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11 SES 06 A: Quality Higher Education: Developing Students' Competences
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11. Educational Improvement and Quality Assurance
Paper Exploring Lithuanian Students’ Self-Regulating Strategies: A Pathway to Quality Learning Vilnius University, Mykolas Romeris University (Lithuania) Presenting Author:ABSTRACT. The role of learners’ self-regulation of learning and its impacts have been widely investigated at all levels of education (Pintrich, 1999; van Hout-Wolters, 2000; Zimmerman, 2002, 2015; Nicol and Macfarlane-Dick, 2006; Cassidy, 2011; Kostons et al., 2012; Burkšaitienė, 2012, 2013, 2016; Vandevelde et al., 2015; Spruce & Bol, 2015; Beaumont et al., 2016; Schunk & Greene, 2018; Zeidner & Stoeger, 2019; Vosniadou et al., 2020; Porter & Peters-Burton, 2021;Winne & Azevedo, 2022; Backers et al., 2023; among others). In primary and secondary learning environments, it has been established that fostering self-regulated learning is meaningful and that its beneficial outcomes are likely to be achieved if a special training on how to promote learners’ self-regulated learning is provided to their teachers (Vandevelde et al., 2015; Porter & Peters-Burton, 2021 In higher education, self-regulated learning has in recent years become a prerequisite of academic success and developing lifelong learning skills (Cassidy, 2011). Research in this field has been focused on how students self-regulate their own learning to help them to develop key processes that they may lack, including goal setting, time management, learning strategies, self-evaluation, self-motivational beliefs, and seeking help or information (Nicol & Macfarlane-Dick, 2006; van der Veen & Peetsma, 2009; Abar & Loken, 2010; Cassidy, 2011; Kostons et al., 2012, etc.). Research findings show that students’ awareness of themselves as learners and the use of different methods of regulating one’s learning are the key factors leading to academic success (Pintrich, 1999; Zimmerman, 2002, 2015; Burkšaitienė, 2012, 2013, 2016; Chen, 2016; Ruegg, 2018; Yang, 2016, Pham, 2021; Nicol & Macfarlane-Dick, 2006; among others). It has been reported that when used to promote students’ self-regulated learning, such methods as peer feedback, reflection journals and self-assessment tasks had a positive impact on students' learning, raised their awareness and promoted critical thinking (Klenowski & Lunt, 2008; Yang, 2016; Pham, 2021), as well as fostered engagement in the process of studying, directed students towards further learning and developed their creativity (Burkšaitienė, 2012, 2013, 2016). Students’ self-regulated learning has been widely researched in many European countries, however, in Lithuania, it has not been investigated much (Burkšaitienė, 2012, 2013, 2016; Burkšaitienė et al., 2021; Horlenko, 2025). Therefore, the present study contributes to the research literature by presenting a Lithuanian perspective in the field and calls for further research. The present research is based on the theory of self-directed learning. The most relevant theoretical assumption underlying this theory is that self-directed learning is a process which includes three phases (forethought, performance, and reflection) that promote active engagement in learning by fostering goal setting, self-monitoring, and reflection, which leads to academic success (Zimmerman, 2002, 2015). This theory is relevant for the present research since it stresses that effective use of self-regulating strategies can reduce discrepancies between students’ current understandings, their performance and the goal (Hattie & Timperley, 2007). Besides, it emphasises that to promote self-regulated learning, it is critical to support and direct learners to move forward to the level of self-regulation at which they can identify and use the strategies that are the most effective to meet their goals, which is a path of raising the quality of learning in higher education (Huang, 2018). This study is part of larger mixed-method research conducted at four universities in Lithuania, with the participation of 309 third and fourth-year undergraduate students from various fields of studies, including technological sciences, social sciences and humanities. The study was guided by the research question: “Which processes of self-regulation do the students use in their studies?”. To answer the research question, an online survey consisting of 60 close-ended and a few open-ended questions was conducted. Methodology, Methods, Research Instruments or Sources Used The present study reports on the results of the analysis of students’ responses to one of the open-ended questions, i.e.: “What strategies do you use when you do not have all the necessary information for accomplishing given assignments?”