Scholarlink

Background <p> Research has thoroughly demonstrated that the intention to pursue and persist in a Science-Technology-Engineering-Mathematics (STEM)-related career is influenced by the extent to which students perceive themselves as a STEM person, namely their STEM identity. However, previous studies have mainly used variable-centered approaches to explore relationships between identity and its precursors, which might hide nontrivial differences amongst subgroups of a sample. This study examines profiles of undergraduate students based on their STEM identity, as defined by a multi-dimensional framework which includes perception of the self as a STEM person, perceived competence, interest, sense of belonging and perceived recognition. This study also investigates the role of students’ gender, disciplinary area of the degree course, engagement in and intention to persist in the chosen undergraduate course in predicting profile membership. A convenience sample of 1114 STEM undergraduate students participated in this study (Mean age = 19.1 years; 50.9% female). An improved 6-factor scale was adopted to measure STEM identity. Profiles were extracted using a novel approach, Archetype Analysis, which allows to identify profiles that are more distinct and interpretable.</p> Results <p> Archetypal Analysis revealed five distinct identity profiles: A1: Focused, A2: Relational; A3: Obligated; A4: Aspiring; A5: Detached. Female students were less likely to be classified within Archetypes Focused and Obligated, which are characterised by high levels of STEM self-identification. Students enrolled in engineering programmes were more likely to be associated with Archetypes Relational, Obligated and Aspiring, as opposed to students enrolled in technology courses. Technology students tended to cluster within the low-identity archetype Detached. Engagement was a significant predictor of membership of Archetypes Relational and Obligated, which are both characterised by strong social relatedness and recognition. Finally, the intention to persist in the chosen degree programme was a significant predictor of membership of archetypes Focused and Obligated.</p> Conclusions <p> This study contributes to the growing body of research on STEM identity by employing a novel statistical analysis to study this construct from a person-centred perspective. This approach allowed us to identify individual differences in students’ patterns of interrelationship among the identity dimensions used.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring STEM identity profiles among undergraduate STEM students and their relationships with gender, discipline, persistence and engagement: a latent profile analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月8日

查看全文
  • Raffaella Passeggia,
  • Esposito Giovanna,
  • Lucio Palazzo,
  • Italo Testa

摘要

Background

Research has thoroughly demonstrated that the intention to pursue and persist in a Science-Technology-Engineering-Mathematics (STEM)-related career is influenced by the extent to which students perceive themselves as a STEM person, namely their STEM identity. However, previous studies have mainly used variable-centered approaches to explore relationships between identity and its precursors, which might hide nontrivial differences amongst subgroups of a sample. This study examines profiles of undergraduate students based on their STEM identity, as defined by a multi-dimensional framework which includes perception of the self as a STEM person, perceived competence, interest, sense of belonging and perceived recognition. This study also investigates the role of students’ gender, disciplinary area of the degree course, engagement in and intention to persist in the chosen undergraduate course in predicting profile membership. A convenience sample of 1114 STEM undergraduate students participated in this study (Mean age = 19.1 years; 50.9% female). An improved 6-factor scale was adopted to measure STEM identity. Profiles were extracted using a novel approach, Archetype Analysis, which allows to identify profiles that are more distinct and interpretable.

Results

Archetypal Analysis revealed five distinct identity profiles: A1: Focused, A2: Relational; A3: Obligated; A4: Aspiring; A5: Detached. Female students were less likely to be classified within Archetypes Focused and Obligated, which are characterised by high levels of STEM self-identification. Students enrolled in engineering programmes were more likely to be associated with Archetypes Relational, Obligated and Aspiring, as opposed to students enrolled in technology courses. Technology students tended to cluster within the low-identity archetype Detached. Engagement was a significant predictor of membership of Archetypes Relational and Obligated, which are both characterised by strong social relatedness and recognition. Finally, the intention to persist in the chosen degree programme was a significant predictor of membership of archetypes Focused and Obligated.

Conclusions

This study contributes to the growing body of research on STEM identity by employing a novel statistical analysis to study this construct from a person-centred perspective. This approach allowed us to identify individual differences in students’ patterns of interrelationship among the identity dimensions used.