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Digital Technologies in Higher Education: Development of Technology for Individualizing Education Using E-Courses. Research Project Data

dataset
posted on 01.07.2021, 08:08 authored by Marina SorokovaMarina Sorokova, Maria OdintsovaMaria Odintsova, Nataly RadchikovaNataly Radchikova
The experimental study was conducted at the Moscow State University of Psychology and Education (MSUPE) in the framework of the research project “Digital Technologies in Higher Education: Development of Technology for Individualizing Learning Using E-Courses” in 2019 - 2021. The project design was approved by the MSUPE Scientific Expert Council and by the MSUPE Scientific Council.
Datasets and main research results are presented. MSUPE students participated in e-courses (ECs) in mathematical methods in psychology (MMinP) using blended learning (BL) format in the fall semester 2019 - spring semester 2020 or online learning (OL) format in the fall semester 2020.
The data and results of comparative analysis of the attitudes of Russian Federation (RF) university teachers towards using of digital educational technologies (DET) in higher education (HE) are presented.
Scale for assessing university digital educational environment (AUDEE Scale) was developed and validated

Funding

The reported study was funded by the Moscow State University of Psychology and Education (MSUPE) in the framework of the research project "Digital Technologies in Higher Education: Development of Technology for Individualizing Education Using E-Courses"

History

Research questions / Hypotheses

H1 Individualized learning using e-courses in mathematical methods in psychology in blended format provides better academic achievements compared to full-time education H2 Online individualized learning through e-courses in mathematical methods in psychology provides the same level of students’ academic achievements compared to blended one H3 Graduate students experience more difficulties in studying through e-courses, and their learning is less effective than undergraduate ones H4 For both students’ categories, there are typical predictive opinions about e-learning specific to each category H5 There are differences both in higher education perceptions and attitudes towards digital technologies among university teachers who use and do not use e-courses in their professional activities H6 University teachers experienced in developing and using e-courses differently assess advantages and difficulties of acting in an electronic environment, as opposed to those without such experience

Participants / Sample description

Dataset 1. Perceived learning experiences. Total N = 344 MSUPE students participating in MMinP ECs: N = 161 graduate (82,6% female), N = 183 undergraduate (81,4% female). Available at Mendeley Data Dataset 2. Educational outcomes of MSUPE students who completed MMinP ECs in BL (N = 424). Available at Mendeley Data Dataset 3. DET assessments of RF universities teachers who use ECs (N = 110) or do not use ECs (N = 40) in their professional activities. Age: 25 - 78 years old (48.87 ± 11.81) Dataset 4. Semantic differential data to compare the ideas about HE of university teachers who do not use (N = 9; 55,6% female) or do use (N = 15; 73,3% female) ECs in their professional activities Dataset 5. Academic achievements of MSUPE students who completed MMinP ECs in BL (N = 404) or OL (N = 405), total N = 809. Available at RusPsyDATA Dataset 6. AUDEE Scale development. N = 406 (90,1% female), age 19-72 years old. Respondents are MSUPE students who completed OL ECs. Available at RusPsyDATA

Apparatus and materials

1. Academic achievements in MMinP tests 2. Survey “Universities teachers assessments of DET” 3. Survey “Students' opinions on the EC” 4. Scale for Assessing University Digital Educational Environment (AUDEE Scale by M. Sorokova, M. Odintsova, and N. Radchikova). 5. To check the external validity, 5 questionnaires were used. Study-related experiences were measured by Activity-Related Experiences Assessment technique (AREA) developed by D. Leontiev at al. Academic motivation was evaluated by “Academic Motivation Scales” Questionnaire (by T. Gordeeva, O. Sychev, and E. Osin). Students’ adaptability was accessed by a questionnaire developed by T. Dubovitskaya and A. Krylova. Moral behavior was evaluated with the help of Moral Disengagement Questionnaire (MD-24) adapted by Y. Ledovaya and her colleagues. Subjective wellbeing was measured by the Personal Wellbeing Index for adults (PWI-A) developed by International Wellbeing Group and adapted in Russian by E. Osin and D. Leontiev

