Attitudes towards Digital Educational Technologies among University Students of Different Fields of Study: Role of Academic Motivation and Personality Traits

Full Text

Introduction The rapid digitalization of all aspects of human life is undoubtedly the most important trend of the 21st century, as a result of which radical transformations of society as a whole are taking place, i.e. its “digital transformation” (Vial, 2019). The COVID-19 pandemic and its consequences became a catalyst for the sharp transition of the world community, including the education sector, into the digital space. The “pros” and “cons” of digitalization of education are still one of the most controversial issues discussed not only in scientific, but also in broad public discourse during and after the pandemic (Cretu & Ho, 2023; Narbut et al., 2020; Radu et al., 2020). At the same time, many researchers believe that the use of various types of e-learning, virtual and blended learning will contribute to achieving the goals of sustainable education (Araneo, 2024; He et al., 2024). Numerous studies indicate that many of the difficulties associated with the digital transformation of education are determined not only by objective issues, for example, technical problems, but also by the psychological characteristics of educational process participants, for example, their perception and attitudes towards DETs (Aleshkovski et al., 2021; He et al., 2024; Narbut et al., 2020; Novikova et al., 2021; 2022a; 2023; Radu et al., 2020; Rizun & Strzelecki, 2020). So, S.K.M. Brika and co-authors, based on bibliometric analysis, found that “Motivation and students’ attitudes to e-learning systems” rank first among the nine most important areas of e-Learning research in higher education conducted during the COVID-19 pandemic (Brika et al., 2022). In international psychology, psychodiagnostic tools for measuring students’ attitudes towards various aspects of digitalization of education began to be developed long before the pandemic on the basis of various theoretical models (Chou, 2014; Edmunds et al., 2010; Kar et al., 2014; Rosen et al., 2013; Selwyn, 1997). Thus, well-known and frequently used tools are the Online Learning Readiness Scale (OLRS) by M.-L. Hung et al. (2010), the Media and Technology Usage and Attitudes Scale (MTUAS) by Rosen et al. (2013), as well as the Remote Learning Attitude Scale (RLAS) developed by K. Tzafilkou et al. during the COVID-19 pandemic (Tzafilkou et al., 2021). Many international studies measure university students’ and teachers’ attitudes towards information and communication technologies (ICT) using the Tripartite Model of Attitudes or ACB model (Guillén-Gámez & Mayorga-Fernández, 2020; Guillén-Gámez et al., 2020; Ordóñez & Romero, 2016; Prokop & Fančovičová, 2008; Romero Martínez et al., 2020; Svenningsson et al., 2022). For example, X.G. Ordóñez and S.J. Romero Martínez developed and validated the Scale of Attitudes Towards ICT (SATICT) in Spanish, that consists of three subscales: affective, cognitive and behavioral ones (Ordóñez & Romero, 2016). Prior to the COVID-19 pandemic, hardly no specialized instruments were created in Russia to diagnose attitudes towards digital technologies in education. We can only mention the study of attitudes towards Internet technologies by G.U. Soldatova and T.A. Nestik (2016), short and screening versions of the Digital Competence Index (DCI) by G.U. Soldatova et al. (2018), the University Students’ Attitudes towards DETs Questionnaire by I.A. Novikova et al. (Novikova et al., 2021; 2022a; 2022b), as well as a few sociological studies (Popova, 2019). After the start of the pandemic, Russian scientists conducted a large number of studies on the attitude of university students and teachers to the transition to distance learning, but most of them were based on sociological surveys (Aleshkovski et al., 2021; Narbut et al., 2020) or short questionnaires (Ismatullina & Zakharov, 2021; Nevryuev et al., 2022). Several special psychodiagnostic tools in Russian have been developed and validated in recent years to test and evaluate university students’ and teachers’ attitudes regarding the digitization of education. M.G. Sorokova et al. proposed the Scale for assessing the university digital educational environment (AUDEE Scale) (Sorokova et al., 2021) based on the Personality Attitudes Theory by the famous Russian psychologist Vladimir N. Myasishchev (1995); I.A. Novikova et al. created and psychometrically tested the Attitudes towards DETs Scale for University Students (ATDETS-US) (Novikova et al., 2023; Novikova & Bychkova, 2024) based both on Myasishchev’s Personality Attitudes Theory (1995) and the Tripartite Model of Attitude (Fabrigar et al., 2005; VandenBos, 2015). Using the tools described above, international and Russian psychologists, educators, and sociologists study the correlates and predictors of university students’ attitudes towards digital technologies in education, including in samples of university students from different fields of study. Z.D. Abdullah et al. (2015) examined the difference between Arts and Science students’ attitude towards IT at Koya University in Iraq based on Tripartite Model of Attitudes. As expected, the study revealed more positive attitude towards IT in Science students, however, significant differences were shown only for the behavioral component of attitude (Abdullah et al., 2015). These findings are generally consistent with the results obtained by G. Vladova