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Record W2551458562 · doi:10.18260/1-2--22804

Living With Contradiction: Cultural Historical Activity Theory as a Theoretical Frame to Study Student Engineering Project Teams

2020· article· en· W2551458562 on OpenAlexaff
Michael L. W. Jones

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsActivity theoryContradictionSociologyAgency (philosophy)Constructivism (international relations)Social constructivismPedagogySociocultural evolutionEngineering educationKnowledge managementEngineeringEngineering ethicsComputer sciencePoliticsEpistemologyPolitical scienceEngineering managementSocial science

Abstract

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Abstract Living With Contradiction: Cultural Historical Activity Theory as a Theoretical Frame to Study Student Engineering Project Teams Problem-­‐based learning supports collaborative constructivist learning by encouraging students to engage in independent investigation of specific problems (Savery, 2006). Engineering disciplines tend to engage project-­‐based learning opportunities that are characterized by longer engagement with more complex projects than traditional PBL(Bédard, Lison, Dalle, Côté, & Boutin, 2012), such as capstone projects or competitive project teams. Such extended, complex projects provide students with multiple opportunities to develop both technical knowledge and professional judgment – and multiple challenges that might jeopardize a project group’s success. This paper outlines cultural-­‐historical activity theory (CHAT) as a theoretical lens to understand the challenges engineering PBL student groups face. Building from Vygotsky’s social constructivism, CHAT squarely situates human agency in sociocultural forces that shape and constrain the nature and execution of that activity. (Engestrom, 1987). By balancing human agency with rules, community and power concerns, contemporary CHAT highlights the situated nature of technical work and highlights multiple points of contradiction that must be negotiated and considered (Engestrom, 1999). Perhaps particularly disquieting for engineers, these contradictions are often highly social and political in nature and resist simple analysis or resolution. This paper grounds CHAT analysis in continuing research into the management practices of Formula SAE (FSAE) student engineering teams. The core activity of a FSAE team is to design, manufacture, test and race a small racecar in intercollegiate competition. Towards this goal, FSAE teams must learn to negotiate a range of organizational contradictions. FSAE teams must recruit and retain team members in a high-­‐turnover environment. They must learn to operate within the constraints established by the competition, school administration and societal laws and mores. Teams may also strategically choose to share some information and collaborate with competitive teams for mutual gain. CHAT as a theoretical frame highlights these and other points of potential contradiction and allows for sharing of experiences to help determine best practices in a variety of team contexts. References Bédard, D., Lison, C., Dalle, D., Côté, D., & Boutin, N. (2012). Problem-­‐based and Project-­‐ based Learning in Engineering and Medicine: Determinants of Students’ Engagement and Persistence. Interdisciplinary Journal of Problem-­‐Based Learning, 6(2). doi:10.7771/1541-­‐5015.1355 Engestrom, Y. (1987). Learning by Expanding: An activity-­‐theoretical approach to developmental research. Helsinki: Orienta-­‐Konsultit. Engestrom, Y. (1999). Activity Theory and Individual and Social Transformation. In Y. Engestrom, R. Miettenien, & R.-­‐L. Punamaki (Eds.), Perspectives on Activity Theory. Cambridge: Cambridge University Press. Savery, J. R. (2006). Overview of Problem-­‐based Learning: Definitions and Distinctions. Interdisciplinary Journal of Problem-­‐Based Learning, 1(1), 9–20.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.045
GPT teacher head0.392
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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