. The data were collected from the responses provided by 140 students from two universities. To analyse the data, qualitative approach was chosen and inductive (bottom-up) content analysis was carried out. The suitability of this method for the present study is supported by the literature. According to Elo and Kyngäs, “it is used in cases where there are no previous studies dealing with the phenomenon or when it is fragmented” and allows to establish content-related categories that result in a comprehensive description of the phenomenon (Elo & Kyngäs, 2007, p. 107). The data were analysed in accordance with the procedure described by Elo and Kyngäs. During the first stage, the students' responses were read several times and units of analysis relevant to the research question were selected. This stage was followed by (1) open coding, which included writing down the headings that reflected all aspects of self-regulating strategies used by the students when they did not have enough information, which prevented them from fulfilling their academic assignments), (2) generating initial categories and grouping them under higher order headings, and (3) abstracting, which covered the process of naming each category, identifying subcategories and grouping them. During the last stage, extracts from the students' responses illustrating each subcategory were selected. To ensure reliability and validity of the results, inductive content analysis was conducted by two independent experts, who discussed their findings and made agreed decisions. The sample size of this study could be recognized as its main limitation not allowing for wide-scale generalisations. The research was carried out in line with the requirements of ethics. The questionnaire did not collect any personal data that could potentially identify the respondents. The respondents were provided with the information regarding the research purpose, the amount of time required to complete the survey and were informed that their responses will be used exclusively for the purposes of research. Students’ participation in the survey was anonymous and voluntary. Conclusions, Expected Outcomes or Findings This study revealed four categories and five subcategories of self-regulating strategies used by the students, including (1) combining different self-regulating strategies, (2) relying exclusively on the strategy of searching the Internet, (3) seeking help from different stakeholders, and (4) taking no action. The general conclusion is that most study participants (n= 136) were aware of and adopted from one to three self-regulating strategies of seeking support and / or information necessary for the accomplishment of academic assignments. More specifically, 59 students combined different self-regulating strategies (they looked for information in university library resources, sought support from stakeholders within the academia and outside it, searched the Internet and / or contacted an online community of practice). A slightly smaller number of students (n = 56) relied solely on the strategy of searching the Internet when they faced the problem of lack of information. As they did not explain why they used exclusively this strategy, the reason of their choice remains not clear. It is also not clear what their prior experience of using self-regulating strategies was. What was surprising though, was the fact that no students mentioned that they used some Generative AI tools, which calls for a separate investigation. The finding that 21 students relied solely on the support provided by some experts in the field suggests that their choice is effective as it leads to quick solutions to a problem. On the other hand, these students could be encouraged to use more self-regulating strategies, too. The result that only four students did not use any self-regulating strategies is promising. It is worth mentioning that these students did not provide any explanations why they took no action. In this case, a solution could be to provide support to these students, which could foster the quality of their learning at the university. References Abar, B., & Loken, E. (2010). Self-regulated learning and self-directed study in a pre-college sample. Learning and Individual Differences, 20(1), 25–29. https://doi.org/10.1016/j.lindif.2009.09.002. Backers, L., De Smedt, F., & van Keer, H. (2023). Teachers as agents of self-regulated learning (SRL): Studying the relations between teachers’ knowledge, beliefs, and SRL implementation across educational levels. Unterrichtswissenschaft, 52, 15–38. https://doi.org/10.1007/s42010-023-00190-1 Beaumont, C., Moscrop, C., & Canning, S. (2016). Easing the transition from school to HE: scaffolding the development of self-regulated learning through a dialogic approach to feedback. Journal of Further and Higher Education, 40(3), 331-350.3. Burkšaitienė, N. (2012). Peer feedback for fostering students’ metacognitive skills of thinking about learning in a course of English for law. Societal studies, 4(4), 1341-1355.4. Burkšaitienė, N. (2013). Project-based learning for the enhancement of self-regulated learning in a course of ESP. Radoša Personība: Zinātnisko Rakstu Krājums / Creative Personality. Riga: Riga pedagogy and education management academy, XI, 164-172. Burkšaitienė, Nijolė; Stojkovič, Nadežda. (2016). Students’ reflections in a course of Modern English and creative writing: a path towards self-regulated learning. Language and Contexts, 7(1, part 2), 212-225. Burkšaitienė, N.; Leščinskij, R.; Suchanova, J.; Šliogerienė, J. (2021). Self-Directedness for sustainable learning in university studies: Lithuanian students’ perspective. Sustainability, 13, 9467. https://doi.org/10.3390/su13169467. Cassidy, S. (2011). Self-regulated learning in higher education: identifying key component processes. Studies in Higher Education, 36(8), 989-1000. Elo, S., & Kyngäs, H. (2007). