Personality Traits and Academic Motivation as Predictors of Attitudes towards Digital Educational Technologies among Russian University Students

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Introduction It is generally recognized that modern society has entered the “digital era”, which is a transition from analog to digital format of working with information. The “digital era” is characterized by the total dominance of digital technologies based on special methods of encoding and transmitting information using a discrete cybernetic system, which allows solving many diverse tasks in the shortest possible time intervals and in almost all areas of human activity (Khangeldieva, 2018). T.N. Gorbunova and A.N. Leontiev emphasize that technologies that defined the Fourth Industrial Revolution (Deep Learning, Big data, the Internet of things, self-regulating factories, 3D printing and printed electronics) are leading the world to significant transformations, actively influencing all spheres of life, including education. (Gorbunova, Leontiev, 2021). At the same time, these authors note that education is one of the last major sectors of public life that has entered a period of radical digital transformation (Gorbunova, Leontiev, 2021). The digital transformation of education involves not only the digitization of all educational materials to compose appropriate public knowledge bases (i.e. "digitizing"), but also the maximum transfer of the educational process to the global network and the use of mobile and cloud technologies for its arrangement, the involvement of web 3.0 technologies, artificial intelligence and intelligent systems in the management of the education, the widespread use of massive open online courses (MOOCs), etc. (Strekalova, 2019). According to L.V. Baeva et al. (2020), the development of e-learning and digital education in Russia over the past decades has lagged behind many other countries. Thus, large Open Universities have not yet appeared in Russia, similar to those where students have been taught remotely in Europe, Asia and Africa for many years; and online platforms in Russian higher education have been used in accordance with the Federal State Educational Standards only as an addition to traditional forms of education. Before the pandemic, the greatest success of digital education in Russia was associated with the creation of national educational open resources, and after 2018, with the active introduction of MOOCs in some universities. However, the practice of replacing traditional forms of education with distance learning has caused numerous discussions in the educational community, since not all disciplines turned out to be meaningfully and methodically adaptable to the online format (Baeva et al., 2020). The beginning of the COVID-19 pandemic and the “stay-at-home” restrictions have dramatically accelerated the digitalization of education in the world and in Russia. This situation has become a challenge to which the education system has responded by jumping to a new level of development (Baeva et al., 2020; Krouglov, 2021; Narbut et al., 2020; Novikova et al., 2022). Based on numerous studies conducted during the pandemic in different countries, most experts are inclined to believe that the changes introduced will no longer allow the education system to fully return to its previous track after the pandemic: most likely, there will be enrichment with new forms of education in blended and/or fully digital format (Baeva et al., 2020; Narbut et al., 2020; Aleshkovski et al., 2021; Li, 2022; Novikova et al., 2022). It should be noted that, if, before the pandemic, the need and expediency of introducing digital technologies into education were actively discussed in the scientific and educational community, which was due, in particular, to the specifics of teaching the “digital generation”, now the focus of discussions has shifted. The experience gained during the pandemic in the digitalization of the educational process allowed all its participants to see its “pros” and “cons” and make sure that the effectiveness of modern education depends not only and not so much on the technical aspects of introducing digital technologies as such, but on the qualifications of teachers, lecturers and professors, on the digital competence of educators and students, as well as on the attitude towards the use of digital educational technologies (DETs) in general. In this article, we will consider different factors associated with student’s attitudes towards DETs before and during the pandemic, analyze the personality features associated with these attitudes, and also present the results of our research on personality traits and academic motivation of university students in connection with their attitudes towards DETs. Further, we consider DETs in a broad sense, which includes: (1) digital (electronic) educational materials (e-books, e-tutorials, multimedia presentations, achievement tests, quizzes, etc.); (2) digital educational resources (electronic databases, e-library systems, search systems, etc.); (3) digital educational systems (LMS, Moodle, etc.); (4) digital platforms used for training (ZOOM, MS Teams, etc.); and (5) artificial intelligence and digital (virtual) educational environment (Novikova et al., 2022). Research on attitudes towards digital educational technologies before and during the pandemic Studies conducted by international and Russian scientists before the pandemic show that the readiness and effectiveness of using digital technologies in higher education may