Pedagogical technology for reducing smoking among students using data technologies

Abstract

Smoking remains a serious problem among students, negatively affecting health and academic performance. The development of effective pedagogical prevention technologies using data analytics seems to be a promising area of research. The purpose of the work is to theoretically substantiate and experimentally test the pedagogical technology of reducing smoking among students based on data-analytical methods. The research was based on a set of theoretical (analysis, synthesis, modeling) and empirical methods (survey, testing, experiment). The empirical base included 450 students from 5 universities in Moscow. Data on smoking patterns, motivation to quit, and individual psychological characteristics of respondents were collected. The experimental testing of the developed technology was carried out for 6 months. A pedagogical technology integrating data mining methods, personalized coaching and gamification is proposed. The technology allows students to segment by smoking patterns, predict risks and target preventive effects. In the experimental groups, the number of smokers decreased by 24%, and the average number of cigarettes smoked by 36%. There has been a steady increase in motivation to quit smoking. Discussion. The results obtained are of high practical importance, opening up the possibility for scaling technology in universities. The theoretical value is associated with the expansion of scientific ideas about the potential of data analytics in the pedagogical prevention of addictions. It is advisable to direct further research to improve the accuracy of predictive algorithms and strengthen the gaming component of the technology.