From Serious Leisure to Knowing Organizations: Information and Knowledge Management Challenges in Project-Based Learning Student Engineering Teams
Bibliographic record
Abstract
Abstract From Serious Leisure to Knowing Organizations: Organizational and Knowledge Management Challenges in Student Engineering Project Teams Michael L.W. Jones PhD Candidate, Faculty of Information University of Toronto mlw.jones@mail.utoronto.ca Critiques of contemporary engineering education have highlighted issues of limited applied and “soft” skills development (Fairweather, 2008), arguably leading to retention issues in STEM education (Seymour & Hewitt, 1997) and a mismatch with industry demands for graduates (ABET, 2011). Facing similar challenges, medical and nursing schools have leveraged problem-‐based learning (PBL) strategies where students engage medical cases collaboratively and independently, with faculty serving as facilitators of a student-‐led learning process (Savery, 2006). Engineering educators have adapted similar PBL approaches such as capstone design projects and engineering student design teams to complement the more traditional, basic-‐science based engineering curriculum. Such learning opportunities are qualitatively different than traditional PBL efforts in one demonstrable way. Engineering project teams tend to engage more complex design challenges over a longer period of time compared to the more singularly focused, ad-‐hoc PBL groups used in class-‐based investigations. This qualitative difference creates two organizational challenges unique to engineering project-‐based learning teams. Student engineering design teams must sustain team motivation throughout a long and often arduous process of design and development. Team leaders in particular adopt responsibilities and commitments often equal to or more than a traditional full-‐time job. Much of this effort is not explicitly rewarded – indeed, it often cuts into time and effort normally spent on other curricular and social activities. For many participants, project teams are serious leisure opportunities (Stebbins, 2007), shaped by intense intrinsic motivation, considerable personal sacrifice, strong self-‐identification with the task at hand, and considerable investment of energy and resources. The extended time period and complexity of investigation in engineering student design teams also requires that engineering teams develop into effective and sustained knowledge-‐based organizations. In doing so, student members and leaders engage organizational knowledge management challenges rarely formally taught in the traditional engineering curriculum, including effective sense-‐making, knowledge creation and dissemination processes, and effective and timely decision making (Choo, 2006). This paper describes a larger effort to build a model of these organizational and knowledge management challenges. This effort leverages cultural-‐historical activity theory (CHAT), a complex, dialectical analytical model of situated collaborative action that highlights the contradictions student members and leaders face in engaging a team’s end objectives and goals. While these contradictions and challenges are arguably applicable to all engineering student project teams, specific attention will be paid to Formula SAE (FSAE), a design competition series with a long history and worldwide reach, now including nearly 500 collegiate teams in over 20 countries worldwide. Preliminary research from the author’s participatory observation with one team is discussed in this paper, and is presented as a foundation for a sustained discussion of challenges faced within the larger FSAE community. References ABET. (2011). ABET -‐ Criteria for Accrediting Applied Science Programs, 2012 -‐ 2013. abet.org. Retrieved December 2011, from http://abet.org/asac-‐criteria-‐ 2012-‐2013/ Choo, C. W. (2006). The Knowing Organization: How Organizations Use Information to Construct Meaning, Create Knowledge and Make Decisions. New York: Oxford University Press. Fairweather, J. (2008). Linking Evidence and Promising Practices in Science, Technology, Engineering and Mathematics Undergraduate Education (pp. 1–31). National Academies of Science. Savery, J. R. (2006). Overview of Problem-‐based Learning: Definitions and Distinctions. Interdisciplinary Journal of Problem-‐Based Learning, 1(1), 9–20. Seymour, E., & Hewitt, N. M. (1997). Talking about leaving: Why undergraduates leave the sciences. Boulder, CO: Westview Press. Stebbins, R. A. (2007). Serious leisure: A perspective for our time. Transaction Pub.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".