depend on the attitude both university students and teachers towards them. Several studies on the impact of social media on learning, conducted at universities around the world in 2015-2020, showed that the students who took social media-enabled courses found them a convenient and quality-oriented addition to their traditional courses; they interacted more effectively with their course-mates, were better self-organized and more actively engaged in learning-related dialogue than the students in non-social media courses (Duncan, Barczyk, 2016; De Martino et al., 2020; Peruta, Shields, 2017; Ellefsen, 2016; Tugrul, 2017). In 2017-2020, studies among university students in different countries (Bulgaria, Spain, Cyprus, UAE, etc.) consistently revealed their positive attitudes towards the use of digital technologies in education and online learning. In most cases, it was confirmed that the fact that the students who had more experience and skills in working with ICT was positively associated with their attitudes towards DETs. However, these attitudes could also be mediated by the students’ gender, age, form of education (full- or part-time), field of study, etc. (Peytcheva-Forsyth et al., 2018; Romero Martínez et al., 2020; Guillen-Gamez et al., 2020; Ozdamli, 2017; Andrew et al., 2018). In Russia, much less research of this kind was carried out before the COVID-19 pandemic. An example was an online survey of undergraduate and postgraduate students of a number of Russian universities conducted by sociologists of the Ural State University of Economics in January 2019. The results of the survey showed low estimates of the importance of distance and online learning among the respondents: only about 50% of them noted the need for distance learning and online courses, and only 30% were open to blended learning (Popova, 2019). This could probably be explained by the fact that until recently, as mentioned above, the development of digital education in Russia lagged far behind many other countries (Baeva et al., 2020). Thus, in studies performed before the pandemic outbreak, it was shown that the positive attitudes of university students towards DETs might be associated with the activity of using digital technologies in general, involvement in communication with other users in the digital space, as well as the experience in distance learning using digital technologies (De Martino et al., 2020; Guillen-Gamez et al., 2020; Peytcheva-Forsyth et al., 2018; Soldatova, Nestik, 2016; Romero Martínez et al., 2020). However, as a rule, in the studies of that time, digital technologies were considered only as an addition to traditional forms of education. With the onset of the COVID-19 pandemic, the digitalization of education has become, perhaps, one of the most discussed issues around the world. A large number of international studies conducted in 2020-2021 were devoted to the analysis of the problems associated with the rapid and forced transition to online education (Ahern, López-Medina, 2021; Corell-Almuzara et al., 2021). Many of these studies were focused on various aspects of university students’ using DETs during the pandemic, e.g.: students’ transition to distance learning; their attitudes, expectations and views in relation to the transition to online learning; the impact of the pandemic on the learning process; etc. (Rizun, Strzelecki, 2020; Chaturvedi et al., 2021; Radu et al., 2020; Gonçalves et al., 2020; Martha et al., 2021; Bakhov et al., 2021; Drozdikova-Zaripova et al., 2021; Li, 2022; Yasmin, 2022). During this period in Russia, similar problems began to be studied in great detail. For example, a large-scale sociological survey entitled “The opinion of students of Russian universities on forced distance learning” was conducted in May-June 2020, covering 31,423 university students in all regions of the Russian Federation (Aleshkovskiy et al., 2020). This research showed that Russian university students named the convenience of working at home and travel time saving as the main advantages of distance learning, whereas the lack of personal communication and social contacts as its main disadvantages. Almost half of the respondents noted that they were not ready for the complete transfer of education to a remote format after the end of the pandemic (Aleshkovskiy et al., 2020). It can be noted that at the beginning of the pandemic, most university students around the world similarly assessed their experience of switching to fully distance learning, its disadvantages (e.g., task overload, difficulties with self-organization and motivation, numerous technical problems, lack of digital competence of students and lecturers) and advantages (e.g., time saving, opportunities to learn anywhere or develop new competences). At the same time, the majority of students reacted negatively to the prospect of continuing education only in a digital (distance) format after the lockdown due to the pandemic. At different stages of the spread of the pandemic, there appeared studies that compared the attitudes of university students towards digital learning. For example, according to S. Unger and W.R. Meiran, 51.4% of undergraduate students at a US University said they had less anxiety about online learning after a three-week experience (Unger, Meiran, 2020). A longitudinal study of German university students conducted by G. Vladova et al. (2021) showed that the students’ attitudes towards fully digital learning changed for the worse during the semester (the first wave of COVID-19), especially declining in its last month among