The role of project‐based learning in IT
Bibliographic record
Abstract
Purpose Problem/project‐based‐learning (PBL) approaches have traditionally been shown to be effective for learning within many professional programs that are directly related to the students' future career. The PBL approach has been adopted for over four decades in such fields as medicine and engineering and studies have demonstrated that students working in a PBL context have improved their skill levels over students enrolled in traditional lecture‐based classes. In spite of these successes, PBL has not yet been often considered in computer science/IT‐related academic programs. This is due to a variety of factors, including lack of support from faculty, historical approaches precluding such innovation, and lack of motivation to innovate beyond the dominant and more traditionally known approaches. This paper presents a case study that outlines a particular approach that was adopted to attempt to overcome these limitations in order to introduce PBL to IT‐related studies. The context of this study is a particular program that involves students in industry‐relevant practices while learning the necessary theory and honing their skills. It aims to discuss how PBL has been integrated into the IT Game Development and Entrepreneurship program at the University of Ontario Institute of Technology, a laptop‐based institution. It also aims to provide qualitative results that would support the effective applicability of this PBL approach. Design/methodology/approach A novel method for learning technical game development (theory and practice) is illustrated using a PBL pedagogical approach. The primary objective is to maintain academic integrity, improve critical thinking and problem‐solving skills, and introduce students to the theory of designing and developing video games. Findings Preliminary results indicate that the proposed PBL method has improved students' skills and expanded their knowledge both theoretically and practically within their area of study. With the integration of this approach into the curriculum, the authors have seen a higher retention rate, increased motivation, and the development of higher quality work from students. Originality/value This paper provides a discussion on the role of PBL in IT settings with practical and positive implications on student learning, involvement, and retention. The approach is innovative in higher education and provides a framework that can be easily adapted to other fields of study.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".