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"We are too dumb for this": a critical discourse analysis of stereotypical attributions to struggle with computational thinking.

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Title: "We are too dumb for this": a critical discourse analysis of stereotypical attributions to struggle with computational thinking.
Authors: Vasconcelos, Lucas1 (AUTHOR) limadel@mailbox.sc.edu
Source: Educational Technology Research & Development. Dec2025, Vol. 73 Issue 6, p3675-3698. 24p.
Subject Terms: *Computer science education, *Digital literacy, *Student teachers, *Common misconceptions, *Attribution (Social psychology), Critical discourse analysis, Intersectionality
Abstract: This qualitative single case study examined to what extent two White female preservice teachers' attributions to struggle during computational thinking tasks rely on stereotypical beliefs. Video recordings of participants' discourse during computational thinking and transcribed one-on-one interviews served as data sources. Attribution theory served as the framework to guide this study. Critical discourse analysis was adopted with two overarching goals: first, to examine participants' attributions based on the three dimensions of locus, controllability, and stability; and second, to examine the underlying assumptions in their attributions and assess whether they reinforce or oppose the dominant system of stereotypical beliefs about who can succeed in computer science. An intersectional approach was adopted to discuss the findings about stereotypical attributions as they pertain to participants' age, gender, race, and socioeconomic status. Results showed prevalence of negative stereotypical attributions to causes of struggle that were dispositional, uncontrollable, and permanent. Fewer non-stereotypical attributions were identified, and those focused on situational, controllable, and temporary causes of struggle. Further, negative dispositional attributions persisted even after successful task completion. Results also pointed out that participants' dispositional attributions often reinforce a system of stereotypical beliefs that has persistently excluded minoritized populations from computer science. Implications for computer science education are presented. [ABSTRACT FROM AUTHOR]
Copyright of Educational Technology Research & Development is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: This qualitative single case study examined to what extent two White female preservice teachers' attributions to struggle during computational thinking tasks rely on stereotypical beliefs. Video recordings of participants' discourse during computational thinking and transcribed one-on-one interviews served as data sources. Attribution theory served as the framework to guide this study. Critical discourse analysis was adopted with two overarching goals: first, to examine participants' attributions based on the three dimensions of locus, controllability, and stability; and second, to examine the underlying assumptions in their attributions and assess whether they reinforce or oppose the dominant system of stereotypical beliefs about who can succeed in computer science. An intersectional approach was adopted to discuss the findings about stereotypical attributions as they pertain to participants' age, gender, race, and socioeconomic status. Results showed prevalence of negative stereotypical attributions to causes of struggle that were dispositional, uncontrollable, and permanent. Fewer non-stereotypical attributions were identified, and those focused on situational, controllable, and temporary causes of struggle. Further, negative dispositional attributions persisted even after successful task completion. Results also pointed out that participants' dispositional attributions often reinforce a system of stereotypical beliefs that has persistently excluded minoritized populations from computer science. Implications for computer science education are presented. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Educational Technology Research & Development is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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