The paper substantiates and pilots a methodology for solving computational chemistry problems
within a personalized learning model. Drawing on a review of recent research and classroom practices,
we argue for a task system aligned with students’ individual learning paths. We propose a “Chemical
Kinetics” module that includes algorithms for basic calculations (reaction rate, temperature coefficient
via the Van ’t Hoff rule, and the effects of concentration and pressure based on the law of mass action)
and strategies for combined problems. The transition from qualitative to quantitative understanding of
reaction rate is shown to strengthen both disciplinary and cross cutting competencies. The method offers
selectable difficulty levels, a system of hints, and alternative mathematical strategies (power vs.
logarithmic), thus accommodating differences in pace and learning style.
A pedagogical experiment with 9th grade students used a survey to assess motivation and
satisfaction with formative assessment outcomes. According to the survey, 71% reported higher interest
in problem solving when they could choose task difficulty; 83% were satisfied with their results, with
100% pass rate and 82% quality of knowledge. Reported difficulties (time management, algebraic
transformations) indicate the need for calibrated support and autonomy.
The methodology can be embedded into lessons and extracurricular work and supports the
development of digital practice tools. The results demonstrate the practical value of personalization:
higher intrinsic motivation, informed strategy choice, and deeper understanding of kinetic regularities.
We conclude with implementation tips and outline future research on scaling, transfer to other chemistry
topics, and validation on larger samples.