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107-115. https:// doi.org/10.1111/j.1365-2648.2007.04569.x. Garrison, D. R. (1997). Self-directed learning: Toward a comprehensive model. Adult Education Quarterly, 48, 18–33. Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 8–112. https://doi.org/10.3102/003465430298487. Horlenko, K. (2025). In teachers’ view: exploring teacher professional vision for student self-regulated learning in the classroom: Doctoral dissertation. Pintrich, P. R., (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31(6), p. 459–470. https://doi.org/10.1016/S0883-0355(99)00015-4. Porter, A. N., & Peters-Burton, E. E. (2021). Investigating teacher development of self-regulated learning skills in secondary science students. Teaching and Teacher Education, 105, 103403. https://doi.org/10.1016/j.tate.2021.103403. Zeidner, M., & Stoeger, H. (2019). Self-Regulated Learning (SRL): A guide for the perplexed. High Ability Studies, 30(1–2), 9–51. Zimmerman, B. J. (2002). Achieving academic excellence: A self-regulatory perspective. In: M. Ferrari (Ed.), The pursuit of excellence through education, 85–110. Erlbaum. Zimmerman, B. J. (2015). Self-regulated learning: theories, measures, and outcomes. In: International Encyclopaedia of the Social & Behavioral Sciences, 541–546. Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.26060-1300. 11. Educational Improvement and Quality Assurance
Paper English Willingness to Communicate during Internship as an Indicator of Educational Quality Improvement 1: Suranaree University of Technology, Thailand; 2: Riga Stradins University, Latvia Presenting Author:
Across Europe and internationally, higher education has placed growing emphasis on employability, graduate attributes, and the quality of learning outcomes in internship and work-based learning (European Commission, 2017; Yorke & Knight, 2006). Communication competence, particularly the ability to use a second language effectively in professional contexts, is widely recognised as a key component of graduate readiness (Bennett, 2019). Nevertheless, research increasingly suggests that linguistic knowledge alone does not guarantee meaningful communicative engagement, especially in high-stakes workplace settings where social evaluation and professional identity are involved (Jackson, 2015).
In non-European English-as-a-foreign-language (EFL) contexts such as Thailand, these challenges are particularly visible. Despite sustained policy attention to English proficiency and extensive formal instruction, many university students remain hesitant to engage in English communication. This situation highlights a broader quality gap between instructional provision and actual communicative performance (Sirijanchuen & Tangkiengsirisin, 2025). Prior studies have linked this gap to pedagogical constraints, limited interactional opportunities, and restricted exposure to authentic communication, as well as to psychological barriers such as anxiety, fear of negative evaluation, and low confidence (Chaisiri, 2022; Tian & Dumlao, 2020).
From a psychological perspective, willingness to communicate (WTC) provides a useful lens for examining the quality of second language learning outcomes. WTC reflects learners’ readiness to convert language competence into communicative action and is shaped by an interaction of psychological, social, and situational factors, including self-confidence, motivation, anxiety, and perceived communicative risk (MacIntyre et al., 1998; Yashima, 2002). Research consistently shows that WTC varies across interpersonal contexts, with learners typically more willing to communicate with familiar partners than with strangers, highlighting the roles of trust and social distance (Peng, 2014; Kruk, 2022). Accordingly, WTC is increasingly understood as a dynamic and context-sensitive construct rather than a stable individual trait.
Internships offer a particularly important setting for investigating WTC because they require learners to communicate in authentic professional environments where performance, feedback, and social judgement are integral to the learning process (Jackson, 2015). In such contexts, communication quality depends not only on linguistic ability but also on psychological readiness to manage uncertainty, interpersonal risk, and evaluative pressure, making internship communication highly relevant to European discussions on work-integrated learning and graduate employability (European Commission, 2017).
Among psychological factors influencing WTC, self-esteem plays a central role. As a global evaluation of self-worth, self-esteem supports emotional regulation, confidence, and willingness to take communicative risks (Orth & Robins, 2014). While previous research has linked self-esteem to second language communication, most studies have focused on classroom-based settings, offering limited insight into how self-esteem operates across different interpersonal relationships in workplace learning environments (Dörnyei, 2005).
In addition to individual psychological resources, social exposure shapes communicative readiness. Family educational background, intercultural contact, and engagement with English in digital spaces influence learners’ confidence and opportunities for authentic interaction (Baker & MacIntyre, 2000; Lin & Warschauer, 2015). However, few studies have examined how psychological and social factors jointly contribute to communication quality during internships, particularly in Southeast Asian contexts.