and her colleagues in a study of the influence of the COVID-19 pandemic on student acceptance of technology-mediated teaching in Germany in 2020. In general, Music and Arts students have more negative perceptions of almost all technology-mediated teaching models compared with Information Systems students (Vladova et al., 2021). I.A. Novikova and P.A. Bychkova compared the attitudes towards the DETs (ATDETs) among Russian university students in psychological, medical and natural science fields of study before the start of the COVID-19 pandemic using the University Students’ Attitudes toward DET Questionnaire. The study showed that Natural Science students have a better ATDETs, while Medical students have a worse ATDETs than students from other fields of study (Novikova & Bychkova, 2020). In studies concerning the personality aspects of students’ ATDETs, the FiveFactor Model (FFM) of personality traits in various modifications is widely used (Audet et al., 2021; Baruth & Cohen, 2022; Belinskaya & Fedorova, 2020; Bhagat et al., 2019; Cohen & Baruth, 2017; Fırat, 2022; Keller & Karau, 2013; Mustafa et al., 2022; Peng & Dutta, 2023; Quigley et al., 2022; Rivers, 2021; Rivers, 2022; Staller et al., 2021). The motivational characteristics of students in the context of digitalization of education are often considered within the framework of Deci and Ryan’s Self-Determination Theory (SDT) (Al-Said, 2023; Audet et al., 2021; Bovermann et al., 2018; Gustiani, 2020; Rasskazova & Soldatova, 2022; Rosli et al., 2022; Staller et al., 2021). S. Mustafa et al. studied the role of the Big Five personality traits in Chinese university students’ satisfaction with online teaching modes during COVID-19 pandemic and their adoption intentions towards online teaching modes (Mustafa et al., 2022). The results showed that students’ satisfaction with online teaching modes is negatively impacted by extraversion, but positively by agreeableness, conscientiousness, neuroticism, and openness. Students’ future adoption intentions of online teaching modes are negatively impacted by openness, but positively by agreeableness, extraversion, and neuroticism. Generally, agreeableness is the most significant, and conscientiousness is the least important factor for students to adopt online teaching modes (Mustafa et al., 2022). O. Baruth and A. Cohen explored the relation between personality traits (using Costa & McCrae’s FFM) and Israeli university students’ satisfaction with online learning activities offered by Techno-Pedagogical Learning Solutions (TPLS) (Baruth & Cohen, 2022). All five personality traits and satisfaction with a number of TPLS features were shown to be significantly correlated in the study’s findings. Cluster analysis revealed that, in contrast to students in the “non-neurotic” group, those in the “neurotic” group showed low satisfaction with all TPLS (Baruth & Cohen, 2022). É.C. Audet et al. (2021) studied role of the Big 5 personality traits in Canadian university students’ adaptation to online learning, measured by their quality of motivation, subjective well-being, self-efficacy, online engagement, and online satisfaction. Results showed that conscientiousness and openness to experience were associated with higher self-efficacy and with different forms of autonomous motivation for online learning. Only openness to experience was strongly related to engagement with online learning and higher levels of subjective well-being (Audet et al., 2021). K. Bovermann et al. (2018) investigated academic motivation in relation to online learning readiness and attitude towards gaming in gamified online learning among undergraduate students in Germany. Significant positive correlations were found between students’ online learning readiness in the dimension of technical competencies and both types of autonomous motivation (identified and intrinsic motivation). At the same times students who indicated rather low online learning readiness tended to show non-autonomous motivation (amotivation) (Bovermann et al., 2018). I.A. Novikova et al. (2022b) using the University Students’ Attitudes toward DET Questionnaire found that the scales of academic motivation have a greater impact on ATDETs among Russian university students as compared to personality traits. Nonetheless, these impacts are specific to students studying natural sciences, medicine, and psychology. Among the scales of academic motivation, motivation for self-respect is a positive predictor of ATDETs indicators in all the studied samples, but amotivation is a negative predictor of all ATDETs indicators in the total sample. 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 ATDETs (Novikova & Bychkova, 2022; Novikova et al., 2022b). Our analysis of research on various factors of university students’ ATDETs allows us to argue that an important direction of this kind of research is may be to identify the relationship of ATDETs indicators with personality traits and academic motivation while taking into account the students’ field of study. It is necessary to emphasize that many of the studies described above compared university students not only from different fields of study, but also from different universities, years of study, age and gender, which could have an impact on the findings. Therefore, it seems to us very important to select for the research samples of students who are as equivalent as possible in all of the listed factors except the field of study in order to obtain the most