Personalisation has been an object of research since the beginning of the twenty-first century (Campbell et al., 2007). Several studies suggest that personalisation increases student motivation in learning, boosting their interest in a subject (Campbell et al., 2007; Prain et al.,2013) and reducing student behaviour problems (McGuiness, 2010).

According to Campbell et al. (2017) and McLoughlin and Mark (2010), this approach involves diverse pedagogical approaches that accommodate individual needs, such as cooperative learning, mentoring, ICT, scaffolding and Experiential Learning. The learning environment exerts both direct and indirect impacts on students' well-being, with academic efficacy as a crucial mediator in this relationship (Waldrip et al., 2016).

This approach develops students' capacities, addressing their learning needs (Prain et al.,2013). It also increases productivity for both students and teachers because by understanding a student's specific needs, teachers can support students' independence by fostering self-motivation, encouraging responsibility for setting goals, and allowing flexibility in learning choices (Prain et al.,2018).  However, from a psychological perspective, students perceive learning as personalised when teachers demonstrate genuine care, understand them as individuals, and implement strategies that address their academic and socio-emotional needs (Waldrip et al., 2016).

Despite being promoted by many scholars, most personalisation theories are ambiguous (Campbell et al., 2007), and much uncertainty still exists about whether personalised learning increases students' involvement in their self-regulation. However, it evolves, requiring diverse teaching strategies that empower students to shape their curriculum and learning method (Waldrip et al., 2016). The self-regulated learning (SRL) perspective that is involved in personalisation incorporates cognitive, motivational, affective, and social factors, providing a more comprehensive understanding of student learning and motivation (as cited in Pintritch, 2004). According to Pintritch (2004), developing self-report questionnaires to assess self-regulated learning involves several conceptual and methodological challenges, particularly reading their validity, reliability, and level of detail.

Nazarbayev Intellectual School (further referred to as NIS) introduced personalised learning (personalisation) as an experimental mode of teaching and learning in NIS schools in Kazakhstan in 2019. Personalised learning is an educational model where teaching methods and academic strategies are tailored to students' needs, interests and socio-cultural backgrounds (The guidelines for organising personalised learning in NIS, 2019). personalised learning is implemented in two ways. The first one enables students to study the Subject Programs of three grades in two years, i.e. grades 8-10. It is required that students cover the program for two years and complete all the required practical, laboratory and project work within the program. Having completed the second year of this study, students pass an external summative assessment. The schedule of exams is developed individually for these students. The second variant of personalised learning requires the Individual Learning Trajectory, which involves the study of certain subjects in depth. It also includes students' accelerated learning pace. Students cover the term themes in their learning pace and the time they devote to a deepened investigation of the themes, preparing for Olympiads, or doing their project work. However, they pass the summative assessment together with the class. Another variant involves a personalised schedule for assessment, separately from the rest of the class.

This research examines the emerging role of student involvement in the self-regulation process in the context of personalisation.

The guiding research question is:

How does personalised learning shape NIS students' perceptions of their self-regulation?

Sub-Questions

1. How do NIS students perceive their self-regulation within a personalised learning environment?
2. How do factors such as environment, self-efficacy, and well-being influence students’ self-regulation in a personalised learning setting?

Research Design A non-experimental cross-sectional, correlational design was used to identify the influence of personalised learning on students’ academic efficacy, academic achievement and well-being for those involved in the program of Personalised Learning and those not, and compare and contrast their answers through a questionnaire. This design was appropriate for our study as it provided an opportunity to involve a large group of participants at one point in time (O’Dwyer & Bernaur, 2014). Participants and Sampling The target population in this research involves students of Nazarbayev Intellectual Schools in Aktobe and Nazarbayev Intellectual school of Astana. The sample included 394 students and comprised those who are/were involved in the program of Personalised Learning and students who are/were not involved in the program considering similar age. The total population of both schools combined was 1810 at the time of constructing this abstract. A non-probabilistic convenience sampling approach was used to recruit participants for this study. Although this type of sampling does not guarantee that the study’s participants will be successfully related to the population, specific characteristics that the participants have will help the research to achieve the study’s goals (Johnson & Christensen, 2020). The primary reason for selecting these particular schools as the research site was the convenience and availability as the researchers work there. Data Collection Tools An instrument Personalised Learning Environment Questionnaire (PLQ) (Waldrip et al., 2014) was used to measure students’ perceptions of the factors affecting the implementation of Personalised Learning Plans (PLPs). There are initially 66 items with 19 scales. The items in the questionnaire (e.g. “I use different materials from those used by other students”) employ a five-point Likert response scale: Strongly Disagree, Disagree, Not Sure, Agree and Strongly Disagree. However, we used an adapted version of the questionnaire with 44 items tailored to our study's purpose.

Students who followed a personalised learning plan had a slightly higher mean self-regulation score (M = 4.05, SD = 0.58) compared to those who did not (M = 3.94, SD = 0.65). There is no statistically significant difference in self-regulation scores between students who followed a personalisation and those who did not (p = 0.086). However, the Pearson correlation analysis revealed that self-regulation has the strongest correlation with student engagement (r = 0.61, p < .001) and self-efficacy (r = 0.59, p < .001). Well-being is positively associated with student engagement (r = 0.51, p < .001) and assessment (r = 0.59, p < .001), highlighting that students with higher well-being levels are more engaged and perform better in assessments. In conclusion, the moderate-to-strong correlations suggest that students' self-efficacy, well-being, and environment all play critical roles in their self-regulation and engagement. Our findings showed that students who plan with their teachers how well their learning is and what activities suit best for them tend to have higher self-regulation, which aligns with Waldrip et al.’s (2014) findings. The correlation between personalised learning and self-regulation is negative (r = -0.15, p = 0.003), indicating a small but statistically significant inverse relationship. This suggests that students who studied under a personalised learning plan tend to have slightly lower self-regulation skills compared to those who did not. This appears to be the limitation of our research, which suggests that we have to interview the students involved in personalised learning to understand how personalisation influences their self-regulation, including environment and assessment.

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