Session Information
16 ONLINE 19 A, ICT Supported Learning Environments
Paper Session
MeetingID: 813 0160 4848 Code: Lbac4z
Contribution
Significant experience in the application of modern digital technology and online learning, accumulated by universities around the world during the COVID-19 pandemic, has a good chance of becoming a powerful driver for the development of learning and teaching methods in digital educational environments (Lockee, 2021). The problem of digital socialization at a new historical stage in the development of society includes a wide range of issues (Spante et al., 2018). These are issues of acceptance / rejection of new technologies; attitudes towards innovation; motivation to study in a digital educational environment (DEE); the quality of experience as characteristic of educational activity; moral codes (academic honesty / dishonesty) as universal components of human culture; the ability to quickly adapt to the changing conditions of digitalization. This is also the problem of satisfaction with online learning; the effectiveness of the use of information and communication technologies, their assessments and factors that determine this effectiveness (Richards, 2006). A number of studies have shown that among the most important factors in the readiness of university students and teachers for distance and online learning is the development of their digital competencies and experience in the use of modern ICT technologies (Callo & Yazon, 2020; Martin et al., 2020). An integrated approach that has become widespread recently (Castro & Tumibay, 2019) proposes to determine the efficiency of online learning as a set of two indicators: the level of knowledge, skills, and abilities and student satisfaction with the experience of online education. Academic performance was often considered in the research, possibly due to its easy availability, while satisfaction with online education and convenience of work in a DEE were rarely examined. That is why students’ attitudes towards the DEE were taken as the indicator of e-learning performance in this work. Students’ attitudes towards DEE, acceptance of DEE are even more important because without acceptance and positive attitude good educational results are impossible. In such a way, the main question of interest for scientists working in this field concerns the prerequisites and factors of DEE acceptance. It may be assumed that among such prerequisites should be 1) motivation to learn in a digital educational environment; 2) experiencing pleasure and meaningfulness of learning activities in new conditions; 3) moral codes (academic honesty) as universal components of human culture and 4) the ability to quickly adapt to educational activities in the context of digitalization. It is also believed that the use of e-courses creates especially many problems for older Masters' level students, as they are less adapted to the use of digital technologies than the “generation of millennium”. In other words, it is more difficult for older graduate students to adapt to learning in a digital environment, they experience more problems and are more critical. That is why the age of students (or generation differences) may be also a good predictor for DEE acceptance. The present study aimed to address all these matters and answer the following questions. What opinions are typical for most students concerning difficulties and benefits of studying in the e-course and what is their attitude to this format? How do most students assess their independence, engagement in the learning process and the practical usefulness of the e-course, do they use dishonest strategies in online testing? Are there differences between undergraduate or graduate students in DEE accepting/rejecting? What are the prerequisites of DEE acceptance?
Method
The study had two stages. At the first stage the aim was to compare perceived learning experiences of students who completed the e-course with flipped design, to identify the set of opinions common to the majority of students and assess differences of perceived experiences between graduate and undergraduate students. Total N = 344 students (from Moscow State University of Psychology and Education) were tested: N = 161 graduate (82,6% female), N = 183 undergraduate (81,4% female). There was not significant gender difference (Chi-square test, p = 0.884) but groups significantly differ in age (Chi-square test, p < 0.001). Master's level students are mainly adults: 17.4% are students aged 20–24, 13.0% are 25–29 years old, 24.8% are 30–34 years old and 44.7% are 35 years old and older, while in undergraduate students’ group youth predominates – 16.9% under the age of 20 years, 81.4% – 20–24 years old, and only 1.6% are respondents 25 years old and older. All the data is available at Mendeley Data (Sorokova, 2020b). At the second stage 406 students of Moscow State University of Psychology and Education took part in the investigation (90,1% female). The average age was 28,7±9,6 years (median = 24 years) varying from 19 to 72 years. The data was obtained in September-December of 2020 when the University worked in distance mode. To measure the attitudes towards DEE a Scale for Assessing University Digital Educational Environment was used: AUDEE Scale (Sorokova et al., 2021b). Academic motivation was evaluated by “Academic Motivation Scales” Questionnaire (Gordeeva et al., 2014) Study-related experiences were measured by Activity-Related Experiences Assessment technique AREA (Leontiev et al., 2018). Moral behavior was evaluated with the help of Moral Disengagement Questionnaire MD-24 (Ledovaya et al., 2016). Students’ adaptability was accessed by a special questionnaire developed by (Dubovitskaya & Krylova, 2010). The database is available at RusPsyDATA (Sorokova et al., 2021a).
