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99 ERC SES 07 J: Teaching STEM
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99. Emerging Researchers' Group (for presentation at Emerging Researchers' Conference)
Paper Prospective Science Teachers’ Systems Thinking: Interpreting Ecosystem Dynamics and Human Impacts Middle East Technical University, Turkey (Türkiye) Presenting Author:Ecosystems are complex systems composed of hierarchically organized subsystems embedded within larger networks of interactions (Jin et al., 2019; Schizas, Papatheodorou, & Stamou, 2019). This complexity not only leads students to demonstrate fragmented and compartmentalized knowledge (Eilam, 2011) but also creates difficulties in developing a systemic understanding of ecosystems and causal relationships (Grotzer & Basca, 2003). For example, students often struggle to explain concepts that require a higher level of systems thinking beyond simple cause-and-effect relationships and involve understanding the indirect and dynamic relationships within ecosystems, such as carrying capacity, exponential growth, and feedback loops (Jin et al., 2019). While indirect relationships are central to understanding complex ecological systems, they are usually not directly observable; therefore, students tend to focus on simple linear direct relationships (Mambrey et al., 2020). Predator–prey interactions, for instance, are usually interpreted in terms of direct population changes; that is, a decrease in prey population leads to a decrease in predator populations but misses the indirect relationships. Accordingly, understanding ecosystems requires a systems-thinking approach that holistically addresses nonlinear relationships, feedback loops, and interactions among system components (Grotzer & Basca, 2003). Systems thinking encompasses learners’ ability to understand biological systems by recognizing their key characteristics and applying discipline-specific concepts to explain interdependencies within ecosystems, including the influences of human activities (Gilissen, Knippels, & van Joolingen, 2020; Jin et al., 2019). In the context of this study, systems thinking is defined as learners’ ability to use discipline-specific concepts to describe interdependency in ecosystems, including the influence of human activity (Jin et al. 2019). Although complex systems operate according to their principles and cannot be entirely managed by humans, human activities can influence and, in some cases, disturb natural systems (Riess & Mischo, 2010). Building on this perspective, the purpose of science education has been redefined as preparing accountable, conscious individuals who can reason about complex systems. In fact, the Next Generation Science Standards (NGSS, 2013; NRC, 2013) address the concept of “Systems and System Models” as a cross-cutting concept and ‘Ecosystems: Interactions, Energy, and Dynamics’ as a Disciplinary Core Ideas in life sciences. The Next Generation Science Standards (2013) also note that human activities have significantly impacted the biosphere, in some cases causing habitat loss and species extinction. Similarly, the recently revised Turkish Science Education Curriculum emphasizes the importance of complex systems, systems thinking, and systems literacy (MoNE, 2024, p. 55). To this end, educating future citizens to engage with complex global environmental challenges shaped by human-environment interactions requires teachers to possess high-level systems thinking skills. Accordingly, this study aims to clarify future science teachers’ understanding of ecosystem dynamics and the influence of humans on those dynamics through the lens of systems thinking. Methodology, Methods, Research Instruments or Sources Used In this study, a qualitative research method was utilized. Qualitative research is an approach that focuses on uncovering the meanings individuals attribute to their experiences and uses words as the primary data source (Merriam & Tisdell, 2016). As a part of qualitative research, a case study research design was adopted. Yin (2009) suggests that a case study enables an in-depth examination of a phenomenon through multiple data sources, including interviews, documents, and observations, particularly in contexts where the researcher does not intervene in participants’ actions. To gain a comprehensive understanding of prospective science teachers’ levels of systems thinking in ecosystems and their understanding of human influence, a single embedded case study design was employed following Yin (2018). It is especially suitable for investigating a single case to be investigated through multiple embedded units of analysis. (Yin, 2018), and in this study, the case consisted of prospective science teachers, with individual participants serving as the embedded subunits. Purposeful sampling was used to ensure that participants were appropriate for the study’s purpose (Fraenkel & Wallen, 2009). The study involved five volunteer senior prospective science teachers enrolled at a public university. Data were collected through interviews by using the Systems Thinking Assessment questionnaire developed by Jin et al. (2019). The questionnaire consists of 12 (two-tiered) items presenting real-world scenarios (i.e., the introduction of reindeer on St. Paul Island in 1911, or the removal of