From Serious Leisure to Knowing Organizations: Information and Knowledge Management Challenges in Project-Based Learning Student Engineering Teams
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,014 | 0,007 |
| Communication savante | 0,019 | 0,008 |
| Science ouverte | 0,003 | 0,016 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
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