Addressing this gap, the present study applies first-order structural equation modelling (SEM) to examine how self-esteem and selected background variables—father’s education, number of foreign friends, and English use on social media—predict English WTC across three interpersonal contexts: communication with strangers, acquaintances, and friends during internship. Although situated in a non-European context, the findings contribute to European debates on the quality of work-based learning by highlighting the role of psychological readiness in shaping communicative engagement in professional settings. Methodology, Methods, Research Instruments or Sources Used Participants This study involved 312 fourth-year university students in Thailand who were participating in internship programs. Internships were selected as the study context because they require students to use English in real workplace situations with clear performance expectations. Most participants were female (71.15%) and enrolled in business-related programs. Instruments Willingness to Communicate (WTC). English WTC was measured using a 12-item scale adapted from McCroskey and Baer (1985). The scale assessed students’ willingness to communicate with strangers, acquaintances, and friends. Responses were rated on a 5-point Likert scale. Procedure Data were collected using a paper-based, self-administered questionnaire distributed during regular class sessions. Prior to data collection, students were informed about the purpose of the study, the voluntary nature of participation, and their right to withdraw at any time without penalty. Informed consent was obtained from all participants before questionnaire administration. Eligibility criteria required participants to be fourth-year undergraduate students who were actively enrolled in an internship program at the time of data collection. Students who were not undertaking an internship, were not in their final year of study, or declined to provide informed consent were excluded from participation. In addition, questionnaires with substantial missing data (more than 10% unanswered items) were excluded from the final dataset to ensure data quality and analytical reliability. Data Analysis Data analysis was conducted using SPSS version 29.0 and Mplus version 8.3. First, descriptive statistics—including means, standard deviations, and distributional properties—were calculated to summarize participants’ characteristics and observed variables. Pearson correlation analyses were then performed to examine preliminary relationships among self-esteem, background variables, and willingness to communicate (WTC), and to assess the suitability of the data for multivariate analysis. Confirmatory factor analysis (CFA) was subsequently employed to evaluate the measurement model and to verify the construct validity of the latent variables. Model fit was assessed using multiple goodness-of-fit indices, including the comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). Following validation of the measurement model, structural equation modeling (SEM) was used to test the hypothesized relationships between self-esteem, background variables, and English WTC across three interpersonal contexts: communication with strangers, acquaintances, and friends. Standardized path coefficients were examined to determine the strength and significance of relationships, with statistical significance set at p < .05. Conclusions, Expected Outcomes or Findings The findings supports psychological perspectives that conceptualize communication quality in work-based learning as dependent not only on linguistic competence, but also on learners’ internal readiness to engage under evaluative pressure (Harvey, 2024; Leal Filho et al., 2025). From a social cognitive perspective, self-esteem operates as a foundational resource that strengthens communicative self-efficacy, thereby enhancing motivation and reducing avoidance in challenging interactions (Bandura et al., 1999; Harris & Orth, 2020). Extending classroom-based research on second language communication (Clément & Kruidenier, 1985; Dörnyei, 2005), this study shows that these psychological mechanisms remain salient in authentic professional environments, where communication contributes directly to learners’ emerging professional identities and perceived employability. In this sense, WTC during internships can be interpreted as an indicator of the quality of work-based learning outcomes (Westerheijden & Kohoutek, 2014). The findings further suggest that supporting students’ emotional and psychological development is essential for fostering communication quality, particularly in contexts where sociocultural norms related to face-saving and fear of error may inhibit oral participation (DeWaelsche, 2015). From a European perspective, this resonates with current debates on integrating psychological support, reflective practice, and formative feedback into internship and work-integrated learning models aligned with employability agendas (European Commission, 2025). Although situated in a non-European context, these findings contribute to European discussions on the quality and effectiveness of internships by underscoring the importance of psychological readiness alongside structural and curricular design. The study suggests that employability-oriented reforms within the Bologna Process and related European frameworks may benefit from greater attention to learners’ internal psychological resources, positioning WTC as a meaningful bridge between language education, psychological development, and high-quality work-based learning outcomes (Alzafari & Ursin, 2019; Westerheijden & Kohoutek, 2014). References Alzafari, K., & Ursin, J. (2019). Implementation