STEM training model for Venezuelan migrant children and youths

• Autores: Juan Sebastián Sánchez Gómez
• Directores de la Tesis: Maria Catalina Ramirez Cajiao (dir. tes.)
• Lectura: En la Universidad de los Andes (Colombia) ( Colombia ) en 2025
• Idioma: inglés
• Tribunal Calificador de la Tesis: Carola Hernández Hernández (presid.), Juan Gabriel Castañeda Polanco (secret.), Angel Alonso Gutiérrez Pérez (voc.)
• Enlaces
  • Tesis en acceso abierto en: hdl.handle.net
• Resumen

  • This dissertation proposes a DEI-informed, ICT-enhanced STEM education model for Venezuelan migrant children and youth in Colombia that both identifies what learners currently know, value, and believe about STEM and creates the conditions for those profiles to improve in ways that make STEM a plausible higher-education pathway. The model is built as an integrated, mixed-methods system with five interlocking layers. First, a context and equity layer addresses invisibilization and identity threats by explicitly valuing migrants’ funds of knowledge and lived experiences, positioning inclusion and belonging, not remediation, as the entry point to participation (Pain, 2004; Abrego, 2014; Claeys-Kulik et al., 2019; Dobscha, 2021; Zheng, 2022). Second, a teacher-capacity layer develops DEI-aligned practice through professional learning that emphasizes inclusive and active pedagogies, with teachers iteratively interpreting and enacting new routines in their own settings (Dewsbury et al., 2022; Castillo-Montoya et al., 2023; Kim et al., 2023; Zappe et al., 2022). Third, a learning-design layer specifies culturally relevant, project-based tasks mediated by ICT to widen access, personalize learning, and scaffold language and trauma-aware participation, operationalizing blended/b-learning affordances as equity tools rather than mere delivery modes (Galvis & Pedraza, 2013). Fourth, a learner-facing layer translates design principles into a hybrid Python course organized by “big ideas” and active methods so that students experience early mastery in decomposition, conditional reasoning, and iteration, dispositions central to computational thinking and long-term interest (Palmer, 2012; Galvis Panqueva, 2021). Fifth, a measurement-and-feedback layer uses a perceptions questionnaire anchored in public understanding of science indicators, knowledge, interest, and attitudes (KIA), to generate ethical, comparable baselines and monitor change, complemented by qualitative evidence from multiple case studies and focus groups (Miller, 1983, 2012; Durant, Evans, & Thomas, 1989; Durant, Bauer, & Gaskell, 1998; CONACYT, 2017; Minciencias y Observatorio de Ciencia y Tecnología, 2015; ANII, 2014; SENACYT, 2017; Stake, 1998; Yin, 2009). The overall architecture is underpinned by a PRISMA-based review that justifies construct selection and design choices and by a pragmatic mixed-methods stance that lets the research question dictate method rather than paradigm (Kitchenham, Mendes, & Travassos, 2007; Page et al., 2021).

    The model answers the research question, what STEM knowledge, interests, and attitudes migrant learners possess and require to become interested in STEM as a higher-education option, in two complementary moves. Identification is achieved through the KIA instrument and qualitative case evidence: knowledge items approximate understanding of core scientific constructs and inquiry processes; interest items capture information habits and engagement with STEM media and spaces; attitude items probe perceived benefits/risks, trust in institutions, and images of STEM professions (Miller, 1983, 2012; CONACYT, 2017; Minciencias y Observatorio de Ciencia y Tecnología, 2015; ANII, 2014; SENACYT, 2017). This yields an empirically grounded profile of what learners possess. Specification of requirements follows from the design and implementation layers: where instruction is culturally responsive, ICT-mediated, collaborative, and explicitly scaffolded for language and socio-emotional needs, learners encounter relevance, belonging, and authentic success early; these “mastery moments” predictably improve attitudes and interests and make visible which knowledge elements must be strengthened next (Dewsbury et al., 2022; Castillo-Montoya et al., 2023; Kim et al., 2023; Galvis & Pedraza, 2013; Palmer, 2012; Galvis Panqueva, 2021). The theory of change is therefore cyclical: inclusive design, early mastery and recognition, more positive perceptions (KIA), stronger computational-thinking practices, and higher willingness to pursue STEM studies; measurement closes the loop by indicating where to adjust tasks and scaffolds (Durant et al., 1989, 1998; Miller, 2012).

    Synthesis across the layers indicates that migrant students typically begin with uneven disciplinary knowledge, situational interests clustered around technology and tangible tasks, and ambivalent attitudes shaped by perceived difficulty and limited trust; under the model, these profiles shift when teachers enact DEI-aligned routines, and students experience structured, ICT-mediated problem solving tied to their contexts. In practical terms, the model produces three audience-specific recommendations that follow from the evidence. For STEM-education researchers, make visible and theorize migrants’ situated knowledge and products emerging from training, videos, programs, prototypes, and reflections, so that scholarship actively counters anti-migration narratives and documents learning assets, not only deficits (Pain, 2004; Abrego, 2014). For primary, secondary, and middle-school teachers, guarantee continuity in learners’ formal trajectories by designing inclusive classrooms that leverage informal experiences and funds of knowledge gathered along migration routes; enact active, collaborative, language-aware routines and use ICT to personalize access and practice (Dewsbury et al., 2022; Galvis & Pedraza, 2013). For policymakers, shape migration policies that recognize migration experiences and prioritize regularization for children and families; irregular status undermines well-being and discourages sustained participation in formal schooling, blunting the motivational gains that inclusive STEM instruction can create (Abrego, 2014). By aligning equity-centered design, teacher development, learner experience, and rigorous measurement, the model not only describes what migrant learners currently know, want, and believe about STEM but also specifies and tests the instructional and policy conditions they require to translate that profile into computational thinking, strengthened interest, and a credible route into STEM higher education.