Expected Outcomes
The results of the first study (Sorokova, 2020a) showed that most students (undergraduate and graduate) confirm the benefits of e-courses and there are not statistically significant differences between them. No substantial difficulties in on-line learning were identified. Most respondents at both levels show a positive motivation for learning in a digital environment. This contradicts the preconception that older Master's level students harder adapt to digital environment learning, experience more difficulties and are more critical. Most students confirm the interactive style of blended sessions' activities, i.e., mutual assistance and interaction with classmates and the instructor, but they often perceive deficiency of personal contacts with the instructor and do not agree to replace face-to-face sessions with webinars and communication on forums. This set of opinions is more characteristic of graduate students and could be explained by their beliefs of instructors supported the full-time education. Most of the respondents are characterized by careful answers to points concerning the use of dishonest strategies in online learning and the belief that dishonest strategies using is inevitable. This fact may be explained by a reflection of their life experiences or an excuse for their own dishonest strategies. The results of the second study (Radchikova et al., 2021) show that the prerequisites for the DEE acceptance are the following: experience of pleasure and meaningfulness of educational activity; increased motivation to learn new things, to achieve high results in studies, to develop their abilities; sufficient adaptability to the educational process. Prerequisites for DEE resistance are the following: the experience of emptiness and meaninglessness of educational activity; decreased motivation; insufficient adaptability to the educational process; using some mechanisms of moral separation to neutralize moral responsibility. These characteristics can be viewed as resources for embracing digital reality in education, reducing tensions and determining the success of information technology adoption.
References
Callo E. C., Yazon A. D. (2020). Exploring the Factors Influencing the Readiness of Faculty and Students on Online Teaching and Learning as an Alternative Delivery Mode for the New Normal. Universal Journal of Educational Research, 8(8), 3509-3518. https://doi.org/10.13189/ujer.2020.080826. Castro, M. D. B., Tumibay, G. M. (2019) A literature review: efficacy of online learning courses for higher education institution using meta-analysis. Education and Information Technologies, 24, 1-19. https://doi.org/10.1007/s10639-019-10027-z Dubovitskaya, T. D., Krylova, A. V. (2010) Method of Research of Students Adaptability in the Higher Educational Establishment. Psychological Science and Education, 2. URL: https://psyjournals.ru/psyedu_ru/2010/n2/27814.shtml (In Russ.) Gordeeva, T., Sychev, O., Osin, E. (2014) “Academic motivation scales” questionnaire. Psikhologicheskii Zhurnal, 35, 96-107 (In Russ.) Ledovaya, Y. et. al. (2016) Moral disengagement: the psychological construct and its measurement. Vestnik of St Petersburg University. Series 16. Psychology and Education, 6, 23-39 (In Russ.). Leontiev, D. A. et al. (2018) Study-Related Experiences and Their Association with Psychological Well-Being. Psychological Science and Education, 23(6), 55-66 (In Russ.) Lockee, B. B. (2021) Online education in the post-COVID era. Nature Electronics, 4, 5–6. https://doi.org/10.1038/s41928-020-00534-0 Martin, F., Stamper, B., Flowers, C. (2020) Examining Student Perception of Readiness for Online Learning: Importance and Confidence. Online Learning, 24(2). http://dx.doi.org/10.24059/olj.v24i2.2053. Radchikova, N. P., Odintsova, M. A., Sorokova, M. G. (2021) Prerequisites for Accepting the Digital Educational Environment in New Cultural and Historical Conditions. Cultural-Historical Psychology, 17(3), 115-124. https://doi.org/10.17759/chp.2021170315 Richards, C. (2006). Towards an integrated framework for designing effective ICT-supported learning environments: The challenge to better link technology and pedagogy. Technology, Pedagogy and Education. 15. 239-255. https://doi.org/10.1080/14759390600769771. Sorokova, M. (2020a) Educational outcomes of graduate and undergraduate students who completed e-courses in mathematical methods in psychological and educational researches, Mendeley Data, V1. https://doi.org/10.17632/hvfkdpfwnr.1 Sorokova, M. G. (2020b) Skepticism and learning difficulties in a digital environment at the Bachelor's and Master's levels: are preconceptions valid? Heliyon, 6(11), E05335. https://doi.org/10.1016/j.heliyon.2020.e05335 Sorokova, M. G., Odintsova, M. A., Radchikova, N. P. (2021a) AUDEE Scale: validation database. Psychological Research Data & Tools Repository. Dataset, https://doi.org/10.25449/ruspsydata.14742816 Sorokova, M. G., Odintsova, M. A., Radchikova, N. P. (2021b) Scale for Assessing University Digital Educational Environment (AUDEE Scale). Psychological Science and Education, 26(2), 52-65. https://doi.org/10.17759/pse.2021260205 Spante M., Hashemi S. S., Lundin M., Algers A. (2018) Digital competence and digital literacy in higher education research: Systematic review of concept use, Cogent Education, 5:1. https://doi.org/10.1080/2331186X.2018.1519143
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