of quality assurance standards in European higher education: Does context matter? Quality in Higher Education, 25(1), 58–75. https://doi.org/10.1080/13538322.2019.1578069 Baker, S. C., & MacIntyre, P. D. (2000). The role of gender and immersion in communication and second language orientations. Language Learning, 50(2), 311–341. https://doi.org/10.1111/0023-8333.00119 Bandura, A., Freeman, W. H., & Lightsey, R. (1999). Self-efficacy: The exercise of control. Journal of Cognitive Psychotherapy, 13(2), 158–166. Dörnyei, Z. (2005). The psychology of the language learner: Individual differences in second language acquisition. Lawrence Erlbaum. European Commission. (2017). Improving and modernising education. Publications Office of the European Union. European Commission. (2025). The European Social Fund Plus (ESF+): Supporting skills, employment and social inclusion. https://european-social-fund-plus.ec.europa.eu Harvey, L. (2024). What have we learned from 30 years of Quality in Higher Education? Academics’ views of quality assurance. Quality in Higher Education, 30(3), 360–375. https://doi.org/10.1080/13538322.2024.2385793 Harris, M. A., & Orth, U. (2020). The link between self-esteem and social relationships: A meta-analysis of longitudinal studies. Journal of Personality and Social Psychology, 119(6), 1459–1477. https://doi.org/10.1037/pspp0000265 Jackson, D. (2015). Employability skill development in work-integrated learning: Barriers and best practice. Studies in Higher Education, 40(2), 350–372. https://doi.org/10.1080/03075079.2013.842221 Kruk, M. (2022). Dynamicity and contextuality of willingness to communicate in a second language. System, 105, 102722. https://doi.org/10.1016/j.system.2022.102722 Leal Filho, W., Sigahi, T. F. A. C., Anholon, R., et al. (2025). Promoting sustainable development via stakeholder engagement in higher education. Environmental Sciences Europe, 37, 64. https://doi.org/10.1186/s12302-025-01101-0 MacIntyre, P. D., Clément, R., Dörnyei, Z., & Noels, K. (1998). Conceptualizing willingness to communicate in a L2: A situational model of L2 confidence and affiliation. The Modern Language Journal, 82(4), 545–562. https://doi.org/10.1111/j.1540-4781.1998.tb05543.x Orth, U., & Robins, R. W. (2014). The development of self-esteem. Current Directions in Psychological Science, 23(5), 381–387. https://doi.org/10.1177/0963721414547414 Rudzinska, I., & Khampirat, B. (2019). Cultural diversity in English language learning in Thailand and Latvia. Journal of Education Culture and Society, 10(1), 219–233. https://doi.org/10.15503/jecs20191.219.233 Westerheijden, D. F., & Kohoutek, J. (2014). Implementation and translation: From European standards to internal quality work. In H. Eggins (Ed.), Drivers and barriers to achieving quality in higher education (pp. 1–11). Sense Publishers. https://doi.org/10.1007/978-94-6209-494-9 Yorke, M., & Knight, P. T. (2006). Embedding employability into the curriculum. Higher Education Academy. 11. Educational Improvement and Quality Assurance
Paper The Influence of Selective High Schools on Students’ Academic Performance and College Admission 1: Nanjing University, China, People's Republic of; 2: Peking University, China, People's Republic of Presenting Author:1.Background One common feature of education systems worldwide is the significant investment of time and resources by students seeking admission to selective high schools. Numerous studies have explored the impact of attending selective schools on academic performance, yet the findings have been notably varied. While some studies report positive results, most show no significant effect. There are several potential explanations for the mixed findings. Firstly, attending a more selective school may offer students better educational resources, the opportunity to study alongside high-achieving peers, and the chance to learn from top-quality teachers, which may have positive effects. Conversely, high-achieving peers can cause students to underestimate their abilities, reducing self-efficacy and leading to poor academic performance. Secondly, inconsistent findings may be attributed to variations in the approaches used to control for selection bias. Some studies have utilized randomized admissions lotteries, which may not be feasible in most educational contexts. As an alternative, other studies have used the discontinuity around the entrance exam cutoff scores. Lastly, differences in education and economic development levels in the researched areas can lead to varying research conclusions even within the same country. 2.Research Questions Due to the inconclusive nature of previous findings, this study seeks to provide additional evidence on the impacts of selective high schools. We first investigated the effects of attending selective high schools on students’ performance on NCEE. We then divide the selective high schools into two tiers based on their admission scores: Tier-1 high schools (more than 98% of students meet the cutoff for the top 30% four-year universities) and Tier-2 high schools (more than 90% of students meet the cutoff for the top 30% four-year universities). Furthermore, we investigated the effect on college admission, as indicated by the university a student ultimately attends. This outcome is crucial as it reflects not only a student’s academic performance but also a range of non-academic skills, including application strategies, information-gathering abilities, etc. Finally, our research examined the impact on students’ self-efficacy as the potential mechanism. This outcome is important as it strongly correlates with individuals’ academic performance and long-term achievement. 