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The Adoption of a Capstone Assessment Instrument

2012· article· en· W2047501728 on OpenAlexaff
Devlin Montfort, Shane Brown, Jerine Pegg

Bibliographic record

VenueJournal of Engineering Education · 2012
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Alberta
FundersNational Science Foundation
KeywordsCapstoneCitationLibrary scienceState (computer science)World Wide WebComputer scienceComputer securityProgramming language

Abstract

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Previous research has shown that the adoption of effective innovations in engineering education typically lags behind awareness of those innovations, and this delay may be limiting the positive impacts of advances in engineering education. PURPOSEThis study investigates early adoption processes in detail in the context of an assessment instrument designed for use in capstone design courses.Research at this level of detail is needed to better understand adoption processes in general and to encourage spread of innovation in engineering education practice. DESIGN/METHODSemistructured clinical interviews were conducted with the developers and users of the assessment instrument and the educators who were introduced to it in a workshop.These interviews were analyzed thematically with respect to the diffusion of innovations theory. RESULTSThe qualitative, in-depth nature of the analysis revealed surprising diversity in the participants' perceptions of the assessment instrument.The most important features affecting their adoption were the participants' perceptions of its compatibility with their own and their institutions' values and goals.The complex and varied ways in which the participants were involved with capstone courses at their university were important in understanding their adoption decisions. CONCLUSIONThe findings are analyzed in terms of the context in which they arose, and their transferability to other contexts is discussed.The interactions between participants' perceptions and the specific context of their university's capstone program affected their adoption decisions, but these decisions are not easily characterized by existing theories or addressed by typical dissemination efforts. KEYWORDS adoption, diffusion of innovations Montfort, Brown, & Peggbut not one that has often been addressed with the same rigor as applied to the design and development of innovations.This study contributes to the understanding of adoption processes by using an established theoretical framework to explore the early adoption of a specific innovation, rather than broadly characterizing the generalized process.Those broad characterizations are important and valuable, and indeed the approach used in this research builds from them, but the move to particulars provides information on the details, interactions, exceptions, and examples of individuals' enactment of that generalized process.Investigation and interpretation of these specifics allow for adjustments to our ways of thinking about adoption, which has the potential to improve future research and encouragement of adoption. BACKGROUND The InnovationThe innovation in this study is the Team Citizenship Assessment Instrument (TCAI), one part of a suite of assessment instruments currently being developed for widespread use in capstone design courses.The various evaluation tools were developed to help assess and achieve important educational outcomes such as teamwork, communication, and design skills (particularly those required by the ABET Accreditation Commission, 2010).These assessment tools were developed by a group of diverse and experienced engineering faculty at various institutions (Davis, Trevisan, Beyerlein, Harrison, & Thompson, 2007; Engineering Education Research Center, 2009).The TCAI consists of a questionnaire administered to each student in a capstone design team.Team members rate each other on their contributions to the team, communication skills, and team effectiveness.The TCAI was designed to be flexible and to fit within as broad a range of course designs as possible.For example, the TCAI could be implemented verbally by a faculty advisor with one small team in an effort to promote more productive attitudes toward teamwork, or could be used in written form by a course instructor to evaluate the quality of students' feedback at the beginning and end of the course.Although the TCAI was designed primarily to serve as one of a suite of assessments intended to cover the full range and content of capstone courses, it can also be used as a stand-alone instrument.At the time of this study, the assessment developers were in the process of piloting the TCAI, a group of colleagues of the developers were partially implementing the assessments, and a different group of faculty attended a workshop on the assessments.Research at this stage of development provided a uniquely rich opportunity to investigate early adoption processes of three different groups of potential adopters.Early adoption is particularly important because these processes determine whether an innovation will ever spread to a broader population (Rogers, 2003).Additionally, the theoretical framework guiding this research emphasizes the importance of potential adopters' perceptions of an innovation, and the timing of this research allowed for the investigation of those perceptions as they were developing. Theoretical FrameworkDiffusion of innovations (DI) theory describes the process of how new ideas, products, or practices become commonplace in a population.DI theory was chosen for this study because it best represents a holistic approach to adoption processes that includes the innovation itself, the individuals adopting it, and the contexts within which adoption would 21689830, 2012, 4,

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 imitation

Not 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.

metaresearch head score (Codex)0.060
metaresearch head score (Gemma)0.153
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.153
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.242
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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Citations19
Published2012
Admission routes1
Has abstractyes

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