valid conclusions. The purpose of present study is to identify differences both in the ATDETs and its components based on the Tripartite Model of Attitudes, and in the correlation of personality traits and academic motivation with the ATDETs between university students of different fields of study (using the example of Psychology students and Philology students). Based on previous studies of university students’ ATDETs, we assume that there are differences between Psychology students and Philology students in: (1) their ATDETs and its components; (2) the correlation of personality traits and academic motivation with the ATDETs and its components; and (3) in regression models for indicators of ATDETs and its components with personality traits and types of academic motivation as predictors. Materials and Methods Participants A total of 362 students (90.05% females and 9.95% males) from RUDN University took part in the research, aged 16 to 22 years (Mage = 18.27 ± 0.89). All participants are first-year undergraduate students of the Faculty of Philology, major in different fields of study: - 199 Psychology students (89.95% females and 10.05% males), aged 16 to 22 years (Mage = 18.41 ± 0.95); - 163 Philology students (90.18% females and 9.82% males), aged 16 to 22 years (Mage = 18.09 ± 0.78); All the students participated in the study during classes in psychological or philological disciplines as one of the additional tasks, for which they received additional points. Data collection took place from February 2022 to January 2024 via Google Forms. Techniques To diagnose students’ attitudes towards DETs, the Attitudes towards DETs Scale for University Students (ATDETS-US) by I.A. Novikova et al. (2023) was used. ATDETS-US contains 36 items grouped into three subscales (12 items each) in accordance with the Tripartite Model of Attitudes (ACB model): - Emotional Subscale (ES) aimed at determining students’ emotions and feelings in relation to DETs (α = 0.90; ω = 0.89); - Cognitive Subscale (CS) aimed at determining students’ perceptions and knowledge regarding the DETs (α = 0.89; ω = 0.88); - Behavioral Subscale (BS) aimed at assessing how students master digital devices and technologies in the process of studying at a university (α = 0.83; ω = 0.82); - The Total Indicator of the ATDETS-US (α = 0.95; ω = 0.86), reflecting the general attitude of university students toward digital technologies in education. Participants rate their agreement with scale items using a 5-point Likert scale (from 1 = strongly disagree to 5 = strongly agree), accordingly, raw scores for each subscale can range from 12 to 60 points, and for the total indicator raw scores can range from 36 to 180 points. Instructions, text and key to the scale are provided in the Appendix in English and Russian. The FFM personality traits were measured using the Russian version of NEOFFI adapted by S. Biryukov and M. Bodunov (Biryukov & Vasilev, 1997; Bodunov & Biryukov, 1989; Costa & McCrae, 1992). This version of NEO-FFI consists of 60 statements to which the respondent expresses the degree of consent by 5-point Likert scale (from 1 = strongly disagree to 5 = strongly agree). The raw scores for each of the Five-Factor personality traits (Neuroticism, Extraversion, Openness, Conscientiousness, and Agreeableness) can range from 12 to 60 points. To determine the motivation of students’ educational activity, the “Academic motivation scales” (AMS) questionnaire by T.O. Gordeeva et al. based on Deci and Ryan’s Self-Determination Theory was used (Gordeeva et al., 2014). This questionnaire allowed us to diagnose seven different types of educational motives of students: three types of intrinsic motivation (Intrinsic cognition, Achievement, and Personal growth), three types of extrinsic motivation (Motivation for selfrespect, Introjected, and External regulation) and an Amotivation. AMS consists of 28 direct statements to which the subject expresses the degree of consent on a 5-point Likert scale (from 1 = strongly disagree to 5 = strongly agree). Each of the academic motivation scales includes 4 statements, the raw scores can range from 1 to 20 points (Gordeeva et al., 2014). Data Analysis Most of the studied variables (except Neuroticism) have a non-normal distribution according to the Shapiro-Wilk test. In this regard, nonparametric methods were chosen for correlation and comparative analyses that correspond to this type of data. The descriptive statistics methods, Spearman’s rank correlation analysis with Holm corrections, Mann-Whitney’ U-test for independent samples, Fisher F-test, and multiple regression analysis were used for statistical analysis. Regression analysis was performed by using the method of “backward” stepwise search. Independent variables were personality traits (five NEO-FFI subscales) and academic motivation (seven AMS scales); dependent variables were indicators of students’ attitudes towards DETs (ES, CS, BS, and the Total Indicator of the ATDETS-US). In the first step, full regression models with personality traits as possible predictors of each indicator of students’ attitudes towards DETs were constructed for the total sample and separately for Psychology and Philology students. In the second step, all AMS scales were added to the regression models as possible predictors. At the third step, the regression models obtained in the first and second steps were compared and the ΔR² indicator was calculated. The next step was to analyze all input models by