music and art students compared to information systems students. The second stage of the study “The opinion of students of Russian universities on forced distance learning” in February 2021 covered 32,358 students and revealed that the proportion of those students who had believed that the remote format worsened the quality of education significantly decreased (from 43.7% to 30.6%), and 34.6% of students (against 27.7% at the first stage) stated that distance learning did not affect the quality of education (Aleshkovski et al., 2021). A comparison of the two waves of this survey shows that the first and mostly negative impressions of distance learning among Russian university students were changed by more balanced estimates and recognition of the positive aspects and possibilities of remote learning in the extreme conditions of the pandemic (Aleshkovski et al., 2021). In our previous research (Novikova et al., 2021b), we had the opportunity to compare Russian university students’ attitudes towards DETs before (in February-early March 2020) and after (late May-early June 2020) the start of the pandemic lockdown at three Moscow universities. We found that the students majoring in various disciplines (Medicine, Psychology and Natural Sciences) generally positively assessed the e-learning experience gained in spring 2020. We also found, as expected, the increase of students’ involvement in the digital environment and in the use of DETs; however, their digital competence had not changed much over this period. The most pronounced positive shift in attitudes towards DETs was among the medical students, while before the pandemic and the transition to distance learning they were more reserved about DETs than the students of psychology and natural sciences (Novikova et al., 2021b). Subsequently, we conducted another cross-section of this study in January 2021, but only with the psychology students (Novikova et al., 2022). A comparison of the three stages of our study showed that the number of the students who positively assessed the impact of digital technologies on the educational process decreased with increasing experience in distance learning, although self-reported attitudes towards DETs remained relatively stable. Summarizing the results of a qualitative analysis of the results of international and Russian studies conducted during the period of the forced transition to digital learning in 2020-2022, we should note that university students name technical issues among the main disadvantages of using DETs, while time saving and convenience are among the most frequently mentioned advantages of DETs (Novikova et al., 2022). Thus, studies on university students’ attitudes towards DETs for the last three years in connection with the pandemic, for obvious reasons, have mainly been devoted to changes in these attitudes and their relationships with the socio-demographic characteristics of students, such as country, academic discipline, year of study, etc. Recently, there have been practically no studies of the relationships between students’ attitudes towards DETs and their personality traits; therefore, we will turn to earlier studies. Personality features as factors of attitudes towards digital technologies in the educational context T.A. Nestik and colleagues studied the individual attitudes to new (digital) technologies in general and showed that when persons adopting new technologies, the key factor is their involvement in communication with other users (Nestik et al., 2017). In addition, it was revealed that the respondents' social representations about the future of digital technologies are ambivalent: on the one hand, they recognize the inevitability of technological development, on the other hand, the respondents associate not only comfort, but also social degradation with it. In this study the predictors of "techno-optimism" were trust to the stakeholders of technological progress, future orientation, belief in the reward for efforts, as well as low religiosity and low level of respect for authorities. At the same time, the attitude towards scientific and technological progress and orientation towards the future in general play a less significant role in the use of new technologies than the orientation towards enjoyment in the present, the attractiveness of technology and ease of it use (Nestik et al., 2017). E.P. Belinskaya and N.V. Fedorova (2020) emphasized that the impact of students’ personality traits on the different aspects of the educational process is a fairly popular topic in contemporary science, but much less research (especially in Russian psychology) is devoted to the influence of personality traits on the patterns of remote or online education. Only relatively recently, US researchers D. Gray and A. DiLoreto (2016) developed a comprehensive model of factors affecting the students’ perception of the effectiveness of online learning and satisfaction with it. The leading role in these processes turned out to be played by personality traits considered on the basis of the Five-Factor Model (FFM), and the students’ engagement was a mediator between situational factors and estimates of the effectiveness of online learning. K.K. Bhagat and colleagues (Bhagat et al., 2019) showed that from FFM personality traits only conscientiousness and openness to new experiences have a significant positive effect on students’ perception of online learning, while neuroticism, on the contrary, negatively affects students' expectations. The involvement of students in the learning process is also one