wolves in Yellowstone National Park), followed by open-ended questions. Participants’ responses were analyzed based on three systems thinking concepts—indirect/distant connections, feedback loops, and emergent properties—as described by Jin et al. (2019). Indirect/distant connections refer to situations in which ecosystem components appear unrelated but are, in fact, indirectly connected. Feedback loops, however, are used to analyze interdependent relationships in complex systems. Negative feedback loops help maintain ecosystem stability by reducing change, while positive feedback loops amplify change and may destabilize the system. Emergent properties, as explained by Chi et al. (2012), describe how overall system patterns do not result from the actions of a single agent or a controlling group, but rather emerge from the collective interactions of all agents. In complex ecosystems, emergent properties include carrying capacity, exponential growth, and energy pyramids (Jin et al., 2019). During qualitative data analysis, codes were generated using established qualitative coding procedures (Saldaña, 2021). Data were transcribed line by line using MAXQDA software, and a thematic analysis was subsequently conducted (Creswell, 2013) Conclusions, Expected Outcomes or Findings Overall findings revealed science teacher candidates’ tendency to explain relationships in ecosystems depending on observable relationships and patterns rather than the dynamic, interconnected mechanisms underlying ecosystem behavior. When participants' responses were analyzed with respect to three systems thinking concepts, feedback loops were found be the most frequently mentioned systems thinking concept, followed by the emerging properties and the distant or indirect interactions among species. For example, when described the scenario regarding the introduction of 25 reindeer to St. Paul Island and the subsequent changes in the reindeer population (Scheffer, 1951), participants paid particular attention to the interrelationship between population size and food availability (i.e., negative feedback loop). They stated that as the reindeer population increased, grazing pressure reduced food resources, which caused population decline. The population decreases, then allowed food resources to recover. Participants also link changes in the reindeer population to food availability, competition, and reproduction (emergent properties). One participant, however, attributed fluctuations in population to the male-to-female reindeer ratio and argued that seasonal variation in breeding periods could influence population growth. Another participant drew attention to carrying capacity, emphasizing that exceeding an ecosystem’s capacity prevents that ecosystem from sustaining its population. This finding suggests that, while participants identified relevant key system components, they had difficulty integrating these elements into system-level mechanisms. When participants were asked to reason about a trophic cascade in Yellowstone National Park following the elimination of wolves, they pointed to multiple relationships among populations and noticed some distant or indirect interactions between populations. They linked the trophic cascade to human activity, explaining how it could affect wolf populations and degrade habitat. Others explained how eliminating wolves indirectly affects plants. The current findings have important implications for science teacher education programs toward the inclusion of systems thinking skills into their curricula. References Creswell, J. W. (2013). Qualitative inquiry & research design: Choosing among five approaches (3rd ed.). SAGE. Chi, M. T. H., Roscoe, R. D., Slotta, J. D., Roy, M., & Chase, C. C. (2012). Misconceived causal explanations for emergent processes. Cognitive Science, 36(1), 1–61. https://doi.org/10.1111/j.1551-6709.2011.01207.x Eilam, B. (2012). System thinking and feeding relations: Learning with a Live ecosystem model. Instructional Science, 40(2), 213–239. https://doi.org/10.1007/s11251-011-9175-4 Fraenkel, J. R., & Wallen, N. E. (2009). How to design and evaluate research in education (7th ed.). McGraw-Hill Higher Education. Gilissen, M. G. R., Knippels, M.-C. P. J., & van Joolingen, W. R. (2020). Teachers’ pedagogical content knowledge of systems thinking in biology education. International Journal of Science Education, 42(11), 1808–1830. https://doi.org/10.1080/09500693.2020.1755741 Grotzer, T. A., & Basca, B. B. (2003). How does grasping the underlying causalstructures of ecosystems impact students’ understanding? Journal of Biological Education, 38(1), 16–29. https://doi.org/10.1080/00219266.2003.9655891 Jin, H., Shin, H. J., Hokayem, H., Qureshi, F., & Jenkins, T. (2019). Secondary students’ understanding of ecosystems: A learning progression approach. International Journal of Science and Mathematics Education, 17(2), 217–235. https://doi.org/10.1007/s10763-017-9864-9 Mambrey, S., Schreiber, N., & Schmiemann, P. (2020). Young students' reasoning about ecosystems: The role of systems thinking, knowledge, conceptions and representation. Research of Science Education. https://doi.org/10.1007/s11165-020-09917-x Merriam, S. B., & Tisdell, E. J. (2016). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass. Ministry of National Education. (2024). Türkiye yüzyılı maarif modeli öğretim programları ortak metni [Common text of the Türkiye Century Education Model curricula]. National Research Council (2012) A framework for K-12 science education: Practices, crosscutting concepts, and core idea. Washington, D.C.: The National Academies Press. NGSS Lead States. (2013). Next Generation Science Standards: For states, by states. The National Academies Press. Riess, W., & Mischo, C. (2010). Promoting systems thinking through biology lessons. International Journal of Science Education, 32(6), 705–725. https://doi.org/10.1080/09500690902769946 Saldaña, J. (2021). The coding manual for qualitative researchers (4th ed.). Sage. Scheffer, V. B. (1951). The rise and fall of a reindeer herd. The Scientific Monthly, 73(6), 356–362. https://www.jstor.org/stable/20582 Schizas, D., Papatheodorou, E., & Stamou, G. (2019). Unravelling the holistic nature of ecosystems: Biology teachers’ conceptions of ecosystem balance and self-regulation. International Journal of Science Education, 41(18), 2626–2646. https://doi.org/10.1080/09500693.2019.1682963 Yin, R. K. (2009). Case study research: Design and methods (4th ed.). Sage Publications. Yin, R. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Thousand Oaks, California: SAGE. 99. Emerging Researchers' Group (for presentation at Emerging Researchers' Conference)
Paper Seeing Beyond the Answer: Mapping Teachers’ Interpretations of Students’ Knowledge and Reasoning about Heat and Temperature Middle East Technical University, Turkey (Türkiye) Presenting Author:Teacher noticing is widely recognized as central to responsive teaching (Sherin & van Es, 2009; Jacobs et al., 2010), yet science education research has often described what teachers attend to more than how they interpret student thinking in discipline-specific ways (Chan et al., 2021; Amador & Weston, 2024). This gap is especially consequential in conceptually dense topics such as heat and temperature, where students’ ideas are frequently shaped by everyday language and experience and where partially correct explanations can coexist with persistent conceptual confusions. Teachers’ interpretive stances therefore matter because they shape whether students’ utterances are treated as resources for scientific sense-making or positioned mainly as errors to be corrected (Coffey et al., 2011). Building on conceptualizations that distinguish attending, interpreting, and deciding how to respond (Jacobs et al., 2010; Barnhart & van Es, 2015), this study focuses on the interpreting facet and examines qualitative variation in physics teachers’ meaning-making about student conceptual knowledge and reasoning. Rather than classifying teacher noticing with broad evaluative labels, the study aims to make interpretive pathways visible and describable by identifying patterns in what teachers treat as salient, what they take as evidence, and how they warrant claims about students’ thinking. The study is grounded in a view of noticing as professional vision (Goodwin, 1994) and knowledge-based reasoning. Accordingly, even when teachers respond to the same student knowledge or reasoning in a shared stimulus, they can differ in what they treat as evidence and in how they justify their interpretations. Empirically, the study uses a shared instructional stimulus in which all participants respond to the same hypothetical classroom dialogue on heat and temperature. This design enables systematic comparison of interpretive stances under a common condition while still approximating classroom discourse and the kinds of student ideas teachers regularly encounter. The focal content domain includes heat, temperature, and internal energy, along with internationally documented conceptual difficulties and reasoning patterns. Although the data are situated in one national context, the domain and its learning challenges have been widely reported across countries, supporting the broader relevance of the interpretive patterns identified. Research question: What are the qualitative variations in physics teachers’ interpretations of students’ conceptual knowledge and reasoning when prompted by a hypothetical classroom dialogue on heat and temperature? The intended contribution is a transferable analytic lens that maps variation in teachers’ interpretive orientations and clarifies how different interpretations are constructed. By rendering interpretive work more explicit, the study provides a basis for teacher education discussions about analyzing student thinking in science and for future work examining how interpretive orientations relate to subsequent instructional moves and to students’ opportunities for scientific sense-making. Methodology, Methods, Research Instruments or Sources Used Participants and context: Data were collected from 68 in-service high school physics teachers in Ankara, Turkey, spanning a wide range of experience from novice to over 20 years. Basic demographics (gender, graduated department, and years of teaching) were recorded. Participation was voluntary, and teachers completed the written task in a single session. Instrument: The stimulus was an 80-line classroom-like dialogue between a teacher and five students. Students were not assigned fixed identities; a student could express multiple difficulties or partially correct ideas across the dialogue. The dialogue