3. Significance This study makes several significant contributions to the existing literature. One advantage is that we were able to investigate the effects on a broader range of outcomes, such as total scores, test scores for each subject in the National College Entrance Exam (NCEE), and perceived self-efficacy, which offers a more comprehensive understanding of the effects of selective high schools. Furthermore, our study has the advantage of observing the university that students ultimately attend. Second, our data was collected from the capital city, where the level of education and economic development is among the best in China, and school choice is actively discouraged by the local education department to foster educational equity. The impacts in this context may differ substantially from those observed in other contexts, such as rural counties. Additionally, China introduced a new scheme for NCEE in 2014, which may impact the dynamics of school choice and the effects of school quality. Our data includes students who took NCEE in 2021, enabling us to investigate the impact of selective high schools after implementing the new scheme. Lastly, our data enabled us to test the impacts of Tier-1 and Tier-2 selective high schools separately, providing a more detailed understanding of the effects of attending these schools. Methodology, Methods, Research Instruments or Sources Used 4.Data and Measures Our data were collected through an online survey administered to high school graduates. The survey incorporated comprehensive information, including students’ demographic characteristics, family background, NCEE scores, college admissions, etc. The survey encompassed responses from 11,495 high school graduates who participated in NCEE and met the four-year undergraduate college admissions criteria. This study only focuses on students whose middle and high schools were located in the same urban district. Our analytical sample consists of 4,366 students, including 867 students from 14 Tier-1 high schools, 945 students from 23 Tier-2 schools, and 2,554 students from 80 regular schools. We focus on three main outcomes: academic performance, college admission, and self-efficacy. To assess academic performance, we use four measures, including the NCEE total score and scores for the three main compulsory subjects. We also assess the probability of students being admitted to first-class universities as a measure of college admission. Finally, we evaluate students’ self-efficacy, which is assessed through ten survey items. 5.Methods To determine the causal effects, we used a regression discontinuity design (RDD) based on the fact that students were admitted to selective high schools if their HSEE scores were above a certain threshold. Our analysis utilized a fuzzy RDD approach, given that the probability of being admitted to selective high schools did not change from 0 to 1 at the cutoff point in our data. The fuzzy RD estimator was constructed by fitting the two-stage local polynomial parameter estimation function. We use an indicator of whether a student’s HSEE score crosses the score threshold for attending a Tier-1/Tier-2 high school as an instrument for the treatment, a dummy variable indicating whether a student attends a Tier-1 or Tier-2 selective high school. We employed the local linear and quadratic functions and utilized MSE-optimal bandwidths. In two-stage regression, the same polynomial degree and bandwidth were selected. For robustness checks, we used CER-optimal bandwidths and other bandwidths suggested by Hahn et al. (2001), Imbens and Lemieux (2008), Ludwig and Miller (2007). We also employed non-parametric kernel-based local linear regression with triangular and Epanechnikov kernels. We also checked the validity of RDD. Firstly, our analyses indicated that passing the Tier-1/Tier-2 school cutoff strongly predicts attendance at a Tier-1/Tier-2 school. Secondly, the standardized HSEE score was continuous at the cutoff. Lastly, the students’ characteristics, such as gender and parents’ education years, were continuous at the cutoff. Conclusions, Expected Outcomes or Findings 6.Findings Our results showed that students with higher initial HSEE scores tend to achieve higher NCEE scores and have a greater chance of attending top universities. Nevertheless, Tier-1 schools reduced the NCEE total and math scores of students near the cutoff by about 44 and 17 points, respectively. Few estimates of the Tier-2 school effect were significantly different from zero. These results suggested that participating in selective high schools did not improve academic performance for students near the eligibility cutoff. Students admitted to Tier-1 selective high schools performed even worse academically than their Tier-2 counterparts. Moreover, attending selective high schools did not positively affect students’ college admission outcomes. Selective high schools might have a heterogeneous impact on students’ performance with different characteristics due to differences in their adaptability. Heterogeneity was only observed in the Tier-1 school effect on NCEE math scores of students with different parents’ education years and the Tier-2 school effect on NCEE math scores and first-class university admission of students with different genders. Tier-1 schools significantly reduced NCEE math scores for students whose parents had 16 or more years of education while having no significant impact on those whose parents’ education years were less than 16 years. Tier-2 schools had a significant negative effect on NCEE math scores for female students but an insignificant impact on males. Regarding the probability of attending first-class universities, Tier-2 schools had a significant negative effect on male students but an insignificant impact on females. One possible explanation for the zero to negative effects on both academic performance and college admission was that the coursework difficulty level in selective high schools may be too challenging for some students and decrease their self-efficacy, ultimately leading to unfavorable academic outcomes. However, our results indicated no impact of attending selective high schools on self-efficacy. References Anderson, Kathryn, Xue Gong, Kai Hong, and Xi Zhang. 