of the traditional indicators of the success of any education; therefore, many researchers in online and distance education focus on studying it (Belinskaya, Fedorova, 2020; Gray, DiLoreto, 2016). M.D. Dixson (2010) noted that discussions on online course forums, online discussion of applied problems and laboratory work, group research and projects contributed to increasing the involvement of students in online learning. It was also found that students completing distance courses, firstly, pursued higher educational goals and, secondly, devoted more time per week to their education than full-time students did (Simonson et al., 2019). E.P. Belinskaya and N.V. Fedorova (2020) conducted a study on personality factors in the effectiveness of online learning among Russian students of online courses, who were divided into two groups: those who completed few (0-1) and many (10-70) such courses. To analyze the results, the authors used the following questionnaires and techniques: (1) The Russian version of M. Berzonsky’s Identity Style Inventory (Belinskaya, Bronin, 2014) for measuring three styles of identity: informational, normative, and diffuse (Berzonsky, 1989; Berzonsky, Kuk, 2000). M. Berzonsky and his colleagues established that the informational style contributed to faster mastering of the education program and good academic performance; the normative style prevented susceptibility to certain educational topics; and the diffuse style was associated with lower academic performance (Berzonsky, 1992; Kerpelman et al., 2008); (2) The Short Portrait Big Five Questionnaire (BF-10) in Russian adaptation (Egorova, Parshikova, 2016); (3) The Student Learningand Satisfactionin Online Learning Environments Instrument (SLS-OLE) (Gray, DiLoreto, 2016), which recorded the student involvement, performance and satisfaction with online learning; and (4) The Commitment Scale was used, which demonstrated the propensity of individuals to defend their opinions about themselves: a low score on this scale indicated a tendency to frequently change them (Belinskaya, Bronin, 2014). This study was conducted using online forms, and the respondents were asked to rate any online course they had recently taken. As a result, it was found that ‘Neuroticism’ was negatively associated with involvement in the learning process; ‘Extraversion’ and ‘Agreeableness’ were positively associated with online learning satisfaction; ‘Commitment’ was positively associated with involvement, satisfaction, performance, and perceptions of the future impact of learning online. ‘Openness’ to new experience and the informational style of identity positively correlated with the students’ perceptions of the impact of knowledge on the future, while the diffuse style of identity, on the contrary, negatively correlated with these perceptions. Based on these findings, the authors emphasized that, in the context of online learning, it was important that teachers contribute to the formation of horizontal connections between students, providing them with technical tools, organizational opportunities and reasons for such communication (Belinskaya, Fedorova, 2020). It should be noted that in the above study, in addition to identifying the role of students’ personality traits in online learning, data of numerous studies on the positive role of students’ interaction and involvement in the distance learning process as factors of a positive attitude towards the use of DETs were confirmed. At the previous stages of our study, which we mentioned above, we analyzed not only the changes in attitudes towards DETs during pandemic, but also their correlations with the FFM personality traits, academic motivation and academic achievements among Russian university students (Novikova et al., 2021a; Novikova, Bychkova, 2022). The findings of this study showed that university students with more pronounced ‘Extraversion’, ‘Openness’ and intrinsic academic motivation were generally more involved in the digital space and in the use of DETs, while less motivated students, in contrast, were less involved in the digital space and in the use of DETs (Novikova et al., 2021a; Novikova, Bychkova, 2022). At the same time, more successful students tended to be more involved in the digital space in general (Novikova, Bychkova, 2022). However, there was a specificity of these correlations among university students from different fields of study: for students of natural sciences and psychology, the attitudes towards DETs were more closely related to personality traits and academic motivation than for medical students (Novikova et al., 2021a; Novikova, Bychkova, 2022). The purpose of the present study is to consider and to compare the impact of academic motivation and personality traits on the attitudes towards DETs among Russian university students of fields of study. Based on our previous studies, we assume that academic motivation has a greater impact on the attitudes towards DETs than personality traits; however, there is a specificity of these impacts among students of different fields of study. Methods Participants. A total of 173 (105 females and 67 males) university students, aged 17 to 26 (Mage = 18.67 years) took part in the research. All of them were first- and second-year students of three large Moscow universities (Peoples’ Friendship University of Russia (RUDN University), National University of Science and Technology (NUST) MISiS, and Pirogov Russian National Research Medical University (RNIMU University). The students represent different departments and, accordingly, different