Multi-Institutional Study on Impostor Phenomenon
Notice bibliographique
Résumé
Motivation : In computing, Impostor Phenomenon (IP) has been viewed as a problem for many years, but little research has been done to show its prevalence. In 2020, IP in computing began to be explored at single institutions [ 68 ]. The results showed that IP is prevalent among undergraduate and graduate students in computing courses and that the rates of IP are higher for women. In 2022, these results were reaffirmed with a replication study including two institutions [ 82 ]. This is concerning due to the negative effects correlated with people who experience IP such as low self-esteem [ 19 , 37 ] and anxiety [ 21 , 38 ]. Objectives : This study aims to replicate these previous findings at a considerably larger scale to determine whether similar results are observed across institutions. To support future work, we conduct an exploratory analysis of student demographics, course factors, and institutional factors to gain insight into factors that may be associated with higher levels of IP among students. Methods : A survey consisting of Clance’s IP scale (CIPS) and questions on students’ demographic and background information was given at 18 institutions. Higher CIPS scores indicate more IP experiences. Differences in CIPS scores were analyzed based on students’ demographics and background information (gender, race/ethnicity, transfer status, and chosen degree program), course factors (introductory computing courses vs. non-introductory computing courses, upper- vs. lower-division), and institutional factors (size of the institution, public vs. private, teaching- vs. research-centric). Results : Our results are consistent with previous findings that IP is prevalent among students in computing courses and that women have significantly higher CIPS scores of IP than men in computing, and that traditionally marginalized race/ethnicity status in computing and chosen degree program do not have an observable impact. In terms of course factors, we do not see a difference in scores based on whether students are enrolled in a lower- or upper-division course. We see that students enrolled in introductory computing (CS1) courses have statistically significant higher scores than students outside of CS1 courses. In terms of institutional factors, students in computing courses at public institutions have statistically significantly higher scores than students at private institutions. Students at medium-sized institutions have statistically significantly higher scores than students at small or large institutions. We do not find any difference based on whether an institution is teaching- or research-centric. Discussion : These results suggest that IP is prevalent in computing courses across the entire curriculum and across different types of institutions. Differences in demographic groups are consistent with prior work in computing, specifically higher rates among women, suggesting IP may be worth further inquiry as a potential factor in the gender participation gap in computing.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».