Board 90: Work in Progress: Response to Failure and Success in an ECE Course
Notice bibliographique
Résumé
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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».