Experimental design

MSUPE students participated in ECs in MMinP using BL (fall semester 2019 - spring semester 2020) or OL (fall semester 2020) All 3 ECs are aimed at developing competencies for quantitative analysis of empirical data in psychological research in SPSS. LMS Moodle was used. The EC for undergraduate students consists of 3 mandatory modules. The ECs for graduate students contain also optional Module 4 We compared perceived experiences and academic achievements (AA) of students who completed 3 mandatory modules. AA were evaluated using 5 tests inside the EC, and an individual case-task. Students who completed the EC filled out an anonymous feedback survey. After 1 - 4 months, they participated at external testing again to evaluate knowledge retention. Pretest, final test and external test are the same RF universities teacher’s assessments of DET and their ideas about HE were also analyzed. Respondents were teachers who use ECs or do not use them in their professional activities

Data collection procedure

Dataset 1 “Perceived learning experiences” and Dataset 2 “Educational outcomes of MSUPE students who completed e-courses in MMinP in BL” were collected in the fall semester 2019 and spring semester 2020. Only upon completion of the e-course, students filled out the feedback questionnaire. Both databases are available at Mendeley Data. The survey of university teachers was conducted online using the questionnaire available at the project website. The respondents had the opportunity to fill out the questionnaire (Datasets 3) and/or semantic differential matrix (Datasets 4). The data is collected in January - September 2020. In the fall semester 2020 the MSUPE was functioning in online format. Students participated in ECs in OL only. The Datasets 5 and 6 were collected after students’ completion one of the 3 ECs mentioned above. The Datasets 5 consists of 2 parts: academic achievements of MSUPE students who completed ECs in MMinP in blended (1st part) or online learning (2nd part)

Statistical methods

1. Hierarchical Cluster Analysis with Average Linkage Within Group method 2. Exploratory Factor Analysis (EFA) with Principal Component Extraction method and Varimax Rotation 3. Confirmatory Factor Analysis (CFA) 4. Two-way analysis of covariance (ANCOVA) 2x2 5. Two-way analysis of variance (ANOVA) for a mixed scheme 4. Cronbach's alpha coefficient 5. Guttman's alpha coefficient 6. Pearson correlation coefficient 7. Parametric and nonparametric statistical tests 7. Descriptive statistics The data analysis was performed in the SPSS V.23 and V.25

Results

1. Most students confirm the benefits of ECs. No substantial difficulties were indicated. This contradicts bias 2. No significant differences in AA between graduate and undergraduate students were found, and also between BL and OL 3. Effectiveness of ECs in both BL and OL formats is statistically proven 4. Scale for assessing university digital educational environment (AUDEE Scale) was developed and validated 5. Аll university teachers can be divided into "skeptics" and "enthusiasts" in relation to the acceptance of DET. "Enthusiasts", unlike "skeptics", have a positive attitude to innovations 6. For university teachers not using ECs, DET are socially accessible and technically advanced but very problematic and subjectively unattractive. For instructors using ECs, DET are also problematic, but socially accessible, convenient for teacher and subjectively very attractive 7. For both categories, there is a factor “The most attractive HE qualities”, but the sets of qualities are different

Publication reference

Sorokova, Marina G. (2020) Skepticism and learning difficulties in a digital environment at the Bachelor's and Master's levels: are preconceptions valid? // Heliyon, V. 6, Issue 11, E05335. DOI: https://doi.org/10.1016/j.heliyon.2020.e05335

Affiliation

Marina G. Sorokova, Doctor in Education, PhD in Physics and Mathematics, Head of Scientific and Practical Center for Comprehensive Support of Psychological Research PsyDATA, Professor, Chair of Applied Mathematics, Faculty of Information Technology, Moscow State University of Psychology and Education, Moscow, Russia Maria A. Odintsova, PhD in Psychology, Professor, Chair of Psychology and Pedagogy of Distance Learning, Head of the Chair of Psychology and Pedagogy of Distance Learning, Faculty of Distance Learning, Moscow State University of Psychology and Education, Moscow, Russia Nataly P. Radchikova, PhD in Psychology, Associate Professor of the Department of Developmental Psychology, Faculty of Pre-School Pedagogy and Psychology, Moscow Pedagogical State University; Leading Researcher of Scientific and Practical Center for Comprehensive Support of Psychological Research PsyDATA, Moscow State University of Psychology and Education, Moscow, Russia