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Record W4401312747 · doi:10.18260/1-2--48391

Board 90: Work in Progress: Response to Failure and Success in an ECE Course

2024· article· en· W4401312747 on OpenAlexaff
Jennifer G. Cromley, Jessica R. Gladstone, Juan Alvarez, Zhengyan Ye, Parth Shastri, Aizhan Daukenova, Shiyu Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsYork UniversityPediatric Oncology Group
Fundersnot available
KeywordsCourse (navigation)Work (physics)Computer scienceReliability engineeringEngineeringMechanical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

Abstract In engineering as a profession and in engineering education, failure is commonplace--attempted designs fail, experiments fail about 90% of the time, and students do not achieve the scores they desire on homework, quizzes, and exams. Thus, the ability to navigate and respond to failure as an opportunity for growth and learning is a key component of the scientific enterprise. However, engineering education research is sparse on how students respond to failure. Research on response to failure has been extensive in workplace settings, in which there is a very wide range of negative and positive responses to failure, including denial, anger, bargaining, depression, acceptance, working harder, working smarter, shock, fear, renewed dedication, increased susceptibility to stereotypes, blame, shame, despair, changes in interest, reassessing career, reassessing self-perceived ability, and learning from failure. Thus, these responses may include combinations of emotional, cognitive, and behavioral responses. Research on responses to success likewise suggests positive emotions in response to success on work projects, such as pride, satisfaction, and happiness. Educational research on response to failure across different domains and ages--not just in engineering education--has been quite narrow because research has used theories that make very narrow predictions about response to failure, like self-belief theory[1], achievement goal orientation[2], expectancy-value theory[3], interest theory[4], etc. Educational research on response to success has been sparse, but also suggests positive emotions in response to success. Our long-term goal is to find ways to help build resilience to failure and ways in which we can help students learn from failure (e.g., viewing failure as an opportunity), and hence it is critical to understand the range of responses to failure. Furthermore, to understand what response to failure looks like, we need to contrast it with response to success. Additionally, it is important to consider not only the standards of the course or the specific task (i.e., what counts as a failing score) but also the standards of the students. For instance, the same grade may be interpreted differently by two different students, one may see it as a success and the other may see it as a failure. Thus, it is critical to interview students to develop a better understanding of their response to their standard of failure or success. For this work-in-progress, we interviewed 26 students in a second year ECE course in a large public university, which is required for EE or CE majors, so we expect that students will see the course as important, and simultaneously put a great deal of grade pressure on themselves, making this an excellent test bed for measuring responses to failure and success in Engineering. Interviews focused on a) perceived success vs. perceived failure on the first exam, b) emotional responses to the first exam grade received, c) perceived reasons for that success or failure on the first exam, d) any additional study strategies used to study for the first exam, and e) plans--if any--to change study strategies for the second exam. We have completed the first-exam interviews and a motivation questionnaire, and applied a draft coding scheme derived from the interview statements to about one-half of the transcripts. Results so far are consistent with broader findings regarding emotions in formal learning--those who did better than expected verbalized a wide range of (almost all) positive emotions and those who did worse than expected verbalized a wide range of (almost all) negative emotions. Follow-up interviews after the second exam of the semester have begun, and will be transcribed, coded, and analyzed using the same coding scheme with codes added as warranted by additional student statements in the interviews. A Likert-type questionnaire will be written based on the coded interview responses. In addition, interview responses will be analyzed based on scores on various motivation scales (e.g., students valuing the content more may have more-negative responses to perceived failure versus those who value the content less). References [1] Dweck, C. S. (2000). Self-theories: Their role in motivation, personality, and development. Psychology Press. [2] Elliot, A. J., & McGregor, H. A. (2001). A 2× 2 achievement goal framework. Journal of Personality and Social Psychology, 80(3), 501-519. [3] Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81. [4] Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111-127.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.390
Teacher spread0.369 · 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.

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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Citations0
Published2024
Admission routes1
Has abstractyes

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