removing the least significant predictors. For further analysis, models with the highest information load and the smallest number of predictors (“best predictor model”) were selected. Statistical processing was carried out in the R software environment for statistical computing and graphics, version 4.4.0 (2024-04-24 ucrt) - “Puppy Cup” (R Core Team, 2023; Revelle & Condon, 2019; The jamovi project, 2022). Results Table 1 presents the results of descriptive statistics (means and standard deviations) in total sample and subsamples of Psychology and Philology students as well as the analysis of differences between all variables studied using the U-test in Psychology and Philology students. Table 1 shows that there is only one significant difference between Psychology and Philology students: Openness is higher among philologists. Table 1 Means (M), standard deviations (SD) and Mann-Whitney’ Utest between study variables in Psychology and Philology students Psychology Philology Mann- plevel Total Sample students students Whitney’ Variables (N = 362) (N = 199) (N = 163) UTest M SD M SD M SD Attitudes towards DETs Scale for University Students Emotional Subscale 52.77 7.76 52.76 7.93 52.78 7.57 16581 0.713 Cognitive Subscale 50.59 7.03 50.93 7.24 50.17 6.77 17473 0.205 Behavioral Subscale 47.90 7.10 48.31 7.02 47.40 7.18 17415 0.227 ATDETS-US 151.25 19.15 152.00 19.65 150.34 18.54 17310.5 0.270 NEO-FFI Factors Neuroticism 35.93 6.99 35.76 6.78 36.13 7.25 15783 0.660 Extraversion 39.91 7.62 40.26 7.21 39.49 8.10 17312 0.269 Openness 41.97 5.13 40.95 4.88 43.22 5.17 11900.5 0.001 Agreeableness 41.34 6.35 40.82 6.51 41.96 6.12 14699.5 0.125 Conscientiousness 41.98 7.77 41.65 7.65 42.37 7.92 15178.5 0.294 Academic Motivation Scales Intrinsic cognition motivation 16.62 3.18 16.61 3.15 16.64 3.22 16069 0.879 Achievement motivation 15.00 3.68 14.98 3.58 15.02 3.80 15830.5 0.694 Motivation for personal growth 16.15 3.40 16.23 3.40 16.06 3.39 16804 0.551 Motivation for self-respect 15.04 3.75 14.95 3.89 15.15 3.57 15871 0.725 Introjected motivation 12.55 3.76 12.65 3.75 12.43 3.78 16553.5 0.735 External regulation 11.33 3.62 11.36 3.71 11.29 3.53 16213 0.996 Amotivation 7.71 3.72 7.63 3.79 7.80 3.64 15458.5 0.437 Tables 2-4 present Spearman’s correlations between the ATDETS-US subscales and total indicator, personality traits, and AMS scales in total sample and subsamples of Psychology and Philology students. The Tables 2-4 confirm, first of all, the close positive associations between the ATDETS-US subscales and it total indicator, as well as the associations between both some personality traits and academic motivation scales, which generally correspond to the theoretical foundations of the techniques used for measurement. Based on the purpose of our research, the greatest interest for analysis is the correlation between indicators of ATDETs and their personality traits on the one hand and types of academic motivation on the other. Figures 1 and 2 visualize these correlations. Figure 1 shows that in the total sample, all personality traits with the exception of Neuroticism are positively associated with indicators of students’ ATDETs; three types of internal motivation (Intrinsic cognition motivation, Achievement motivation, Motivation for personal growth) and one type of external motivation (Motivation for self-respect) are positively related to most of this indicators, and Amotivation is negatively related to all indicators of students’ ATDETs. At the same time, two types of external motivation (Introjected motivation and External regulation External regulation) have no correlations with indicators of students’ ATDETs. Figure 2 shows that both in the subsample of Psychology students and Philology students, two types of internal motivation (Intrinsic cognition motivation and Motivation for personal growth) are positively related to the majority of indicators of students’ ATDETs, and Amotivation is negatively related to all these indicators, which generally corresponds to the nature of the correlations in the total sample. However, the nature of the correlations of indicators of students’ ATDETs with personality traits is dominated by differences in the compared subsamples: Agreeableness and Conscientiousness have more correlations with these indicators in Psychology students, and Extraversion and Openness - in Philology students. Figure 1. Graphical representation of the correlations between the variables studied in the total sample of university students (N = 362) Note: the solid lines - positive correlations; the dashed lines - negative correlations; ES - Emotional Subscale; CS - Cognitive Subscale; BS - Behavioral Subscale; N - Neuroticism; E - Extraversion; O - Openness; A - Agreeableness; C - Conscientiousness; MCI - Intrinsic cognition motivation; MA - Achievement motivation; MP - Motivation for personal growth; MS - Motivation for self-respect; MI - Introjected motivation; ME - External regulation; AM - Amotivation. Source: created by Polina A. Bychkova using the MS PowerPoint Figure 2. Graphical representation of the correlations between the variables studied in in Psychology and Philology students Note: the solid lines - positive correlations; the dashed lines - negative correlations; ES - Emotional Subscale; CS - Cognitive Subscale; BS - Behavioral Subscale; N - Neuroticism; E - Extraversion; O - Openness; A - Agreeableness; C - Conscientiousness; MCI - Intrinsic cognition motivation; MA - Achievement motivation; MP - Motivation for personal growth; MS - Motivation for self-respect; MI - Introjected motivation; ME - External regulation; AM - Amotivation. Source: created by Polina A. Bychkova using the MS PowerPoint The results of the multiple regression analysis (best predictor models) are presented in Tables 5-8. Multiple correlation coefficients between dependent variables (indicators of students’ attitudes towards DETs, i.e., the ATDETS-US subscales and it total indicator) and predictors (personality traits and academic motivation scales) for all models are statistically valid according to the Fisher F-test, which confirms that there is a significant impact of some personality traits and/or types of academic motivation on ATDETs indicators in Psychology and Philology university students. The adjusted determination coefficients (R2adj) obtained for the regression models are not very high and vary from 13 to 22%. However, taking into account the fact that students’ ATDETs depend on many factors other than personality traits and academic motivation, these results can be considered quite satisfactory. Table 5 shows that the best predictor model for Emotional Subscale of ATDETS-US predicts 13.8% of the variance in the total sample, 14.6% in Psychology students and 15.7% in Philology students. None of the significant predictors coincided in the three analyzed samples. Extraversion and Openness (with positive impact), and achievement motivation (with negative impact) are significant predictors in the total sample and subsample of philologists, Agreeableness with positive impact is significant predictor in the total sample and subsample of psychologists. Motivation for personal growth is positive significant predictor only in the total sample, Amotivation is negative significant predictor only in the Psychology subsample, and Intrinsic cognition motivation is positive predictor only in the Philology subsample at the level of statistical trend (p = 0.089). It can be noted that the significant predictors of the emotional component of the students’ ATDETs do not coincide in the subsamples of Psychology and Philology students, while more similarities were found between the total sample and the subsample of philologists. Table 5 Best predictor regression models for Emotional Subscale of ATDETSUS Sample/Variable Summary of Model Coefficients R2adj F pValue Estimate Std. Error tValue pValue Total sample (N = 362) 0,138 12,5 < .001 (Intercept) 23.941 3.9782 6.02 < .001 Extraversion 0.139 0.0522 2.66 0.008 Openness 0.293 0.079 3.71 < .001 Agreeableness 0.222 0.0611 3.63 < .001 Achievement motivation -0.439 0.1642 -2.67 0.008 Motivation for personal growth Psychology students (N = 199) (Intercept) 0.520 0.18 2.89 0.004 0.146 17.9 < .001 40.519 3.6589 11.07 < .001 Agreeableness 0.378 0.081 4.67 < .001 Amotivation -0.416 0.139 -2.99 0.003 Philology students (N = 163) 0.157 8.52 < .001 (Intercept) 24.94 5.0697 4.92 < .001 Extraversion 0.196 0.0702 2.79 0.006 Openness 0.437 0.1129 3.87 < .001 Intrinsic cognition motivation 0.504 0.2944 1.71 0.089 Achievement motivation -0.477 0.2461 -1.94 0.055 Best predictor regression models for Cognitive Subscale of ATDETSUS Table 6 Summary of Model Coefficients Sample/Variable 2 R adj F pValue Estimate Std. Error tValue pValue Total sample (N = 362) (Intercept) 0.22 18 < .001 19.052 3.4861 5.47 < .001 Openness 0.231 0.0688 3.36 < .001 Agreeableness 0.176 0.0536 3.28 0.001 Conscientiousness 0.143 0.048 2.98 0.003 Intrinsic cognition motivation 0.473 0.1848 2.56 0.011 Achievement motivation -0.425 0.1532 -2.77 0.006 Motivation for personal growth 0.438 0.1794 2.44 0.015 Psychology students (N = 199) (Intercept) 0.294 12.8 < .001 8.273 5.6477 1.46 0.145 Openness 0.213 0.0973 2.19 0.03 Agreeableness 0.326 0.0706 4.61 < .001 Conscientiousness 0.166 0.0637 2.61 0.01 Intrinsic cognition motivation 0.487 0.2334 2.09 0.038 Achievement motivation -0.327 0.1947 -1.68 0.094 Motivation for personal growth 0.46 0.2217 2.08 0.039 External regulation 0.268 0.1296 2.07 0.04 Philology students (N = 163) 0.184 19.3 < .001 (Intercept) 25.611 4.2261 6.06 < .001 Openness 0.341 0.0982 3.47 < .001 Intrinsic cognition motivation 0.591 0.158 3.74 < .001 Table 6 shows that the best predictor model for Cognitive Subscale of ATDETS-US predicts 22.0% of the variance in the total sample, 29.4% in Psychology students and 18.4% in Philology students. Openness and Intrinsic cognition motivation are significant positive predictors in all samples. Agreeableness, Conscientiousness, Intrinsic cognition motivation and Motivation for personal growth (with positive impact), and Achievement motivation (with negative impact) are significant predictors in the total sample and subsample of psychologists. External regulation is positive significant predictor only in the Psychology subsample. In this case, two significant positive predictors of the cognitive component of the students’ ATDETs coincide in the subsamples of psychologists and philologists, however, in the subsample of psychologists there are more significant predictors, which shows similarity with the total sample. Table 7 Best predictor regression models for Behavioral Subscale of ATDETSUS Sample/Variable Summary of Model Coefficients R2adj F pValue Estimate Std. Error tValue pValue Total sample (N = 362) 0.151 22.3 < .001 (Intercept) 26.68 2.8729 9.29 < .001 Agreeableness 0.17 0.0548 3.1 0.002 Conscientiousness 0.124 0.0492 2.52 0.012 Motivation for personal growth 0.556 0.1122 4.95 < .001 Psychology