targeted internationally documented conceptual challenges in heat, temperature, internal energy, and related reasoning patterns. It was developed with the advisor, revised through expert feedback, and refined after a pilot. Teachers then answered three open-ended prompts aligned with noticing facets (Jacobs et al., 2010; van Es & Sherin, 2002): (1) statements noticed as important, (2) what those statements reveal about students’ conceptual knowledge and/or reasoning and what it means for the teacher, and (3) instructional decisions in response. This paper analyzes Prompt 2. Analytic approach: Analysis followed a qualitative, variation-oriented logic consistent with phenomenography (Marton, 1986) and adopted a second-order perspective, examining how teachers experience and describe student thinking rather than judging correctness. Responses were read repeatedly and segmented into meaning units, defined as interpretive segments where a teacher makes a claim about student thinking and provides an explanation or warrant. Through iterative, inductive coding and constant comparison, categories of description were developed to capture qualitatively different interpretive orientations. An outcome space (category map) was then constructed to represent structural relations among categories in terms of what is treated as salient, what counts as evidence, and how claims are warranted. MAXQDA supported anonymization (P1–P68), coding, memos, and traceability. Trustworthiness: Trustworthiness was supported through an iterative consensus approach. A second coder (physics education expert) independently coded about 20% of the dataset, followed by consensus meetings to refine decision rules and resolve ambiguities. The refined codebook was applied in a second coding pass, yielding high agreement for the coded subset. An audit trail was maintained via analytic memos and ongoing refinements of code definitions in MAXQDA to document category development and support transparency (Lincoln & Guba, 1985). Conclusions, Expected Outcomes or Findings An analysis of 970 coded units shows qualitative variation in teachers’ interpretations of student conceptual knowledge and reasoning. A coded unit was defined as a coherent interpretive segment in which a teacher makes a claim about student thinking and provides an explanation or warrant. The analysis develops an outcome space (category map) of four interpretive orientations, positioned relationally by systematic differences in what teachers treat as evidence and how they warrant claims about the same student utterances. The most frequent orientation is epistemically oriented interpretation, which foregrounds the coherence and justification of students’ reasoning. Teachers attend to conceptual linkages or mental models, possible sources of ideas, the adequacy of warrants (evidence or logic), and the perceived stability or changeability of students’ thinking. A second orientation is assessment-oriented interpretation, where teachers judge statements against scientific norms. Here assessment-oriented refers to normative evaluation. This includes correctness evaluations (approve, partially approve, or disapprove), brief diagnostic labels (for example, lack of knowledge or misconception), and holistic judgments about overall understanding. Affective-oriented interpretation foregrounds students’ emotions, motivation, and participation, with inferences about engagement, curiosity, hesitation, or reluctance. Metacognitively oriented interpretation captures inferences about students’ awareness and management of their thinking, including noticing confusion, monitoring through self-checking or self-correction, and regulation through strategy adjustment. Overall, the dominance of epistemic interpretations challenges the assumption that teachers primarily interpret student ideas through correctness alone. The resulting map can support teacher education by making interpretive stances visible and discussable, informing professional learning tasks that broaden interpretation beyond correctness checking and laying groundwork for subsequent studies linking interpretive orientations to instructional responses. References Amador, J. M., & Weston, T. L. (2024). A review of analytic frameworks for noticing in mathematics and science: Comparing noticing frameworks across disciplines and over time. International Journal of Science and Mathematics Education. https://doi.org/10.1007/s10763-024-10452-8. Barnhart, T., & van Es, E. (2015). Studying teacher noticing: Examining the relationship among preservice science teachers’ ability to attend, analyze and respond to student thinking. Teaching and Teacher Education, 45, 83–93. https://doi.org/10.1016/j.tate.2014.09.005. Chan, K. K. H., Xu, L., Cooper, R., Berry, A., & van Driel, J. H. (2021). Teacher noticing in science education: Do you see what I see? Studies in Science Education, 57(1), 1–44. https://doi.org/10.1080/03057267.2020.1755803. Coffey, J. E., Hammer, D., Levin, D. M., & Grant, T. (2011). The missing disciplinary substance of formative assessment. Journal of Research in Science Teaching, 48(10), 1109–1136. https://doi.org/10.1002/tea.20440. Goodwin, C. (1994). Professional vision. American Anthropologist, 96(3), 606–633. https://doi.org/10.1525/aa.1994.96.3.02a00100. Jacobs, V. R., Lamb, L. L., & Philipp, R. A. (2010). Professional noticing of children’s mathematical thinking. Journal for Research in Mathematics Education, 41(2), 169–202. https://www.jstor.org/stable/20720130. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage. Marton, F. (1986). Phenomenography: A research approach to investigating different understandings of reality. Journal of Thought, 21(3), 28–49. Sherin, M. G., & van Es, E. A. (2009). Effects of video club participation on teachers’ professional vision. Journal of Teacher Education, 60(1), 20–37. https://doi.org/10.1177/0022487108328155. van Es, E. A., & Sherin, M. G. (2002). Learning to notice: Scaffolding new teachers’ interpretations of classroom interactions. Journal of Technology and Teacher Education, 10(4), 571–596. 99. Emerging Researchers' Group (for presentation at Emerging Researchers' Conference)
Paper Profiles of Primary Mathematics Teachers and Their Links to Students’ Achievement and Motivation University of Tartu, Estonia Presenting Author:Mathematics education in primary schools provides a crucial foundation for students’ long-term academic development, with teacher-related factors playing a decisive role in shaping both achievement and motivation (Niemi et al., 2025). Teachers’ instructional practices are among the most influential school-based determinants of learning and motivation (Hattie, 2023). In mathematics classrooms, effective teaching extends beyond the delivery of content to encompass how teachers structure learning processes, foster motivation, and create emotionally supportive relationships with students (Desmet et al., 2023). Recent research highlights that the cognitive, motivational, and relational dimensions of teaching jointly contribute to students’ academic achievement, engagement, and well-being (Yang et al., 2021). Instructional practices in mathematics can be located along a continuum from structured, teacher-directed approaches that ensure clarity and organisation, to student-oriented and agency-supportive practices that promote exploration, autonomy, and ownership of learning (Lee et al., 2026). Motivationally supportive practices— particularly competence support and utility enhancement—help students perceive mathematics as both achievable and meaningful, thereby increasing engagement and persistence (Niemi et al., 2025). Beyond instructional strategies, the emotional and relational quality of teacher–student relationships (STRs) is a critical determinant of motivation and engagement (Longakit et al., 2025). Supportive relationships characterised by warmth, trust, and responsiveness have been shown to enhance students’ intrinsic motivation and emotional well-being (Desmet et al., 2023). Teacher emotional support has a particularly strong effect during the primary years, predicting both academic motivation and engagement through mechanisms such as self-efficacy and behavioural involvement (Yang et al., 2021). Teachers’ emotional intelligence and their ability to express genuine, appropriately regulated emotions play an essential role in maintaining these positive interactions and in creating psychologically safe classroom climates (Longakit et al., 2025). Despite this extensive body of evidence, teachers vary considerably in how they integrate cognitive, motivational, and emotional dimensions in their classroom practices (Kasa et al., 2024). Estonia provides a particularly valuable context for exploring such diversity. Although teachers enjoy high professional autonomy and perform strongly in international assessments such as PISA and TIMSS (Eisenschmidt et al., 2025), empirical studies reveal wide variation in classroom practices (Leijen & Pedaste, 2018). The coexistence of class teachers (generalists) and subject-specialist mathematics teachers adds further heterogeneity to instructional approaches (Tuul et al., 2015). While national policy promotes student-centred and competence-based learning, many classrooms still exhibit a blend of traditional and reform-oriented features (Leijen & Pedaste, 2018). Thus, although Estonia’s overall student achievement is high, little is known about which types of teachers and teaching profiles contribute most effectively to students’ performance and motivation in mathematics. This paper, which forms part of a doctoral project on mathematics teachers’ beliefs and practices in Estonia, employs a person-centred approach—specifically Latent Profile Analysis (LPA)—to identify distinct profiles of primary mathematics teachers based on their instructional practices, motivational support, and teacher–student relationships. The study further examines how these teacher profiles relate to students’ mathematics achievement and motivation. Research questions
