Research question:

How does aligning instructional materials with students’ individual information-perception types (VARK learning styles) influence their ability to analyze educational information and improve learning achievements in high-school chemistry?

Objective:

To determine the influence of the correspondence of the form of presentation of educational information to the individual type of its perception by students (according to VARK) on the development of skills for analyzing educational information and improving academic achievement in the study of chemistry.

Theoretical Framework of the Study

Personalized learning, based on individual learning styles, has become a key focus of modern pedagogy. According to modern learning models, personalization involves adapting the content, presentation, and methods of learning to the individual characteristics of students, including their preferred learning style (Tomlinson, 2021; Pane, 2020).

Research in cognitive psychology shows that students differ in the ways they process information, which influences the speed and depth of learning (Fleming & Baume, 2020). Considering learning modalities - visual, auditory, reading/writing, and kinesthetic - remains a common tool for adapting learning materials. Although some research criticizes rigid classifications of learning styles, current literature confirms that students have preferences in processing information, and adapting materials to these preferences can enhance engagement and the quality of learning (Cuevas, 2023; Leutner et al., 2022).

According to research by Deborah & Kaminski (2021), traditional instruction that fails to account for differences in learning styles reduces engagement and limits the abilities of certain groups of students. Their findings demonstrate that tailoring the delivery of materials to students' preferences increases interest and engagement in the learning process.

Modern research by Chimmalgi (2022), conducted in the field of anatomy teaching, shows that matching the format of a learning resource to a student's learning style (e.g., using interactive visuals for visual learners) improves academic performance and understanding.

Additional evidence is provided by research by Nzesei (2023), which reveals a positive correlation between preferred learning styles and academic outcomes, making personalization an important tool for enhancing educational achievement. Despite these positive findings, contemporary empirical research (Wilkinson, 2021; Newton & Miah, 2020) suggests that the impact of learning style on academic success may be moderate or context-dependent. For example, Wilkinson's (2021) study found no consistent relationship between learning style and academic achievement in medical students, suggesting that effective learning depends less on the match between style and method than on the quality of instructional design.

This emphasizes that personalized learning should be considered more broadly than simply matching "style" and "resource." Personalization also includes the pace of learning, level of support, types of activities, and the depth of cognitive load (Walkington, 2020).

In the context of school chemistry, the quality of students' initial information processing is particularly important. High school students successfully master practical elements but struggle with analytical and explanatory tasks. This is due to the insufficient development of skills in working with text, tables, and graphs, as well as limited consideration of individual differences in the perception of educational materials.

The implementation of personalized approaches based on the perception of educational resources contributes to:

improved learning quality - by adapting materials to students' cognitive characteristics;

increased engagement - as students receive information in a format convenient for them;

development of metacognition - students recognize their preferences and choose the most effective strategies;

professional development of teachers - educators are provided with tools for targeted instructional adjustments;

the development of individual educational trajectories that align with global trends in the digital transformation of education.

This study focused on a personalized learning system based on each student's perception of educational resources, assessing its impact on developing information analysis skills and improving academic performance. An initial survey using VARK methods and J. Bruner's questionnaire was administered to 37 11th-grade students at the Nazarbayev Intellectual School of Chemistry and Biology. The survey included 16 test statements, for which students selected one of four possible answers. There was no time limit for completing the task, and the results were interpreted collaboratively with the teacher. To gain a deeper understanding of how students perceive educational material, semi-structured interviews were conducted with 15-20 students and focus groups of 6-8 participants were held to discuss information perception preferences and suggestions for adapting educational resources. During the experiment, students were provided with information in various formats (visual, auditory, textual, and practical) according to their individual preferences. Observations were conducted during 10–12 chemistry lessons to assess students' engagement, independence, and ability to analyze information. The effectiveness of personalized learning was assessed through a comparative analysis of academic performance before and after the implementation of adapted learning resources. Results from chemistry quizzes and topic-based tests were used as indicators. Descriptive and inferential statistics, including the Student t-test for independent samples, were used to statistically analyze the data, allowing for reliable conclusions about the impact of the personalized approach on the educational process.

Organization of Personalized Learning Students were divided into four groups according to their VARK results. During chemistry lessons, they received learning materials tailored to their perception type: visual learners used diagrams, charts, maps, and flowcharts; auditory learners engaged with lectures, discussions, and oral explanations; read/write learners studied texts, manuals, notes, and instructions; kinesthetic learners worked with practical tasks, demonstrations, simulations, and case studies. Academic Performance Comparison of results before and after implementing personalized learning showed a clear improvement. The average chemistry grade increased from 4.2 to 4.6 (9.5% growth). The number of “5” grades rose from 2 to 10, while “4” and “3” grades decreased by 4 each. Positive trends were also observed in other science subjects: physics increased from 40.05 to 41.06 points, biology from 53.44 to 53.94 points, and chemistry from 40.08 to 43.2 points, the largest gain among the three. Engagement and Openness to Change Students’ engagement improved significantly: 85% reported increased interest in lessons, 70% became more active in classroom activities, and 90% in focus groups positively evaluated the changes implemented based on their feedback. Conclusion The study demonstrates that identifying students’ individual perception types and providing tailored learning resources supports advancement along personalized educational trajectories. Teachers should diversify and offer choices of resources to foster both innate and less-developed comprehension skills. Previous research (Koppenol-Gonzalez, Bouwmeester & Vermunt) indicates that students rarely switch between verbal and visual information processing over time, which may limit learning activities and reduce cognitive motivation. Differentiating students by perception type and allowing independent selection of materials increases engagement, motivation, and overall knowledge quality. The study highlights the importance of designing educational resources for school science education with careful consideration of content, structure, and presentation methods according to students’ learning needs and outcomes.

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