students (N = 199) 0.188 16.3 < .001 (Intercept) 22.887 3.7239 6.15 < .001 Agreeableness 0.267 0.0698 3.82 < .001 Conscientiousness 0.168 0.0642 2.62 0.009 Motivation for personal growth 0.463 0.1434 3.23 0.001 Philology students (N = 163) 0.169 12 < .001 (Intercept) 22.525 4.7569 4.74 < .001 Extraversion 0.149 0.0658 2.27 0.025 Openness 0.197 0.1063 1.86 0.065 Intrinsic cognition motivation 0.628 0.1716 3.66 < .001 Table 7 shows that the best predictor model for Behavioral Subscale of ATDETS-US predicts 15.1% of the variance in the total sample, 18.8% in Psychology students and 16.9% in Philology students. None of the significant predictors coincided in the three analyzed samples, however, in this case, significant positive predictors (Agreeableness, Conscientiousness, and Motivation for personal growth) completely coincide in the total sample and the subsample of psychologists. Respectively, Extraversion, Openness, and Intrinsic cognition motivation are positive predictors only in the Philology subsample. Table 8 shows that the best predictor model for the Total Indicator of ATDETS-US predicts 21.4% of the variance in the total sample, 25.6% in Psychology students and 20.6% in Philology students. Openness is significant positive predictor in all samples. Agreeableness, Conscientiousness, and Motivation for personal growth are significant positive predictors in the total sample and subsample of psychologists. Intrinsic cognition motivation is positive predictor in the total sample and in the Philology subsample. Achievement motivation is negative significant predictor only in the total sample. Table 8 Best predictor regression models for the Total Indicators of ATDETSUS Sample/Variable 2 R adj F pValue Estimate Std. Error tValue pValue Total sample (N = 362) 0.214 17.4 < .001 (Intercept) 64.945 9.536 6.81 < .001 Openness 0.667 0.188 3.55 < .001 Agreeableness 0.521 0.147 3.55 < .001 Conscientiousness 0.430 0.131 3.28 0.001 Intrinsic cognition motivation 0.968 0.505 1.91 0.056 Achievement motivation -1.241 0.419 -2.96 0.003 Motivation for personal growth 1.316 0.491 2.68 0.008 Psychology students (N = 199) 0.256 18.1 < .001 (Intercept) 55.17 12.998 4.24 < .001 Openness 0.492 0.266 1.85 0.066 Agreeableness 0.974 0.188 5.17 < .001 Conscientiousness 0.443 0.173 2.56 0.011 Motivation for personal growth 1.139 0.41 2.78 0.006 Philology students (N = 163) 0.206 15 < .001 (Intercept) 72.72 12.007 6.06 < .001 Extraversion 0.387 0.166 2.33 0.021 Openness 0.961 0.268 3.58 < .001 Intrinsic cognition motivation 1.251 0.433 2.89 0.004 Discussion The purpose of present exploratory study is to identify differences both in the ATDETs and its components based on the Tripartite Model of Attitudes, and in the correlation of personality traits and academic motivation with the ATDETs between Psychology and Philology university students. Summarizing the results of the study, we must state the assumptions we put forward were only partially confirmed. Firstly, our study did not reveal significant differences in the ATDETs and its components between Psychology and Philology university students. This contradicts the findings of most previous studies that compared the attitudes towards DETs between university students from different fields of study (Abdullah et al., 2015; Novikova & Bychkova, 2020; Vladova et al., 2021). We believe that the predominance of similarities in ATDETs in the subsamples we compared is explained by the fact that these students study not only at the same university, but also at the same faculty, and are also first-year students studying many of the same general education disciplines. The similarity between the studied subsamples of university students was also manifested in the absence of significant differences between them in the severity of types of academic motivation and personality traits (except for Openness). Secondly, present study shows that the nature of correlations between ATDETs and its components with types of academic motivation are dominated by similarities among the Psychologist and Philologists subsamples, while in the correlations between these attitudes and personality traits, more differences were revealed between the studied subsamples. In both subsamples, two types of internal motivation (Intrinsic cognition motivation and Motivation for personal growth) are most closely positively related to indicators of ATDETs and its components, and Amotivation is negatively related to these indicators. It is important to note that of the ATDETs components in both samples, the cognitive and behavioral components are more closely related to the types of academic motivation. Accordingly, both Psychology students and Philology students, who are more driven to learn and develop personally throughout the educational process, are better aware and comprehend the capabilities of DETs, have better command of digital devices and technologies in the process of studying at the university, and have a better ATDETs in general. At the same time, amotivated students of both subsamples, on the contrary, have a much worse ATDETs and the possibilities of their use in the educational process. This nature of correlations is fully consistent with the generally accepted provisions on the important role of intrinsic motivation in the learning process (in which DETs are one of the possible means), as well as with the data of previous studies (Al-Said, 2023; Bovermann et al., 2018; Gustiani, 2020; Novikova & Bychkova, 2022; Novikova