Methodology, Methods, Research Instruments or Sources Used Methods The study draws on data from the second wave (2023) of an international longitudinal project investigating the development of students’ mathematics motivation in primary schools. The Research Ethics Committee of the University of Tartu granted ethical approval. Participants. Data were collected from 133 teachers (97% female) representing 45 schools across Estonia. The average teacher age was 46.4 years (SD = 12.4), and the average teaching experience was 20.5 years (SD = 13.6). The student sample included 1,672 students (52% girls; M age = 11.1, SD = 0.7) in grades 4 and 5. Measures. Teachers completed validated self-report scales assessing: (a) General instructional practices – Teacher-Oriented (e.g., “Memorise rules, procedures, and facts”), Student-Oriented (e.g., “Discuss incorrect solutions and what can be learned from them”), and Agency-Supportive (e.g., “Encourage students to bring in maths problems from daily life”); (b) Motivationally supportive practices – Competence Support (e.g., “Give students opportunities to justify their problem-solving strategies”) and Utility (e.g., “Relate lessons to students’ daily experiences”); (c) Teacher–student relationships – Affective Relationship (e.g., “I share a warm and trusting relationship with most students”) and Classroom Climate (e.g., “I make classroom routines clear and consistent”). All scales used a four-point Likert format (1 = never/strongly disagree to 4 = every or almost every lesson/strongly agree). Students completed the Expectancy–Value Scale, measuring five motivational components: intrinsic value, attainment, cost, utility, and perceived competence. Mathematics achievement was assessed using a 14-item standardised test based on released TIMSS 2011 items, with scores scaled via Rasch modelling (M = 500, SD = 100). Socioeconomic status (SES) was measured by the number of books at home (1 = 0–10; 5 = 200+). Analytical strategy. Analyses were conducted in Mplus 8.11. Confirmatory Factor Analysis (CFA) validated the measurement models (CFI ≥ .95, RMSEA ≤ .08). Latent Profile Analysis (LPA) identified subgroups of teachers based on seven standardised indicators. Model fit was evaluated using AIC, BIC, SABIC, entropy (>.80), and likelihood ratio tests (LMR, BLRT). BCH analysis examined profile differences in student outcomes (achievement, motivation), and Kruskal–Wallis tests explored SES variation across profiles. Conclusions, Expected Outcomes or Findings A three-profile model provided the best statistical and conceptual fit (BIC = 1784.66; entropy = .815). Profile 1 – Teacher-centred (43%) Teachers in this group scored low on student orientation, agency, support, and affective aspects, with a focus primarily on structure, clarity, and content delivery. Their teaching reflected a traditional, instructor-led approach. Profile 2 – Balanced, utility-oriented (31%) These teachers demonstrated moderate values across all dimensions, with a strong emphasis on the usefulness and real-life relevance of mathematics. Their teaching blended structured guidance with moderate autonomy support. Profile 3 – Student-centred, supportive (26%) Teachers in this profile reported high levels across all dimensions, combining autonomy support, emotional warmth, and motivational engagement. Student outcomes. Students taught by teacher-centred teachers achieved the highest mathematics scores (M = 672), significantly outperforming those in Profile 2 (p = .013). Conversely, students of student-centred supportive teachers showed the highest intrinsic motivation (p < .05) and perceived competence (p < .05). No significant differences emerged for cost or utility value. SES differed across profiles, with students in Profile 3 classrooms reporting slightly higher backgrounds (p < .05). Theoretical and educational implications. The findings support multidimensional models of teaching quality (Kunter et al., 2013) and motivational theories such as Self-Determination Theory (Ryan & Deci, 2000) and Expectancy–Value Theory (Eccles & Wigfield, 2020). Effective teaching involves balancing structure with autonomy support and relational warmth. In practice, the results underscore the importance of professional development that enhances teachers’ reflective awareness of their instructional profiles. Supporting teacher-centred practitioners in integrating more autonomy-supportive feedback and helping student-centred teachers maintain cognitive rigour may encourage more balanced and effective teaching. This study thus offers an evidence-based framework for differentiated teacher learning and motivation-focused instructional design. References Desmet, O. A., Camargo Salamanca, S., Lee, H., & Tuzgen, A. (2023). The effect of student–teacher relationships on students’ math motivation across EU countries. Journal of Advanced Academics, 34(3–4), 271. Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary educational psychology, 61, 101859. Eisenschmidt, E., Ahtiainen, R., Kondratjev, B. S., & Sillavee, R. (2025). A study of Finnish and Estonian principals’ perceptions of strategies that foster teacher involvement in school development. International Journal of Leadership in Education, 28(1), 81. Hattie, J. (2023). Visible learning: The sequel. New York. Kasa, Y., Areaya, S., & Woldemichael, M. (2024). Relationship between university teachers’ beliefs about teaching mathematics and their instructional practices. Cogent Education, 11(1), 2335838. Kunter, M., Klusmann, U., Baumert, J., Richter, D., Voss, T., & Hachfeld, A. (2013). Professional competence of teachers: Effects on instructional quality and student development. Journal of Educational Psychology, 105(3), 805. Lee, E., Baird, T. D., & Lee, D. (2026). Supporting student agency through student-centred learning across educational contexts. Innovations in Education and Teaching International, 63(1), 163. Leijen, Ä., & Pedaste, M. (2018). Pedagogical beliefs, instructional practices, and opportunities for teachers' professional development in Estonia. In The teacher’s role in the changing globalising world (pp. 33–46). Brill. Longakit, J., Lobo, J., Gazali, N., Toring-Aque, L., Sayson, M., Aque, F. J., ... & Celestial, E. F. (2025). The influence of teacher emotional support on academic engagement of university students: Examining the mediating role of academic motivation through the lens of Self-determination theory. Sportis Scientific Journal of School Sport Physical Education and Psychomotricity, 11(2), 1-25. Niemi, L. H. L., Holm, M., Haataja, E., Ilomanni, P. K., & Laine, A. (2025). The role of teachers’ beliefs and professional development in students’ mathematics motivation in primary education. LUMAT, 13(1). Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68. Tuul, M., Mikser, R., Neudorf, E., & Ugaste, A. (2015). Estonian preschool teachers’ aspirations for curricular autonomy: The gap between an ideal and professional practice. In Early childhood pedagogies (pp. 131–147). Routledge. Yang, Y., Li, G., Su, Z., & Yuan, Y. (2021). Teacher's emotional support and math performance: The chain mediating effect of academic self-efficacy and math behavioral engagement. Frontiers in psychology, 12, 651608. 99. Emerging Researchers' Group (for presentation at Emerging Researchers' Conference)
Paper Examining Pre-service Science Teachers’ Genetic Determinism, Knowledge, Attitudes, and Decisions Regarding Modern Genetic Applications Sakarya University, Turkey (Türkiye) Presenting Author:In the twenty-first century, individuals are increasingly encountering genetic-related issues all around the world due to technological advances, societal demands, and political factors (Aivelo & Uitto, 2021; Cebesoy & Öztekin, 2016). As science education is being renewed to meet the needs of the century, genetics-related issues are also being taken into consideration within the scope of science courses. Genetics-related issues can be viewed as an umbrella term encompassing various issues, such as genetic testing, cloning, gene therapy, and genetically modified foods (Lederman et al., 2014). The applications of genetic engineering, which affect societies in various ways, are discussed in different perspectives, such as ethical, political, and religious perspectives (Arslan, 2020). Genetic determinism is the belief that genes alone determine phenotypes, often underestimating the roles of environmental and epigenetic factors (Bruu-Carver et al., 2017). This misconception is common in genetics education (Aivelo & Uitto, 2021; Tornabene et al., 2020). Current scientific models highlight the complex interactions between multiple genes, epigenetics, and environmental influences, shifting from a deterministic to a probabilistic understanding (Bruu-Carver et al., 2017). However, studies on individuals' beliefs regarding genetic determinism are limited (Cebesoy & Karışan, 2019; Gericke et al., 2017; Stern et al., 2020), prompting researchers to explore pre-service science teachers' (PSTs') beliefs about genetic determinism. Additionally, the rapid production of information in the genomics field makes it imperative for citizens to develop informed attitudes and make informed decisions on genetic-related issues. This presents a significant challenge for science educators, who must closely follow scientific developments, keep their content knowledge up-to-date, and adapt to new technological innovations (van der Zande et al., 2012). The science education literature includes studies that focus on specific dimensions related to genetics, such as content knowledge of genetic topics (van der Zande et al., 2011) and informal reasoning (Sadler & Zeidler, 2004). Also, when dealing with socio-scientific issues like modern genetic applications, it is necessary to consider not only individuals' knowledge levels but also their attitudes and their decision-making. There have been studies that explore individuals' attitudes towards genetic issues and their decision-making in various scenarios, such as modern genetic applications and biotechnological advancements (Cebesoy & Rundgren, 2023; Soğukpınar & Karışan, 2020). This research takes a comprehensive look at genetic determinism, knowledge, attitudes, and decision-making. Additionally, the current mixed-methods study aims to compare quantitative and qualitative data regarding PSTs attitudes toward modern genetic applications. PSTs were chosen as the focus of this research because they will play a critical role in teaching future science and biology courses. Within the scope of this study, answers were sought to five research questions. RQ1: What are PSTs’ beliefs in genetic determinism? RO2: What are the PSTs’ knowledge about the complexity of gene-environment interactions and modern genetics and genomics? RQ3: What are the PSTs’ attitudes toward applications of modern genetics and genomics (gene therapy, genetic testing, prenatal genetic testing and personalised medicine-pharmacogenomics cases)? RQ4: How do PSTs' reasoning behind their decisions regarding modern genetic applications in terms of the SEE-SEP model? RQ5: To what extent and in what ways do qualitative findings explain the quantitative results regarding PSTs’ attitudes toward modern genetic applications? Methodology, Methods, Research