Reflection on Integrative Project-Based Learning in Business and Information Technology Programs
Bibliographic record
Abstract
Recently there has been an increase in demand for interdisciplinary programs that enable graduates to demonstrate a blend of technical and ‘soft skills’. As a result, many higher education organizations are developing programs that integrate areas such as management and information technology or entrepreneurship and engineering. The wide range of topics covered in these programs and the need for graduate to be able to integrate and apply of core concepts. Since 2010 we have used integrative project-based learning as a core element of our game development and entrepreneurship program. In this model, students work in project teams to create a “complete” video game following a set of specific feature requirements drawn from the students’ courses. This project requires students to integrate concepts across all courses taken (including those from business, game design, programming, and game art) and develop a commercially viable game. More recently, we have developed project-based learning elements for our networking and information technology security program. In this paper, we reflect on the success and challenges of implementing integrative project-based learning throughout a university program. Elements considered include scalability, management of student groups, faculty engagement, program scheduling, and effectiveness of content integration. Results have demonstrated that students are better able to understand how fundamental concepts from the various curriculum areas interact while gaining additional opportunities to practice ‘soft skills’ such as project management, communications, problem solving, and leadership. The paper will provide recommendations on the necessary learning environment and supports for successful implementation of integrative project-based learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".