et al., 2022b). Among the personality traits, Agreeableness and Conscientiousness are positively associated with ATDETs in a subsample of Psychology students, and Extraversion and Openness are positively associated with these attitudes in a subsample of Philology students. In our opinion, these correlations reflect the specifics of students’ training in the compared fields of study: (1) the most important professionally significant qualities for psychologists are empathy and focus on other people (aspects of Agreeableness), and Conscientiousness is necessary for Psychology students when they mastering natural science and mathematical disciplines, including using DETs; (2) general activity, including verbal activity, is important for Philology students for analyzing various texts and discussing them in classes (Extraversion), and Openness (cognitive activity, curiosity, creativity, etc.) is the only personality trait for which differences were identified in our study when comparing subsamples (significantly higher among Philology students). Overall, these findings are consistent with previous research that has shown differences in both the expression of personality traits and their correlations with attitudes toward DETs (Belinskaya & Fedorova, 2020; Bhagat et al., 2019; Cohen & Baruth, 2017; Fırat, 2022; Keller & Karau, 2013; Novikova et al., 2022b; Peng & Dutta, 2023; Quigley et al., 2022; Rivers, 2021; Rivers, 2022; Staller et al., 2021). However, a more detailed comparison is difficult due to the fact that different research studied students from different forms of study, years of study, fields of study, as well as from different countries and universities. Thirdly, this study revealed that both some personality traits and some types of academic motivation are significant predictors of the students’ ATDETs and could explain from 13 to 22% of its variance. Regression analysis confirmed that the most universal predictors with a positive impact of the ATDETs and its components are Agreeableness, Conscientiousness and Motivation for personal growth in the subsample of psychologists, and Openness, Extraversion and Intrinsic cognition motivation in the subsample of philologists. It is important to note that Neuroticism and extrinsic academic motivation were not significant predictors of the ATDETs in both subsamples. As we noted above, the identified significant correlations and especially significant predictors in both students’ subsamples emphasize the specifics of student learning in the fields of study under consideration. Thus, it can be argued that the differences between Psychology students and Philology students are more pronounced not in the absolute expression of personality traits (except for Openness), types of academic motivation, and the ATDETs, but in the associations of these variables to each other in each of the subsamples. In general, our results that different personality traits and different types of academic motivation can be significant predictors of the attitudes towards DETs among university students of different fields of study correspond to the conclusions of previous research (Al-Said, 2023; Keller & Karau, 2013; Novikova et al., 2022b; Quigley et al., 2022), however, a more detailed comparison is difficult, since previous studies usually used other diagnostic tools and compared students from other fields of study. There are several limitations to our study that should be taken into account when evaluating its results and conducting future research in this area. Firstly, the main limitation is female-to-male ratio in the sample in which female students predominate, but this is consistent with the gender distribution in university students’ populations in Psychology and Philology. Secondly, the limitation is that university students from only two fields of study, only first-year students, and only students from one University were studied, but in this research this helped to equalize the subsamples in terms of basic socio-demographic indicators. Thirdly, the possible limitation is the measure used to collect the data. The university students ATDETs can be measured with various tools, the most popular being self-questionnaires. However, there is a need to use more objective methods, for example, experts’ and peers’ assessments. The next limitation is a certain lack of prior research studies on ATDETs predictors based on the Tripartite Model of Attitudes in university students in Russian psychology, so it is difficult to compare our results with other researchers’ data. Accordingly, summing up all of the findings and limitations of our research, the prospects for further research are associated with balancing the samples by the female-to-male ratio and expanding its by students from different Russian universities, different fields, degrees, forms, and years of study and with the use of additional methods for students’ attitudes towards DETs measurement (for example, expert assessment by teachers and classmates). Thus, despite some limitations, results of the present research confirm the need to take into account the psychological features of students when implementing and applying digital technologies in education. Based on the results of the study, we plan to develop recommendations for psychological support for Psychology students and Philology students to improve and optimize their attitude towards digital technologies, which will inevitably be increasingly used in education in the future.

