Multi-Institutional Study on Impostor Phenomenon
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| Title: | Multi-Institutional Study on Impostor Phenomenon |
|---|---|
| Language: | English |
| Authors: | Sophia Krause-Levy (ORCID |
| Source: | ACM Transactions on Computing Education. 2025 25(4). |
| Availability: | Association for Computing Machinery. 1601 Broadway 10th Floor, New York, NY 10119. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/ |
| Peer Reviewed: | Y |
| Page Count: | 29 |
| Publication Date: | 2025 |
| Sponsoring Agency: | National Science Foundation (NSF), Division of Graduate Education (DGE) National Science Foundation (NSF) |
| Contract Number: | 1650112 2121592 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Computer Science Education, Higher Education, College Students, Self Concept, Beliefs, Student Characteristics, Institutional Characteristics, Demography, Educational Background, Classes (Groups of Students), Public Colleges, Private Colleges |
| DOI: | 10.1145/3748665 |
| ISSN: | 1946-6226 |
| Abstract: | 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. 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. This is concerning due to the negative effects correlated with people who experience IP such as low self-esteem and anxiety. 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. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1488842 |
| Database: | ERIC |
| FullText | Text: Availability: 0 |
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| Header | DbId: eric DbLabel: ERIC An: EJ1488842 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-Institutional Study on Impostor Phenomenon – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sophia+Krause-Levy%22">Sophia Krause-Levy</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6810-8590">0000-0002-6810-8590</externalLink>)<br /><searchLink fieldCode="AR" term="%22Andrew+Petersen%22">Andrew Petersen</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1337-7985">0000-0003-1337-7985</externalLink>)<br /><searchLink fieldCode="AR" term="%22Oladele+Oladunjoye+Campbell%22">Oladele Oladunjoye Campbell</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4922-6791">0000-0003-4922-6791</externalLink>)<br /><searchLink fieldCode="AR" term="%22William+G%2E+Griswold%22">William G. Griswold</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0663-6977">0000-0003-0663-6977</externalLink>)<br /><searchLink fieldCode="AR" term="%22Leo+Porter%22">Leo Porter</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1435-8401">0000-0003-1435-8401</externalLink>)<br /><searchLink fieldCode="AR" term="%22Oluwatoyin+Adelakun-Adeyemo%22">Oluwatoyin Adelakun-Adeyemo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1817-0027">0000-0003-1817-0027</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jennifer+Campbell%22">Jennifer Campbell</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5092-6600">0000-0001-5092-6600</externalLink>)<br /><searchLink fieldCode="AR" term="%22Michelle+Craig%22">Michelle Craig</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8283-0072">0000-0001-8283-0072</externalLink>)<br /><searchLink fieldCode="AR" term="%22Adrienne+Decker%22">Adrienne Decker</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0822-4813">0000-0002-0822-4813</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sebastian+Dziallas%22">Sebastian Dziallas</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0001-0647-5635">0009-0001-0647-5635</externalLink>)<br /><searchLink fieldCode="AR" term="%22Carrie+Demmans+Epp%22">Carrie Demmans Epp</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9079-4921">0000-0001-9079-4921</externalLink>)<br /><searchLink fieldCode="AR" term="%22David+R%2E+Gibson%22">David R. Gibson</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0009-7011-5210">0009-0009-7011-5210</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yekaterina+Kharitonova%22">Yekaterina Kharitonova</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0001-2567-0145">0009-0001-2567-0145</externalLink>)<br /><searchLink fieldCode="AR" term="%22Devorah+Kletenik%22">Devorah Kletenik</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4362-3884">0000-0003-4362-3884</externalLink>)<br /><searchLink fieldCode="AR" term="%22David+L%2E+Largent%22">David L. Largent</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1019-1763">0000-0003-1019-1763</externalLink>)<br /><searchLink fieldCode="AR" term="%22Emma+McDonald%22">Emma McDonald</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0009-4615-3954">0009-0009-4615-3954</externalLink>)<br /><searchLink fieldCode="AR" term="%22Brian+M%2E+McSkimming%22">Brian M. McSkimming</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9363-4974">0000-0001-9363-4974</externalLink>)<br /><searchLink fieldCode="AR" term="%22Tina+L%2E+Peterson%22">Tina L. Peterson</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9624-2861">0000-0001-9624-2861</externalLink>)<br /><searchLink fieldCode="AR" term="%22Caroline+Sih%22">Caroline Sih</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3867-621X">0000-0003-3867-621X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Cynthia+Taylor%22">Cynthia Taylor</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2219-9626">0000-0002-2219-9626</externalLink>)<br /><searchLink fieldCode="AR" term="%22Neena+Thota%22">Neena Thota</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3795-6060">0000-0002-3795-6060</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22ACM+Transactions+on+Computing+Education%22"><i>ACM Transactions on Computing Education</i></searchLink>. 2025 25(4). – Name: Avail Label: Availability Group: Avail Data: Association for Computing Machinery. 1601 Broadway 10th Floor, New York, NY 10119. 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In 2020, IP in computing began to be explored at single institutions. 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. This is concerning due to the negative effects correlated with people who experience IP such as low self-esteem and anxiety. 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. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2025 – Name: AN Label: Accession Number Group: ID Data: EJ1488842 |
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