2016. “Do Selective High Schools Improve Student Achievement? Effects of Exam Schools in China.” China Economic Review 40: 121-34. Barrow, Lisa, Lauren Sartain, and Marisa de la Torre. 2020. “Increasing Access to Selective High Schools through Place-Based Affirmative Action: Unintended Consequences.” American Economic Journal: Applied Economics 12, no. 4: 135-63. Berkowitz, Daniel, and Mark Hoekstra. 2011. “Does High School Quality Matter? Evidence from Admissions Data.” Economics of Education Review 30, no. 2: 280-88. Dee, Thomas and Xiaohuan Lan. 2015. “The Achievement and Course-Taking Effects of Magnet Schools: Regression-Discontinuity Evidence from Urban China.” Economics of Education Review 47: 128-42. Ding, Weili and Steven F. Lehrer. 2007. “Do Peers Affect Student Achievement in China’s Secondary Schools?” The Review of Economics and Statistics 89, no. 2: 300-12. Dustan, Andrew, Alain de Janvry, and Elisabeth Sadoulet. 2017. “Flourish or Fail?: The Risky Reward of Elite High School Admission in Mexico City.” Journal of Human Resources 52, no. 3: 756-99. Elsner, Benjamin and Ingo Eduard Isphording. 2017. “A Big Fish in a Small Pond: Ability Rank and Human Capital Investment.” Journal of Labor Economics 35, no. 3: 787-828. Estrada, Ricardo and Jérémie Gignoux. 2017. “Benefits to Elite Schools and the Expected Returns to Education: Evidence from Mexico City.” European Economic Review 95: 168-94. Hoekstra, Mark, Pierre Mouganie, and Yaojing Wang. 2018. “Peer Quality and the Academic Benefits to Attending Better Schools.” Journal of Labor Economics 36, no. 4: 841-84. Jackson, C. Kirabo. 2010. “Do Students Benefit from Attending Better Schools? Evidence from Rule-Based Student Assignments in Trinidad and Tobago.” The Economic Journal 120(December): 1399-429. Park, Albert, Xinzheng Shi, Chang-tai Hsieh, and Xuehui An. 2015. “Magnet High Schools and Academic Performance in China: A Regression Discontinuity Design.” Journal of Comparative Economics 43, no. 4: 825-43. Pop-Eleches, Cristian and Miguel Urquiola. 2013. “Going to a Better School: Effects and Behavioral Responses.” American Economic Review 103, no. 4: 1289-324. Shi, Ying. 2020. “Who Benefits from Selective Education? Evidence from Elite Boarding School Admissions.” Economics of Education Review 74: 101907. Wu, Jia, Xiangdong Wei, Hongliang Zhang, and Xiang Zhou. 2019. “Elite Schools, Magnet Classes, and Academic Performances: Regression-Discontinuity Evidence from China.” China Economic Review 55: 143-67. Zhang, Hongliang. 2016. “Identification of Treatment Effects under Imperfect Matching with an Application to Chinese Elite Schools.” Journal of Public Economics 142: 56-82. 11. Educational Improvement and Quality Assurance
Paper Applying Lewin’s Three-Stage Model to Pedagogical Innovation: Factors Shaping Second-Order Organisational Change in an Arab School Beit- Berl college, Israel Presenting Author:This paper examines the implementation of second-order organisational and pedagogical innovation in an Arab primary school, analysing the factors that shaped the process across Lewin’s (1951) three-stage model of change: unfreezing, changing, and refreezing. The study addresses a critical question for educational systems experiencing cultural and structural transformation: What factors promote or hinder second-order educational change in a culturally conservative school context? Topic and Research Objectives Situated within the field of educational improvement and organisational change, the study explores how deep-rooted cultural norms, teacher beliefs, leadership practices, and systemic constraints shape efforts to embed school-wide pedagogical innovation. It examines why surface-level adjustments often fail to become routine practice and how schools negotiate the shift toward inquiry-based learning, outdoor learning, and game-based learning. Focusing on a minority cultural setting provides insights relevant to marginalised educational systems across Europe and beyond, contributing to discussions on school improvement, teacher agency, cultural resistance, and the long-term sustainability of pedagogical innovation. Conceptual and Theoretical Framework The analysis is grounded in Lewin’s Three-Stage Model, which conceptualises change as a dynamic process involving unfreezing existing structures, implementing new practices, and refreezing to stabilise new norms. This model is complemented by scholarship on second-order change (Carnall & Todnem By, 2014), emphasising transformations that reshape organisational culture and pedagogical identity. Literature on educational innovation (Fullan, 2007; Nair, 2017) and organisational climate and leadership (Kaya, 2021; Klein & Sorra, 1996) further informs the analysis, highlighting interactions between individual beliefs, organisational systems, and policy environments. Methodology A qualitative case study design (Yin, 2009) enabled an in-depth examination of the school as a dynamic organisational system. Data were collected through 31 semi-structured interviews with all 28 teachers and the school principal, as well as three focus groups conducted at different points in the reform. Thematic analysis (Braun & Clarke, 2006) was employed to identify patterns relating to each stage of Lewin’s