About the authors

Irina A. Novikova

RUDN University

Author for correspondence.
Email: novikova_ia@pfur.ru
ORCID iD: 0000-0001-5831-1547
SPIN-code: 7717-2834
Scopus Author ID: 35766733000
ResearcherId: Q-5276-2016

Ph.D. in Psychology, Associate Professor, is Associate Professor at the Psychology and Pedagogics Department

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Polina A. Bychkova

RUDN University

Email: bychkova_pa@pfur.ru
ORCID iD: 0000-0002-6526-7262
SPIN-code: 1819-7877
Scopus Author ID: 57222720667
ResearcherId: ACD-4333-2022

Ph.D. in Psychology, is Assistant at the Psychology and Pedagogics Department

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Dmitriy A. Shlyakhta

RUDN University

Email: shlyakhta_da@pfur.ru
SPIN-code: 6172-5460
Scopus Author ID: 57191998066

Ph.D. in Psychology, Associate Professor, is Associate Professor at the Psychology and Pedagogics Department

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Alexey L. Novikov

RUDN University

Email: novikov_al@pfur.ru
ORCID iD: 0000-0003-3482-5070
SPIN-code: 3416-1350
Scopus Author ID: 56005222400
ResearcherId: Q-5419-2016

Ph.D. in Philology, Associate Professor, is Associate Professor at the General and Russian Linguistics Department

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

References

Supplementary files