fields of study, namely: 1) Psychological Sciences: 48 second-year students of RUDN University (39 females and 9 males), aged 18 to 26 years (Mage = 20.07 years); 2) Medical Sciences: 62 first-year students of Pirogov RNIMU University (49 females and 13 males), aged 17 to 20 years (Mage = 18.23 years); 3) Natural Sciences - 63 first-year students of NUST MISiS (17 females and 45 males), aged 17 to 21 years (Mage = 18.00 years). The research was conducted in February - early March 2020, before the lockdown in Russia due to the coronavirus pandemic. All the students participated in the study during classes in psychological disciplines, as one of the additional tasks, for which they received additional points. They were duly informed that participation would be free and voluntary. Techniques. In accordance with the purpose and hypothesis of the study, we used the following three diagnostic tools. 1. To diagnose students’ attitudes towards DETs, the authors’ questionnaire was used. We developed the questionnaire based on the analysis of the results of previous studies on the use of digital technologies in education (Soldatova, Rasskazova, 2018; Yuzefovich, 2018). We used Cronbach's alpha and MacDonald's omega coefficients and factor analysis for psychometric verification of the structure and internal consistency of this questionnaire (Bychkova, 2020). The final version of The University Students’ Attitudes toward DET Questionnaire includes 21 questions and 4 indicators (some items can fall on two or three indicators): 1) "General involvement in the use of DETs" indicator characterizes the general interest in DETs (12 items, the raw scores can range from 2 to 39 points); 2) "Involvement in the digital space" indicator reflects the activity of using digital technologies in general, not only for educational purposes (8 items, the raw scores can range from 2 to 27 points); 3) "The use of digital technologies in education" indicator more specifically reflects the attitudes to digital technologies in the educational process (8 items, the raw scores can range from 0 to 24 points); 4) "Digital competence" indicator (4 tasks, the raw scores can range from 0 to 12 points). This indicator is additional, it does not reflect the "attitude" to digital technologies, but rather the knowledge and possession of certain rules of behavior in the digital space. The final version of the check for the consistency of the authors’ questionnaire did not include tasks from this indicator (Bychkova, 2020). But in further studies, we used this indicator as a control one for more information (Novikova, Bychkova, 2022; Novikova et al., 2021, 2022). 2. The FFM personality traits were measured using the Russian version of (NEO-FFI) adapted by S. Biryukov and M. Bodunov (Biryukov, Vasilev, 1997; Bodunov, Biryukov, 1989; Costa, McCrae, 1992). The Russian version of NEO-FFI consists of 60 statements (direct and inverse) to which the respondent expresses the degree of consent by 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”). The values for each of the Five-Factor scales (neuroticism, extraversion, openness, conscientiousness, and agreeableness) range from 12 to 60 points. 3. To determine the motivation of students' educational activity, the “Academic motivation scales” (AMS) questionnaire by T.O. Gordeeva et al. was used (Gordeeva et al., 2014). This questionnaire allowed us diagnosing motivation allows to analyze seven qualitatively 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 self-respect, introjected, and external regulation) and an amotivation. This questionnaire 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 (Intrinsic cognition motivation, Achievement motivation, Motivation for personal growth, Motivation for self-respect, Introjected motivation, External regulation, Amotivation) contains 4 statements, the raw scores can range from 1 to 20 points (Gordeeva et al., 2014). Statistical Analysis. Statistical analysis was carried out using the descriptive statistics methods, Mann-Whitney U test, Spearman correlation analysis, and multiple regression 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 ("General involvement in the use of DET", "Involvement in the digital space", and "The use of digital technologies in education). In the first step, full regression models with all possible predictors of three indicators of students’ attitudes towards DETs were constructed for the total sample and separately for the male and female students. The next step was to analyze all the input models by searching for all possible combinations of the predictors and evaluating the information contribution of each set using the Akaike information criterion (AIC). For further analysis, models having the highest information load for the smallest number of predictors (“a best predictor model”) were selected for further analysis. Statistical processing was carried out in the R software environment for statistical computing and graphics, version 4.1.1 (R Core Team 2021; Revelle 2019; The Jamovi Project 2021; Epskamp et al. 2012). Results and discussion Table 1 presents the results of descriptive statistics (means and standard deviations) of all the variables studied. Table 1 Means and Standard Deviations (SD) of all Study Variables, raw scores Variables Total sample (N = 173) Natural sciences students (N = 63) Medical students (N = 62) Psychology students (N = 48) Mean SD Mean SD Mean SD Mean SD Indicators of Attitudes toward DET General involvement in the use of DET 21.66 4.03 21.62 4.23 21.42 3.83 22.04 4.06 Involvement