The focus of the study is to search for the impact of quality of teaching on academically resilient students’ learning outcomes. In the literature, this specific student population, with characteristics that pose challenges for teachers, is identified. Many researchers from different fields (e.g., psychology, economics, sociology) have examined the academically resilient students using different approaches and criteria for their identification related to their different scientific background and special interests (e.g., Agasisti & Longobardi, 2014; Cefai, 2004; Martin & Marsh, 2006). Specifically, this study focuses on students from low socioeconomic background who achieve high learning outcomes at school. In this sense, it is expected to contribute to equity issues in education and determine how and why these specific student population overcome its socioeconomic disparities and achieve to close the performance gap between high and low socioeconomic status (SES) students.  

Previous research on academically resilient students from low socioeconomic background revealed a broad set of factors that contribute positively to academic resilience, for example a positive school climate (Agasisti & Longobardi, 2014), the educational aspirations of the student (Sandoval-Hernández & Bialowolski 2016), and students’ sense of belonging at school (Pitzer & Skinner, 2017). Also, a significant association has been observed between socioeconomic characteristics such as whether the student’s mother lives at home (Gómez, Valenzuela, & Sotomayor, 2015), and the availability of cultural possessions at home (Borman & Overman 2004). Although previous studies on academic resilience recognize the positive role of school and teacher on academic resilience, still they don’t provide to any specific suggestions on how to support these students effectively into the classroom based on a specific theoretical scheme. Hence, this study will examine academic resilience by using the theory of the Dynamic Model of Educational Effectiveness (Kyriakides & Creemers, 2008).

Educational Effectiveness Research (EER) is the field that attempt to connect research in teacher behavior, school organization, and educational policy with student learning outcomes (Creemers, Kyriakides, & Sammons, 2010). Considering findings in relation to teacher effectiveness factors (Kyriakides & Creemers, 2008; Hattie, 2002; Seidel & Shavelson, 2007) it seems that teacher related factors have a greater impact on student outcomes compared to school related factors. This conclusion referred to general student populations. Teacher effectiveness research (TER) has not yet examined factors that are relevant to specific student populations. This study aims to move a step forward by examining, the impact of teacher related factors, included in the dynamic model on a specific student population, namely the academically resilient students. This study is also taking into consideration one of the main assumptions of the dynamic model that teaching, and learning are seen as dynamic processes that are constantly adapting to changing needs (Creemers & Kyriakides, 2010). In this sense, it is attempted to approach academic resilience as a dynamic process in which academic resilience status may change (by comparing their status at the beginning and at the end of the school year) and examine how teacher factors affect those changes.

In this context, the following research questions were formulated:

1. Do teacher factors, included in the dynamic model of effectiveness, have any impact on academically resilient students’ outcomes, and if so, which of these factors explain variation of academically resilient students in each subject (language and mathematics)?
2. Are there teacher factors which have differential effects on academically resilient students’ outcomes?
3. Can changes in the academic resilience status of the students be predicted by the teacher factors of the dynamic model?
4. Do different methods on identifying academically resilient students affect the depiction of teacher factors effects?

This study started during 2018-2019 school year and was planned to follow a sample of primary school students of grade 4, 5, and 6 in Cyprus for two consecutive school years. However, due to the pandemic COVID-19 no measurements were possible to be conducted during 2019-2020 school year. Thus, a replication study of year 1 was conducted during 2020-2021 school year to investigate the extent to which similar findings are identified by considering the data of each phase of the study separately. A sample of 29 classrooms from 13 primary schools in Cyprus was participated in our research in 2018-2019, examining language and mathematics outcomes at the beginning and at the end of the school year. Students’ SES data were collected through a questionnaire (N=489). For investigating teachers’ effects, observations were conducted focusing on the eight factors of the Dynamic Model (i.e., orientation, structuring, questioning, teaching-modelling, application, management of time, teacher role in creating a learning environment, and classroom assessment) measured by five dimensions (i.e., frequency, stage, focus, quality, and differentiation) (Kyriakides & Creemers, 2008). A sample of 66 classrooms from 14 primary schools (N=802 students) has participated during the second phase of the study. All the tools used in this study were validated in previous research projects for justifying the validity of the Dynamic Model (Kyriakides, et al., 2020). Regarding the analysis of the data, firstly, there will be an attempt to identify the low SES students of our sample. Alternative criteria for identifying low SES students will be tested (e.g., SES score or highest parental occupation score), examining also whether the difference between the statistical power of the two samples may affect our results or not. Our next challenge is to determine a benchmark of students’ performance to identify the low SES students who performed “high” and define who is resilient or not. In next, two types of analysis will be conducted: multilevel logistic regression modeling to predict which teaching factors of the dynamic model explain variation of the academically resilient students’ outcomes, and a more dynamic type of analysis, the discriminant factor analysis (DFA) to find out whether changes in the academic resilience status of the students (Stable resilient, Improved, Declined, Stable non resilient) can be explained by the teaching factors of the dynamic model.

Two methods were used to identify resilient students. We first treated parental occupation status as the indicator of measuring SES. The second method made use of the Rasch score that emerged by analyzing students’ responses to a questionnaire measuring SES. A statistically significant overlap in using these two methods to identify resilient students was identified. However, it was decided to conduct separate analyses (per method) and compare their results. These two methods were used to identify resilient students at the beginning and end of the school year. Multilevel logistic regression analysis was conducted to find out if there is a significant variation at class/teacher level in the percentage of students who managed to be resilient. We then searched for the impact of teacher factors on explaining variation at class level in relation to the percentage of students who managed to be resilient at the end of each school year. Comparable results emerged from the two analyses which reveal the importance of teacher factors in promoting not only quality but also equity in education. Finally, the discriminant function analysis was conducted to find out whether teacher factors can explain changes in the resilient status of students from the beginning to the end of the school year. The DFA was conducted by considering the data of each phase of the study separately. Implications of findings are drawn. Findings of this study provide support not only to the generic nature of the teacher factors of the dynamic model but also to the impact that these factors may have on promoting equity in education. We also argue for using a dynamic approach in measuring the impact of teacher factors. Teacher factors are expected to be more relevant to changes in the status of resilient students. Implications for research, policy and practice are finally drawn.

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