Instruments or Sources Used This study employs an explanatory sequential mixed-methods design, which integrates both quantitative and qualitative phases (Creswell & Plano Clark, 2018). The research begins with a quantitative phase, followed by a qualitative phase. In the quantitative phase, data were gathered from 79 pre-service teachers (PSTs) in the 2nd, 3rd, and 4th grades using the PUGGS instrument. PUGGS is designed to assess public beliefs regarding genetic determinism, their knowledge of gene-environment interactions, and modern genetics and genomics. Also, PUGSS assess attitudes toward the modern genetic applications. Originally developed by Bruu-Carver et al. (2017), PUGGS was translated and adapted for Turkish by Cebesoy and Karisan (2019), resulting in a valid and reliable Turkish version of the instrument with preservice teachers. The results from PUGGS focus on four key areas: “belief in genetic determinism,” “knowledge of gene-environment interactions,” “knowledge of modern genetics and genomics,” and “attitudes toward applications of modern genetics and genomics.” PSTs' attitudes toward modern genetics and genomics are particularly significant for the qualitative phase of the study. In the qualitative phase, the researchers aim to explore PSTs' attitudes toward modern genetic applications through semi-structured interviews. These interviews will involve nine participants selected using maximum diversity sampling, ensuring representation across various levels of attitudes as indicated by their PUGGS scores: high, medium, and low. The semi-structured interviews concentrate on four different modern genetic applications, including gene therapy, genetic testing, prenatal genetic testing, and personalized medicine-pharmacogenomics. These four applications were chosen to be consistent with the items in the attitude section of PUGGS. The researchers developed the interview protocol and sought expert opinions to ensure its quality and relevance. This research was conducted with undergraduate students at a public university in a large city in the Eastern Marmara Region of Türkiye. All participants were enrolled in the Science Education Department. Data collection took place during the spring semester of the 2024-2025 academic year. In analysing the data, the PUGGS was assessed using the provided answer key. The semi-structured interviews were analysed by utilising the SEE-SEP model as a coding framework, developed by Chang Rundgren and Rundgren (2010). The reliability of the PUGGS instrument was established through the calculation of Cronbach’s alpha coefficients as .75, which meets the acceptable reliability (Frankel et al., 2012). For the qualitative phase, inter-coder agreement was calculated for the semi-structured interviews, yielding a high level of agreement (0.91), indicating reliable coding (Miles & Huberman, 1994). Conclusions, Expected Outcomes or Findings PSTs display a range of beliefs regarding genetic determinism as it pertains to both biological and social traits. Descriptive statistics show that PSTs possess a good grasp of gene–environment interactions, achieving a mean score of 0.824 (SD = 0.162) on a scale where the maximum score is 1. However, their understanding of modern genetics and genomics falls short, with a mean score of only 0.425 (SD = 0.134). On the whole, PSTs tend to have a positive outlook toward modern genetic applications, reflected in a mean score of 3.00 (SD = 0.37). Among these, prenatal genetic testing garnered the highest favor, with a mean of 3.17 (SD = 0.50), closely followed by pharmacogenomics at 3.01 (SD = 0.45). Although attitudes toward gene therapy (M = 2.94, SD = 0.42) and genetic testing (M = 2.86, SD = 0.44) are slightly lower, they are still on the positive side, highlighting an overall favorable perspective on these advancements. As the quantitative data showed, PSTs’ decisions about modern genetic applications were predominantly shaped by the value aspect of the SEE-SEP model, rather than scientific knowledge or personal experience. PSTs’ decisions across the subject areas of the SEE–SEP model were primarily framed in terms of ethics/morality, which accounted for 33% of all coded statements. Science (21%), policy (21%), economy (14%), and sociology/culture (11%) of the statements indicate that PSTs prefer to explain their decisions. Overall, these findings suggest that PSTs’ support for or opposition to modern genetic applications was primarily driven by ethical/ moral and value-based reasoning in their decisions. Upon examining both quantitative and qualitative data from the 9 participants who also participated in the qualitative aspect of the study, a convergence in the findings between the two data sources was evident. References Aivelo, T., & Uitto, A. (2021). Factors explaining students’ attitudes towards learning genetics and belief in genetic determinism. International Journal of Science Education, 43(9), 1408–1425. https://doi.org/10.1080/09500693.2021.1917789 Arslan, H. Ö. (2020). Selection of socioscientific issues and examples of contemporary dilemmas. In M. Genç (Ed.), Socioscientific issues from theory to practice (pp. 69–96). Nobel Academic Publishing. Bruu-Carver, R., Castéra, J., Gericke, N., Evangelista, N. A. M., & El-Hani, C. N. (2017). 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