model. Codes were developed inductively and then grouped into themes reflecting the cultural, organisational, and pedagogical components of the change. Key Themes and Contribution In the unfreezing stage, traditional pedagogical beliefs and cultural expectations limited readiness for change. During the change stage, seven influential factors emerged: the depth of the innovation, parental resistance, contrasts between novice and veteran teachers, leadership style, physical building constraints, competing demands from the Ministry of Education, and the fit between innovation and subject area. In the refreezing stage, stabilisation was hindered by teachers’ concerns about losing their traditional professional identity and the lack of long-term systemic and community support. The study demonstrates that second-order reform is shaped by the interplay between cultural norms, organisational structures, and teacher identity. It highlights the need for differentiated professional support, culturally responsive leadership, and structural mechanisms to sustain pedagogical transformation. European and International Relevance The findings are highly relevant to European educational systems facing similar challenges in sustaining school-wide innovation, particularly in culturally diverse or socio-economically disadvantaged contexts. Many countries grapple with tensions between traditional teaching identities and progressive reforms and with ensuring equity in innovation adoption. By offering an empirically grounded perspective from a minority context, this study contributes to international debates on inclusive school improvement, equitable innovation, and the cultural dimensions of educational change. Methodology, Methods, Research Instruments or Sources Used This study adopted a qualitative case study design to examine a complex process of second-order pedagogical change within its authentic cultural and organisational context. The research focused on an Arab primary school undergoing a multi-year reform and explored how teachers and the school principal experienced and interpreted the change across Lewin’s (1951) three-stage model: unfreezing, change, and refreezing. The research involved 29 participants—28 teachers and the school principal—representing diverse subject areas, levels of experience, and degrees of exposure to the innovation. This comprehensive inclusion allowed for a multilayered understanding of the school’s internal dynamics during the reform. Data were collected through semi-structured interviews with all 29 participants, offering insight into personal experiences, perceived challenges, cultural expectations, and professional beliefs. Interviews were conducted in Arabic and lasted between 60 and 105 minutes. The interview guide evolved across three rounds of data collection to reflect the shifting stages of change. To enrich the individual accounts, three focus groups were conducted, enabling the identification of shared tensions, collective interpretations, and group-level reactions to the innovation. In addition, school documents, internal reports, and notes from the pedagogical mentor and organisational consultant were examined to contextualise participants’ narratives and trace structural decisions throughout the reform. Data analysis followed Braun and Clarke’s (2006) thematic analysis framework. Transcripts were reviewed repeatedly to ensure familiarity; initial codes were generated inductively; and themes were refined and mapped onto Lewin’s model, capturing patterns related to resistance, leadership practices, parental expectations, structural constraints, and professional identity. Trustworthiness was supported through triangulation across interviews, focus groups, and documents; member checking; and reflexive memo-writing. Ethical approval was obtained, confidentiality was ensured, and pseudonyms were used throughout the reporting. This methodological design provided a rich, coherent, and culturally grounded understanding of the organisational, pedagogical, and socio-cultural forces shaping second-order change in the school. Conclusions, Expected Outcomes or Findings The findings indicate that implementing second-order pedagogical innovation in a culturally conservative Arab primary school is a complex, non-linear process shaped by cultural expectations, organisational constraints, and diverse teacher identities. Using Lewin’s (1951) three-stage model, the analysis shows how both enabling and inhibiting factors influenced the reform. In the unfreezing stage, many teachers found it difficult to detach from traditional beliefs regarding authority, discipline, and structured teaching. Although some were open to innovation, others expressed uncertainty and limited readiness. The lack of early collective dialogue contributed to uneven motivation and preparedness. During the change stage, seven key factors influenced implementation: the depth and demands of the innovation, strong parental expectations for conventional schooling, differences between novice and veteran teachers, the principal’s leadership style, physical constraints of the school building, competing Ministry of Education requirements, and the degree of fit between subject areas and the promoted pedagogical approaches. These factors created tensions between innovation, cultural norms, and system-level pressures. In the refreezing stage, the innovation did not fully stabilise. Teachers feared losing their pedagogical identity, felt unsure about sustaining new practices, and reported insufficient long-term support. Without shared ownership, community alignment, and structural mechanisms for continuity, the reform remained fragile. 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