Personality traits and academic motivation as predictors of attitudes towards digital educational technologies among Russian university students

Table 5 shows that the regression models for The use of digital technologies in education predict only 6.95% of the variance in the total sample, 28.1% in the psychology students, 26.1 in medical the students, and 11.1% in the natural sciences students. Extraversion has a significant positive impact on The use of digital technologies in education in the total sample and in the psychology students. Motivation for self-respect has a significant positive impact on The use of digital technologies in education in the total sample. Neuroticism and Motivation for personal growth have a significant positive impact on The use of digital technologies in education in the medical students. Conscientiousness has a significant positive impact on The use of digital technologies in education in the natural sciences students. Achievement motivation has a significant positive impact on The use of digital technologies in education in the psychology students, but in the natural sciences students this impact is negative. Openness has a significant negative impact on The use of digital technologies in education in the psychology students. Introjected motivation has a significant negative impact on The use of digital technologies in education in the total sample and in the medical students.

Table 5

Best predictor regression models for The use of digital technologies in education

We can see more differences in the obtained regression models, both with the models obtained for the two previous indicators of attitudes to DETs and between the models obtained for different samples in this case for the use of digital technologies in education. In the total sample, only Motivation for self-respect is retained as a positive predictor, Extraversion and Amotivation (at the trend level) are added to it, and Introjected motivation is a negative predictor. In the sample of natural science students, only Achievement motivation remains as a negative predictor and Conscientiousness appears as a positive predictor (only one time). In the sample of medical students, Introjected motivation persists as a negative predictor, while Neuroticism and Motivation for personal growth first appear as positive predictors. In the sample of psychological students, there is not a single predictor that would be repeated for all the three Indicators of Attitudes towards DETs. Extraversion is a positive predictor only in two cases: for General involvement in the digital space and for The use of digital technologies in education. In the latter case, Achievement motivation is a positive predictor (as compared to the natural sciences students), and Openness appears for the first time as a significant but negative predictor in this sample (in contrast to the positive impact of this personality trait in the other samples and for the other Indicators of Attitudes towards DETs).

Thus, we partially confirmed our assumption that the scales of academic motivation have a greater impact on attitudes towards DETs among university students as compared to personality traits. However, the differences in these impacts, which we have assumed in the samples of students of different fields of study, have turned out to be even larger than we have expected. In general, the impact of personality traits is more pronounced for the psychological students' attitudes, and the impact of scales of academic motivation is more pronounced for the medical students' attitudes.

Conclusion

The purpose of this exploratory study is to consider and to compare the impacts of the academic motivation and personality traits on attitudes toward DETs among Russian university students from different fields of study (Psychology Sciences, Medical Sciences, and Natural Sciences). Summarizing the results of the study, we can draw the following conclusions.

Firstly, the regression models using the FFM personality traits and scales of academic motivation as predictors can explain the different percentage of variance in attitudes towards DETs in the different student samples: from 6.61 to 8.19% in the total sample, from 11.1 to 16.1% in the natural science students, from 10.2 to 26.1% in the medical students, and from 21.0 to 28.1% in the psychology students. We are inclined to interpret the higher percentage of explained variance in the sample of psychological students by the fact that psychology belongs to the “person-to-person” professions, for which not only professional but also personal qualities of specialists are important.

Secondly, among the scales of academic motivation, Motivation for selfrespect is most often a positive predictor of different Indicators of Attitudes towards DETs in all the studied samples. Amotivation is a negative predictor of all the studied Indicators of Attitudes towards DETs in the total sample, Achievement motivation is a negative predictor of all the studied indicators of attitudes towards DETs in the natural science students, Introjected motivation is a negative predictor of all the studied Indicators of Attitudes towards DETs in the medical students, and Intrinsic cognition motivation is a positive predictor of two of the three studied Indicators of Attitudes towards DETs in the psychology students. In this case, it should be noted that for the psychological students, unlike the other samples, only Intrinsic academic motivation (Cognition and Achievement) is a positive predictor of attitudes towards DETs.

Thirdly, among the personality traits, Openness is most often a positive predictor of general interest and involvement in digital technologies in all the samples, except for the psychological students, for whom, more often, Extraversion is a positive and Agreeableness is a negative predictor of various Indicators of Attitudes towards DETs.

The limitations of this study are due to: (1) the relatively small size of the samples and their female-to-male ratio; (2) the technique for measuring attitudes towards DETs, which needs to be improved in accordance with new data obtained in the process of online learning during the pandemic lockdowns; (3) a certain lack of prior research on personality and especially motivational predictors of university students' attitudes to DETs; therefore, it is difficult to compare our results with those obtained by other researchers and provide a more comprehensive outlook on the problem.

Accordingly, we see the prospects of the present study in overcoming these limitations, as well as in the development of psychological support programs for university students to improve the effectiveness of the use of DETs.

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Личностные черты и академическая мотивация как предикторы отношения студентов к цифровым образовательным технологиям

И.А. Новикова , П.А. Бычкова , А.Л. Новиков , Д.А. Шляхта

Аннотация. В современную эпоху цифровизации разработка и внедрение цифровых образовательных технологий (ЦОТ) находятся в центре многочисленных дискуссий педагогов, психологов, социологов, медиков и т. д. Более того, пандемия COVID-19 мгновенно сделала ЦОТ неотъемлемой частью современной общественной жизни во всем мире. Однако как до, так и во время пандемии COVID-19 относительно мало внимания уделялось изучению мотивационных и личностных характеристик студентов вузов, связанных с их отношением к ЦОТ и эффективностью использования ЦОТ в обучении. В настоящем исследовании приняли участие 173 студента (61 % - девушки) российских вузов разных направлений обучения (естественные науки, медицина, психология) в возрасте от 17 до 26 лет. Отношение студентов к ЦОТ диагностировалось с помощью авторской методики «Опросник отношения студентов вузов к ЦОТ». Учебная мотивация студентов измерялась с помощью шкал академической мотивации Т.О. Гордеевой и др. Для диагностики черт личности применялся NEO Five-Factor Inventory в русскоязычной адаптации М.В. Бирюкова и С.Д. Бодунова. Для статистического анализа использовались методы описательной статистики, ^-критерий Манна - Уитни и множественный регрессионный анализ. Результаты исследования показали, что шкалы учебной мотивации являются более значимыми предикторами отношения к ЦОТ по сравнению с личностными чертами студентов. Однако существуют особенности соотношения мотивационных и личностных предикторов ЦОТ у студентов разных направлений обучения, особенно у студентов-психологов. Выводы данного исследования свидетельствуют о том, что учет таких психологических факторов, как учебная мотивация и личностные черты студентов, может способствовать оптимальному внедрению ЦОТ в современный образовательный процесс.

Ключевые слова: цифровые образовательные технологии, отношение, цифровая компетентность, студенты, академическая мотивация, Пятифакторная модель личности, черты личности

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