The Impact of Gender, Academic Performance, and Programming Ability on Computational Thinking: Epistemic Network Analysis Based on Programming Tests.
Background: 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. Objectives: This study aims to investigate how gend...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 2; pp. 1 - 20 |
|---|---|
| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=192476934&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192476934 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Apr2026 vid: 42 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 192476934 192476934 192476934 10.1002/jcal.70228 192476934 ppf: 1 ppct: 19 formats: tig: atl: The Impact of Gender, Academic Performance, and Programming Ability on Computational Thinking: Epistemic Network Analysis Based on Programming Tests. aug: au: Ning, Yimin Jin, Zhijie Wu, Hongde affil: School of Mathematical Sciences, East China Normal University, Shanghai, China sug: subj: Computer Literacy Thinking Problem Solving Programming Languages Evaluation Academic Performance Evaluation Sex Factors Computer-Assisted Instruction Human Funding Source Male Female Child Students, Middle School Systems Analysis Retrospective Design Record Review World Wide Web Kruskal-Wallis Test Chi Square Test Descriptive Statistics Child: 6-12 years Male Female ab: Background: 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. Objectives: This study aims to investigate how gender, academic performance and programming ability influence students' computational thinking using ENA. Methods: This study employed retrospective think‐aloud protocols to capture students' CT (N = 486), encoded them into quantitative data, and applied ENA for analysis. Results and Conclusions: 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 'concepts–practices–perspectives' 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‐constructive and self‐constructive orientations. For academic performance and programming ability, high‐level groups exhibited more complex, richly connected, and well‐balanced 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. Lay Summary: What is currently known about this topic? ○A review of the existing literature shows that gender, academic performance and programming ability are the most significant predictors of computational thinking (CT).○However, previous research has explored the influence of these factors on students' CT, but lacks in‐depth Epistemic network analysis on how they specifically impact different CT dimensions.What does this paper add? ○Gender differences appear less in whether CT elements are present and more in the strength, sequencing, and directionality of relational pathways among the concepts–practices–perspectives triad. Male and female students show complementary network strengths rather than a simple advantage/disadvantage pattern.○Differences by academic performance are primarily reflected in the depth of integration between mathematical modelling and logical reasoning within CT networks. High‐performing students exhibit denser and stronger three‐dimensional connectivity, whereas low‐performing students rely more on intuitive execution with comparatively weaker modelling/logic integration.○Programming ability functions as a carrier of CT practices and more directly shapes CT network structure by influencing the efficiency of transfer from concepts to practices, thereby strengthening or weakening overall network density. Low programming ability is associated with fragile cross‐dimensional coupling, while high programming ability supports more robust integration across concepts, practices, and perspectives.Implications for practice/or policy ○For males, prioritise conceptual abstraction using analogies/visuals (e.g., concept maps) and simple step templates. For females, prioritise supported externalisation through low‐stakes talk (e.g., pair programming, structured brainstorming) in a psychologically safe climate.○For lower‐performing students, make reasoning visible with flowcharts/mind maps and require brief step‐by‐step justifications before coding. For higher‐performing students, provide higher‐order challenges (advanced tasks, reverse‐reasoning) and use mentoring/leader roles.○For lower programming ability, use scaffolded code (partial code with annotations) and reordering/integration tasks instead of full independent programs. For higher programming ability, assign complex contextual tasks and peer tutoring/leadership to extend strategy sharing and creativity. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|