in the digital space 15.41 3.42 15.17 3.58 15.56 3.22 15.52 3.52 The use of digital technologies in education 13.28 2.99 14.08 2.40 11.73 2.94 14.25 2.97 Digital competence 9.78 2.23 10.00 2.13 10.40 1.68 8.69 2.59 NEO FFI Factors Neuroticism 34.25 7.31 32.90 7.99 36.55 6.60 33.04 6.62 Extraversion 39.92 7.23 39.11 8.23 40.27 6.97 40.54 6.11 Openness 39.82 5.60 38.97 5.89 40.08 5.65 40.58 5.08 Agreeableness 40.80 5.96 39.46 5.88 42.13 6.24 40.83 5.41 Conscientiousness 42.87 7.52 42.51 7.56 42.47 7.77 43.85 7.21 Academic motivation scales Intrinsic cognition motivation 16.17 3.07 15.67 3.16 16.89 3.13 15.90 2.76 Achievement motivation 14.21 3.60 14.29 3.92 14.29 3.53 14.00 3.30 Motivation for personal growth 15.45 3.03 14.71 3.59 16.29 2.63 15.31 2.42 Motivation for self-respect 13.86 4.14 12.59 4.76 15.05 3.67 13.98 3.37 Introjected motivation 11.79 4.11 10.6 4.35 12.48 4.16 12.46 3.38 External regulation 9.97 3.96 9.48 4.05 10.05 3.97 10.52 3.82 Amotivation 6.87 3.11 7.48 3.23 5.71 2.27 7.58 3.48 Pairwise comparison of the studied variables between students’ subgroups using the Mann-Whitney U test showed as follows: 1) Among the personality traits, neuroticism was significantly higher in the medical students than in the psychology and natural science students, whereas agreeableness was significantly higher in the medical students than in the natural science students; 2) According to the scales of academic motivation, the medical students had higher intrinsic cognition and personal growth motivation than the psychology and natural science students; the medical students had higher motivation for self-respect and introjected motivation than the natural science students; the medical students had lower amotivation than the psychology and natural science students; the psychology students had higher introjected motivation than the natural science students; 3) Among the indicators of attitudes towards DETs, “The use of digital technologies in education” was significantly lower in the medical students than in the psychology and natural science students, whereas “Digital competence” was significantly lower in the psychology students than in the medical and natural science students. Summarizing the results of the comparison, we could note that the medical students, in general, were more motivated to study but had worse attitudes towards the use of digital technologies in education compared to the psychological and natural science students. In our opinion, these differences corresponded to the peculiarities of medical education, which we noted in our previous publications (Novikova et al., 2021; Novikova, Bychkova, 2022). Table 2 presents Spearman’s correlations between the FFM personality traits, Academic motivation scales and Indicators of Attitudes towards DETs. Figures 1 visualize these correlations. From the correlation matrix (Table 2), we are most interested in the correlations of the DET indicators with personality traits and scales of academic motivation. Among the FFM personality traits, extraversion is most positively associated with the DET indicators (3 main indicators), openness is positively correlated with the two DET indicators (“General involvement in the use of DETs” and “Involvement in the digital space”), and conscientiousness is positively correlated with only one indicator (“The use of digital technologies in education”). These data are partially consistent with the results of previous studies of the relationship between the FFM personality traits and different features of online learning (Bhagat et al., 2019; Belinskaya, Fedorova, 2020). Among the academic motivation scales, Intrinsic cognition motivation, Motivation for personal growth and Motivation for self-respect are positively associated with the DET indicators (“General involvement in the use of DETs” and “Involvement in the digital space”), while Introjected motivation is negatively associated with “The use of digital technologies in education” and “Digital competence”, External regulation is negatively associated with “Digital competence”, and Amotivation is negatively associated with “General involvement in the use of DETs” and “Involvement in the digital space”. These data generally confirm the important role of intrinsic academic motivation in the involvement of students in the digital space; however, as shown in our previous publications, the students’ attitudes towards DETs are more closely related with the academic motivation in the natural sciences and psychological students than in the medical students (Novikova, Bychkova, 2022). We also would like to note that the “Digital competence” indicator does not have significant correlations with personality traits, and is negatively associated with only two scales of extrinsic academic motivation. These results confirm that this indicator is a control one in our questionnaire: it does not reflect attitudes towards DETs and may be associated with different profiles of Internet activity (Soldatova, Nestik, 2016). Due to these facts, we did not include this indicator in further regression analysis. Table 2 Spearman’s Correlations between the Variables Studied in the Total Sample of Students (N = 173) Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1. Neuroticism - 2. Extraversion -.33*** - 3. Openness -.05 .18* - 4. Agreeableness -.01 .28*** -.01 - 5. Conscientiousness -.37*** .39*** .16* .18* - 6. Intrinsic cognition motivation -.19* .33*** .17* .14 .37*** - 7. Achievement Motivation -.22** .31*** .22** .04 .41*** .73*** - 8. Motivation for personal growth -.09 .31*** .11 .13 .26*** .65*** .63*** - 9. Motivation for self-respect .21** .08 -.02 .23** .10 .30*** .29*** .57*** - 10. Introjected motivation .35*** -.17* -.06 .23** -.06 -.06 -.08 .08 .43*** - 11. External regulation .27*** -.17* -.12 .03 -.23** -.20** -.11 -.07 .17* .63*** - 12. Amotivation .17* -.12 -.12 -.13 -.13 -.48*** -.23** -.33*** -.14 .09 .31*** - 13. General involvement in the use of DET -.07 .17* .18* -.01 .10 .21** .14 .19* .20** .01 -.02 -.16* - 14. Involvement in the digital space -.05 .16* .19* -.04 .09 .20** .13 .18* .19* .01 .00 -.17* .93*** - 15. The use of digital technologies in education -.11 .22** .07 -.07 .17* .06 .11 .09 .06 -.20** -.15 .11 .46*** .29*** - 16. Digital competence -.12 -.11 .08 .02 .00 .08 .12 .12 -.04 -.20** -.18* -.12 -.01 .05 -.12 - Note: * - p < 0.05; ** - p < 0.01; *** - p < 0.001. Figure 1. Graphical Representation of the Correlations between all the Variables Studied in the Total Sample of Students (N = 173) Note: The blue lines are positive correlations, and the red lines are negative correlations; the line thickness corresponds to the value of the correlation coefficient. GIU is General involvement in the use of DETs; IDS is Involvement in the digital space; UDT is The use of digital technologies in education; DC is Digital competence; N is Neuroticism; E is Extraversion; O is Openness; A is Agreeableness; C is Conscientiousness; MC is Intrinsic Cognition Motivation; MA is Achievement Motivation; MP is Motivation for Personal Growth; MS is Motivation for Self-Respect; MI is Introjected Motivation; ME is External Regulation; AM is Amotivation. The results of multiple regression analysis (best predictor models) are presented in Tables 3-5. The multiple correlation coefficients between the dependent variables (Indicators of Attitudes towards DETs, except for Digital competence) and the predictors (FFM personality traits and Academic motivation scales) for most of the models are statistically significant according to the Fisher F-test, which confirms that there is a significant impact of some FFM personality traits and/or Academic motivation scales on Indicators of Attitudes towards DETs. At the same time, there is a large range of the adjusted determination coefficients (R2adj), which reflects different degrees of the impact of these predictors on different Indicators of Attitudes towards DETs. Table 3 Best predictor regression models for General involvement in the use of DETs Sample/Variable Summary of Model Coefficients R2adj F p-Value Estimate Std. Error t-Value p-Value Total sample (N = 173) .0661 5.06 .002 (Intercept) 15.863 2.6155 6.07 < .001 Openness .118 .0535 2.21 .028 Motivation for self-respect .163 .0723 2.26 .025 Amotivation -.171 .0972 -1.76 .080 Natural sciences students (N = 63) .147 4.57 .006 (Intercept) 8.495 4.0404 2.10 .040 Openness .210 .0868 2.42 .019 Intrinsic cognition motivation .897 .3009 2.98 .004 Achievement motivation -.638 .2464 -2.59 .012 Medical students (N = 62) .110 2.89 .030 (Intercept) 11.412 4.2708 2.67 .010 Motivation for self-Respect .306 .1401 2.18 .033 Introjected Motivation -.371 .1602 -2.32 .024 External regulation .357 .1596 2.24 .029 Openness .161 .0838 1.92 .060 Psychology students (N = 48) .211 5.20 .004 (Intercept) 19.503 4.625 4.22 < .001 Agreeableness -.277 .105 -2.63 .012 Intrinsic cognition motivation .569 .196 2.91 .006 Motivation for self-respect .344 .166 2.07 .045 Table 3 shows that the best predictor model for General involvement in the use of DETs predicts only 6.61% of the variance in the total sample, 21.1% in the psychology students, 14.7% in the natural sciences students and 11.0% in the medical students. Openness has a significant positive impact on General involvement in the use of DETs in the total sample and in the natural sciences students. Motivation for self-respect has a significant positive impact on General involvement in the use of DETs in the total sample as well as in the medical and psychology students. Intrinsic cognition motivation has a significant positive impact on General involvement in the use of DETs in the natural sciences and psychology students. External regulation has a significant positive impact on General involvement in the use of DETs in the medical students. Achievement motivation has a significant negative impact on General involvement in the use of DETs in the natural sciences students. Introjected motivation has a significant negative impact on General involvement in the use of DETs in the medical students. Agreeableness has a significant negative impact on General involvement in the use of DETs in the psychology students. Summarizing these results, we can once again emphasize that personality and motivational predictors of attitudes towards DETs vary significantly among the students of different fields of study. Among the FFM personality traits, openness is in most cases a significant positive predictor of interest and positive attitudes towards the use of digital technologies in education, which is consistent with the data obtained by K.K. Bhagat and his colleagues (Bhagat et al., 2019). Among the scales of academic motivation, we can note the positive impact of motivation for self-respect and the negative effect of amotivation on the involvement of most students in the use of DET. Table 4 shows that the regression models for Involvement in the digital space predict only 8.19% of the variance in the total sample, 21.0% in the psychology students, 16.1% in the natural sciences students, and 10.2% in the medical students. Openness has a significant