Personalized learning represents a pedagogical approach aimed at adapting instruction to learners’ individual educational needs, learning styles, and prior knowledge. Contemporary educational research emphasizes that personalization requires flexibility in content, learning processes, and assessment, enabling learners to follow individual learning trajectories (Tomlinson & Rose, 2011). The increasing availability of learning analytics and adaptive technologies has made such personalization more feasible by allowing the modelling of optimal learning pathways for diverse learner profiles (Luckin et al., 2016).

The theoretical framework of this study is grounded in constructivist learning theory, formative assessment theory, and models of adaptive and differentiated instruction. Shepard (2000) identifies formative assessment as a core mechanism for supporting learning development, as it provides continuous feedback that informs instructional adjustments and supports learner self-regulation. Reigeluth (2013) further argues that instruction should be aligned with learners’ cognitive structures and information-processing strategies, a principle that is especially relevant in cognitively demanding subjects such as programming. Digital learning environments play a critical enabling role, as they can process large volumes of learner data and generate adaptive tasks (Anderson, 2008). Recent studies also demonstrate that machine learning algorithms can anticipate learning difficulties and recommend appropriate instructional materials, thereby increasing the precision of personalized learning (Zhang et al., 2020).

Programming education is a particularly suitable context for personalized learning due to its abstract, algorithmic, and problem-solving nature. Papert (1980) emphasizes that learners construct knowledge most effectively when actively engaged in meaningful problem-solving rather than following rigid instructional scripts. Hattie and Yates (2014) similarly show that problem-based and project-based learning approaches significantly deepen learners’ understanding of complex concepts. However, previous research also highlights challenges in implementing personalized learning, including limited resources, insufficient teacher preparation, and resistance to pedagogical change (Spector, 2019; Tomlinson, 2014).

This study addresses the following research questions:

1. How does personalized programming instruction influence students’ academic achievement and algorithmic thinking in lower secondary education?
2. How does personalized learning affect student engagement and motivation in programming lessons?
3. What organizational and pedagogical challenges emerge when implementing personalized instruction in school contexts?

The objectives of the study are to systematize key didactic conditions for personalized learning in programming education, design and implement a personalized instructional model, empirically evaluate its effects on student achievement and engagement, and develop evidence-based recommendations for teachers and schools

The study employed a mixed-methods research design consisting of a diagnostic-analytical stage and an experimental stage. During the diagnostic stage, a survey was conducted with 158 students from Grades 7–10, 166 computer science teachers, and representatives of educational authorities in the Zhambyl region. The survey examined learner interest in programming, the availability of personalized tasks, and perceived institutional barriers. The results indicated strong student interest and a demand for differentiated instruction, alongside systemic challenges such as limited methodological resources and insufficient digital infrastructure. These findings informed the design of the personalized instructional model. The experimental stage was conducted over one academic term in two parallel Grade 8 classes. One class served as the experimental group and was taught using the personalized learning model, while the other class served as a control group and followed traditional instruction. The personalized model included initial diagnostics of prior knowledge and learning preferences, differentiated tasks at three levels of difficulty (A–B–C), and active learning strategies such as PRIMM, project-based learning, pair programming, and elements of gamification. Digital educational platforms were used to support adaptive task delivery and feedback. Quantitative data were collected through pre-tests and post-tests, analysis of practical tasks and mini-projects, and summative assessments. Statistical analysis included percentage gain analysis and Student’s t-test. Qualitative data were collected through questionnaires, classroom observations, and semi-structured teacher interviews and analyzed using thematic coding. The integration of quantitative and qualitative methods enabled a comprehensive evaluation of the effectiveness of personalized instruction

The results demonstrate significant benefits of personalized programming instruction. Students in the experimental group improved their post-test scores by an average of 18%, compared to a 6% increase in the control group. The experimental group also showed stronger performance in practical programming tasks, higher-quality mini-projects, and more developed algorithmic thinking and problem-solving skills. Qualitative findings indicate increased learner engagement and motivation. Classroom observations and survey data reveal an approximately 20% increase in student engagement. Students reported sustained interest in programming and a preference for tasks adapted to their individual ability levels and interests. Teachers reported improved lesson quality, increased learner autonomy, and deeper conceptual understanding. At the same time, teachers identified organizational challenges related to limited methodological and technical resources, highlighting the importance of institutional support. Overall, the findings confirm that personalized learning enhances both academic achievement and the quality of students’ learning experiences. «This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant № AP 26193791)».

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