposes and evaluates a teaching model called Mobile Project-Based Learning (Mobile-PBL) that combines the two learning styles. The paper investigates how significantly Mobile-PBL can benefit students. The traditional lecture method used to teach the software architecture module in the classroom is not sufficient to provide students with the necessary practical experience to earn a career as software architects in the future. Therefore, the first author tested the use of the model for teaching the software architecture module at Philadelphia University’s Software Engineering Department on 62 students who registered for a software architecture course over three semesters. She compared the results of using the model for teaching with those results that were obtained when using the project-based learning (PBL) approach alone. The students’ opinions regarding the approach, any problems they had, and any recommendations for improvement were collected through a focus group session after finishing each semester and by distributing a survey to students to evaluate the effectiveness of the used model. Comments from the students were positive, according to the findings. The projects were well-received by the students, who agreed that it gave them a good understanding of several course ideas and concepts, as well as providing them with the required practical experience. The students also mentioned a few difficulties encountered while working on the projects, including student distraction from social media and the skills that educators and learners in higher education institutions are expected to have.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".