positive impact on Involvement in the digital space in the total sample and in the natural sciences students. Motivation for self-respect has a significant positive impact on Involvement in the digital space in the total sample. Intrinsic cognition motivation has a significant positive impact on Involvement in the digital space in the natural sciences and psychology students. External regulation has a significant positive impact on Involvement in the digital space in the medical students. Extraversion has a significant positive impact on Involvement in the digital space in the psychology students. Introjected motivation has a significant positive impact on Involvement in the digital space in the psychology students, but in the medical students this impact is negative. Agreeableness has a significant negative impact on Involvement in the digital space in the psychology students. Amotivation has a significant negative impact on Involvement in the digital space in the total sample and in the medical students. Achievement motivation has a significant negative impact on Involvement in the digital space in the natural sciences students. Table 4 Best predictor regression models for Involvement in the digital space Sample/Variable Summary of Model Coefficients R2adj F p-Value Estimate Std. Error t-Value p-Value Total sample (N = 173) .0819 6.11 < .001 (Intercept) 10.191 2.2049 4.62 < .001 Openness .114 .0451 2.52 .013 Motivation for self-respect .135 .0610 2.22 .028 Amotivation -.172 .0819 -2.10 .037 Natural sciences students (N = 63) .161 4.97 .004 (Intercept) 2.735 3.3960 0.805 .424 Openness .192 .0730 2.626 .011 Intrinsic cognition motivation .714 .2529 2.823 .006 Achievement motivation -.435 .2071 -2.100 .040 Medical students (N = 62) .102 2.72 .038 (Intercept) 15.219 1.929 7.89 < .001 Motivation for self-respect .198 .117 1.70 .095 Introjected motivation -.282 .135 -2.09 .041 External regulation .298 .139 2.14 .036 Amotivation -.371 .185 -2.00 .050 Psychology students (N = 48) .210 4.11 .007 (Intercept) 9.921 4.4906 2.21 .033 Extraversion .189 .0888 2.13 .039 Agreeableness -.288 .0962 -3.00 .005 Intrinsic cognition motivation .374 .1824 2.05 .047 Introjected motivation .302 .1451 2.09 .043 As in the previous case, we can note the positive impact of openness and motivation for self-respect, and the negative effect of amotivation on the involvement of most students in the digital space in general. However, in Table 4 we can see more differences between the students of different fields of study. For example, if, for the natural science and psychological students, intrinsic cognition motivation is a positive predictor of general interest and involvement in the digital space, then, for the medical students, it is external regulation. At the same time, as noted above, introjected (extrinsic) motivation has the opposite impacts on the overall involvement of the psychological (positive) and medical (negative) students in the digital space. In addition, if, for “digital involvement” in the natural science students, a positive impact of openness is confirmed, then, for the psychological students, extraversion has a positive impact but agreeableness has a negative impact; whereas, in the medical students, none of the personality traits has a significant impact in this case. 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 Sample/Variable Summary of Model Coefficients R2adj F p-Value Estimate Std. Error t-Value p-Value Total sample (N = 173) .0695 4.21 .003 (Intercept) 10.1812 1.6496 6.17 < .001 Extraversion .0639 .0313 2.04 .043 Motivation for self-respect .1219 .0604 2.02 .045 Introjected motivation -.1672 .0614 -2.72 .007 Amotivation .1216 .0721 1.69 .093 Natural sciences students (N = 63) .111 4.87 .011 (Intercept) 10.966 1.6617 6.60 < .001 Conscientiousness .136 .0447 3.05 .003 Achievement motivation -.188 .0863 -2.17 .034 Medical students (N = 62) .261 8.17 < .001 (Intercept) -0.547 3.0823 -0.177 .860 Neuroticism .221 .0549 4.018 < .001 Motivation for personal growth .444 .1271 3.498 < .001 Introjected motivation -.243 .0847 -2.864 .006 Psychology students (N = 48) .281 7.14 < .001 (Intercept) 8.840 3.4058 2.60 .013 Extraversion .200 .0665 3.00 .004 Openness -.163 .0772 -2.11 .040 Achievement motivation .281 .1241 2.26 .029 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 self-respect 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 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 agreement 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.

About the authors

Irina A. Novikova

Peoples’ Friendship University of Russia (RUDN University)

Author for correspondence.
Email: novikova-ia@rudn.ru
ORCID iD: 0000-0001-5831-1547

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

Peoples’ Friendship University of Russia (RUDN University)

Email: bychkova.polina.97@mail.ru
ORCID iD: 0000-0002-6526-7262

MA in Psychology, is graduate student at the Psychology and Pedagogics Department

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

Alexey L. Novikov

Peoples’ Friendship University of Russia (RUDN University)

Email: Novikov-al@rudn.ru
ORCID iD: 0000-0003-3482-5070

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

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

Dmitriy A. Shlyakhta

Peoples’ Friendship University of Russia (RUDN University)

Email: shlyakhta-da@rudn.ru
SPIN-code: 6172-5460
Ph.D. in Psychology, Associate Professor, is Associate Professor at the Psychology and Pedagogics Department 6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

References