{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T02:01:27Z","timestamp":1776391287796,"version":"3.51.2"},"reference-count":79,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T00:00:00Z","timestamp":1773532800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T00:00:00Z","timestamp":1773532800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computer Assisted Learning"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Previous Epistemic Network Analysis (ENA) studies examined CT under various instructional strategies, performance levels and scaffolding. However, limited work addresses how gender, academic performance and programming ability shape CT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aims to investigate how gender, academic performance and programming ability influence students' computational thinking using ENA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study employed retrospective think\u2010aloud protocols to capture students' CT (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009486), encoded them into quantitative data, and applied ENA for analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results and Conclusions<\/jats:title>\n                    <jats:p>Whether the differences lie in gender, academic performance, or programming ability, disparities in CT network structures were consistently reflected in the connection strength of the \u2018concepts\u2013practices\u2013perspectives\u2019 triad and in the fluency of transfer, explanation, and reflection processes. The findings revealed that male and female students' epistemic networks were complementary, characterised respectively by interactive\u2010constructive and self\u2010constructive orientations. For academic performance and programming ability, high\u2010level groups exhibited more complex, richly connected, and well\u2010balanced networks. These results highlight the need for differentiated instruction tailored to distinct groups, encouraging heterogeneous pairing to foster the complementarity of group advantages. This study contributes to the ongoing discussion of CT and provides actionable insights for the design of personalised CT instruction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcal.70228","type":"journal-article","created":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T23:35:42Z","timestamp":1773617742000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Impact of Gender, Academic Performance, and Programming Ability on Computational Thinking: Epistemic Network Analysis Based on Programming Tests"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6491-0906","authenticated-orcid":false,"given":"Yimin","family":"Ning","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences East China Normal University  Shanghai 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