A number of international large scale assessments and national studies address the question of differences in students’ achievement regarding their socio-economic status (OECD, 2018, NEC, 2014). This aspect of analysis is of a great interest for scientists and policy makers in the light of the main objective of many educational systems around the world - to provide equal learning opportunities to all students. But, as stated in the resent OECD report on equity in education, “there is no country in the world that can yet claim to have entirely eliminated socio-economic inequalities in education“(OECD, 2018, p.13).

The results of international surveys show that students from socioeconomically disadvantaged homes demonstrate lower academic achievement. However, research shows, that there is a group of disadvantaged students who achieve the same high results as their more advantaged peers (Agasisti et al., 2018). This capacity is called academic resilience. In order to develop effective policy for education quality assurance it is important to investigate the predictors of student academic resilience.

Research shows that students’ educational aspirations, socio-emotional skills, such as perseverance, and well-being can be considered predictors of academic resilience (EBPO 2018; Erberber, et al., 2015; Wills & Hofmeyr, 2019). For example OECD PISA analysis showed that student well-being is positively related to academic performance. Disadvantaged students who are socially and emotionally resilient also tend to do better academically, which implies that helping disadvantaged students develop positive attitudes and behaviours towards themselves and their education can also benefit their academic development (OECD, 2018). Some analysis showed a teacher effect: teachers’ expectations are directly and positively related to student academic resilience (Chirkina et al., 2020). Overall, studies on student resilience cover such concepts as individual psychological resilience, academic resilience, self-regulated learning, confidence (self-efficacy), co-ordination (planning), control, composure (low anxiety), commitment (persistence) (Cassidy, 2016; Martin & Marsh, 2006).

In Lithuania, academic resilience has not been addressed from an empirical data perspective. Analyses of data from national and international student surveys usually provide general findings on student achievement differences (by region, gender, SES). Analysis of National surveys of students’ achievements data show that there is positive correlation between students internal and external motivation to learn mathematics (8^th grade students), learning to learn skills (metacognitive skills, planning, self-regulation) and their achievement in mathematics. However, there is a lack of data-based insight into the variations and interactions between major divides in achievement taking in account disadvantaged students. (NEC, 2014; NEC, 2016).

As a theoretical framework for our study, the self-system model of motivational development (Connell & Wellborn, 1991; Deci & Ryan, 1985) is used. Within this model, students’ academic outcomes are shaped by contextual and intra-psychic factors through the quality of their engagement in learning. Intra-psychic factors reflect the personal motivational resources that are constructed by students’ overt time in response to the interaction between social context and their inner beliefs about the self (e.g. academic self-concept, self-efficacy) (Skinner, Furrer, Marchand & Kinderman, 2008). Students’ engagement in learning, including its emotional, cognitive, behavioural and social aspects, is the target of motivational outcomes as it proximally predicts students’ learning and performance (Skinner, Kinderman, Connell & Wellborn, 2009).

The purpose of this study is to investigate motivational resources in resilient and non-resilient students in order to better understand SES-related achievement inequalities. A hypothesis is that resilient students have higher contextual support (from parents and teachers) and their motivational resources, such as academic self-concept, self-efficacy, task value and engagement, are greater compared to non-resilient students.

In this study we analysed nationally representative data from the 2014 National Survey of Students Achievement of Lithuanian 8th grade students (N=3763; 50.5% females). The data was collected by the National Examination Center and provided in an open-access format. The participants came from 178 classes in 148 schools. Students filled in cognitive tests and answered questionnaires, which included questions about students’ home and school environment, personal characteristics, attitudes toward learning and other study-related aspects. Student SES was measured with a composite index, which covered a relatively broad range of home material and educational resources, including books, computers, separate room, home appliances, and pocket money per week. The items (k=12) were added to form an overal index and the scale was transformed to have a mean of 0.50 and a standard deviation of 0.10. Students were divided in 4 groups using quartiles of SES index. Socio-economically disadvantaged students are defined as students in the bottom quarter of the SES index. For the analysis the results of mathematics cognitive test were used (N=1702). Mathematics results in the database were presented using MTT scale (M=500, SD=100). Two groups were formed. Resilient students are defined as a group of socio-economically disadvantaged students who score at the top two quantiles of mathematics performance among all students (M=608, SD=50,3). Non-resilient students - a group of socio-economically disadvantaged students who score at the bottom two quantiles of mathematics performance among all students (M=397, SD=50,3). Three indicators of students’ motivational resources were used: self-efficacy (4 items, Cronbach’s alpha 0.76), task value (5 items, Cronbach’s alpha 0.82), beliefs about difficulties in learning (6 items, Cronbach’s alpha 0.73) and future expectation (respondents were asked to indicate what education they expect to complete in the future). Three dimensions of students’ engagement were used: emotional engagement (4 items, Cronbach’s alpha 0.78), social engagement (5 items, Cronbach’s alpha 0.74) and two indicators for cognitive engagement: meta-cognitive self-regulation (8 items, Cronbach’s alpha 0.83) and effort regulation (7 items, Cronbach’s alpha 0.82). Information about contextual support was received for two agents: teachers and parents. Four indicators were used for teacher support: autonomy support (4 items, Cronbach’s alpha 0.76), teacher interpersonal involvement (4 items, Cronbach’s alpha 0.75), feedback (5 items, Cronbach’s alpha 0.84) and teacher support-assistance (8 items, Cronbach’s alpha 0.81). Contextual support from parents was measured with 3 items (Cronbach’s alpha 0.71). For comparative analyses we used independent-samples t-test and Hedges’g criteria (for efect size).

There were no significant differences between resilient and non-resilient students according to sex, school location/urbanization level. All students’ motivational resource indicators had substantially higher scores among resilient students (self-efficacy (t(82) = -2.553, p = 0.013, effect size g =0.603) task value (t(82)= -2.520, p = 0.014, effect size g = 0.595), beliefs about difficulties in learning (t(82)= 2.189, p = 0.031, effect size g = 0.517)). Resilient students had higher future learning expectations (t(261) = 5.679, p = 0.000, effect size g = 0.722). Resilient students showed higher social engagement (t(318) =-2.279, p = 0.023, effect size g = 0.269). Regarding emotional engagement significant differences were not observed, which rise a question if this is due to the nature of a subject (math), so futher analysis would be needed, including other subjects. A substantial difference between the two groups was observed in meta-cognitive self-regulation (indicator from cognitive engagement group) (t(82) = -2.155, p =0.034, effect size g = 0.509), but no significant differences were observed in effort regulation. Contextual support from parents also showed significant differences and can be considered a correlate of academic resilience (t(322) = -3.378, p = 0.001, effect size g = 0.396 ). There were no significant differences in teacher support, which rise a need to explore this aspect more in-depth and use more specific indicators, reflecting various aspects of teaching. This study is a first step to a broader analysis on academic resilience among Lithuanian students. Subsequently such analysis will be performed with data from national surveys of 4th and 8th grade students in 2014, 2015 and 2016 covering mathematics and reading achievements.

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