Research Article
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Year 2025, Volume: 12 Issue: 5, 133 - 153, 01.09.2025
https://doi.org/10.17275/per.25.67.12.5

Abstract

References

  • Achuthan, K., Kolil, V. K., Muthupalani, S., & Raman, R. (2024). Transactional distance theory in distance learning: Past, current, and future research trends. Contemporary Educational Technology, 16(1), ep493. https://doi.org/10.30935/cedtech/14131
  • Al-Rawashdeh, A., Mohammed, E. Y., Al Arab, A. R., Alara, M., & Al-Rawashdeh, B. (2021). Advantages and disadvantages of using e-learning in university education: Analyzing students’ perspectives. Electronic Journal of E-Learning, 19(3), 107–117. https://doi.org/10.34190/ejel.19.3.2168
  • Bağrıacık Yılmaz, A. (2023). The Relationship between Satisfaction, Interaction, E-learning Readiness, and Academic Achievement in Online Learning. Open Praxis, 15(3), 199–213. https://doi.org/10.55982/openpraxis.15.3.578
  • Bawaneh, A. (2021). The satisfaction level of undergraduate science students towards using e-learning and virtual classes in exceptional condition covid-19 crisis. Turkish Online Journal of Distance Education, 22(1), 52–65. https://doi.org/10.17718/tojde.849882
  • Bergamin, P., Ziska, S., & Groner, R. (2010). Structural equation modeling of factors affecting success in student’s performance in ODL-programs: Extending quality management concepts. Open Praxis, 4(1), 18–25. https://openpraxis.org/articles/218
  • Bervell, B., & Arkorful, V. (2020). LMS-enabled blended learning utilization in distance tertiary education: establishing the relationships among facilitating conditions, voluntariness of use and use behaviour. International Journal of Educational Technology in Higher Education, 17(1), 1–16. https://doi.org/10.1186/s41239-020-0183-9
  • Bringula, R., Reguyal, J. J., Tan, D. D., & Ulfa, S. (2021). Mathematics self-concept and challenges of learners in an online learning environment during COVID-19 pandemic. Smart Learn. Environ. 8, 22. https://doi.org/10.1186/s40561-021-00168-5
  • Caird, S., & Roy, R. (2019). Blended Learning and Sustainable Development. In W. L. Filho (Ed.), Encyclopedia of Sustainability in Higher Education (pp. 107–116). Springer. https://doi.org/10.1007/978-3-319-63951-2_197-1
  • Cavanaugh, J., Jacquemin, S. J., & Junker, C. R. (2023). Variation in Student Perceptions of Higher Education Course Quality and Difficulty as a Result of Widespread Implementation of Online Education During the COVID-19 Pandemic. Technology, Knowledge and Learning, 28(3), 1787–1802. https://doi.org/10.1007/s10758-022-09596-9
  • Christensen, E. W., Anakwe, U. P., & Kessler, E. H. (2001). Receptivity to distance learning: The effect of technology, reputation, constraints, and learning preferences. Journal of Research on Computing in Education, 33(3), 263-279. https://doi.org/10.1080/08886504.2001.10782314
  • Cobo, C., & Moravec, J. (2011). Aprendizaje invisible: hacia una nueva ecología de la educación [Invisible Learning: Toward a new ecology of education]. Laboratori de Mitjans Interactius. Publicacions i Edicions de la Universitat de Barcelona. https://www.uv.es/bellochc/MasterPoliticas/Cobo_Moravec.pdf
  • Conklin, S., Ovarzun, B., Kim, S., & Dikkers, A.G. (2024). Exploring the Relationships of Learners and Instructors in Online Courses. Online Learning, 28(4), (257-281). https://doi.org/10.24059/olj.v28i4.3934
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(7), 1–9. https://doi.org/10.7275/jyj1-4868
  • Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Pearson.
  • Ekwunife-Orakwue, K. C. V., & Teng, T. L. (2014). The impact of transactional distance dialogic interactions on student learning outcomes in online and blended environments. Computers & Education, 78(1), 414–427. https://doi.org/10.1016/j.compedu.2014.06.011
  • Fox J (2022). polycor: Polychoric and Polyserial Correlations. R package version 0.8-1. https://CRAN.R-project.org/package=polycor
  • Freitas, A., Neves, A. J., & Carvalho, P. (2020). Perceção de estudantes de Matemática sobre a aprendizagem a distância: um caso de estudo no contexto da pandemia COVID-19. Indagatio didactica, 12(5), 273–285. https://doi.org/10.34624/id.v12i5.23472
  • Gall, M., Gall, P., & Borg, W. (2003). Educational research: An introduction. Allyn and Bacon.
  • Garrison, D. R., & Vaughan, N. D. (2008). Blended learning in higher education: Framework, principles, and guidelines. Jossey-Bass.
  • Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk, & C. R. Graham (Eds.), The Handbook of Blended Learning: Global Perspectives, Local Designs (pp. 2–21). Pfeiffer Publishing.
  • Gross, J., & Ligges, U. (2015). nortest: Tests for Normality. R package version 1.0-4. https://CRAN.R-project.org/package=nortest
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Pearson.
  • Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and Psychological Measurement, 34(1), 111–117. https://doi.org/10.1007/s11528-019-00375-5
  • Karaoglan-Yilmaz, F., Zhang, K., Ustun, A., & Yilmaz, R. (2022). Transactional distance perceptions, student engagement, and course satisfaction in flipped learning: A correlational study. Interactive Learning Environments, 32(2), 447–462. https://doi.org/10.1080/10494820.2022.2091603
  • Kintu, M. J., Zhu, C. (2016). Student characteristics and learning outcomes in a blended learning environment intervention in a Ugandan University. Electronic Journal of e-Learning, 14(3), 181–195. https://files.eric.ed.gov/fulltext/EJ1107126.pdf
  • Kuo, Y-C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35–50. http://dx.doi.org/10.1016/j.iheduc.2013.10.001
  • Lencastre, J. A., & Coutinho, C. (2015). Blended learning. In M. Khosrow-Pour (Org.), Encyclopedia of Information Science and Technology, Third Edition, Volume II, 1360-1368. IGI Global. https://doi.org/10.4018/978-1-4666-5888-2.ch129
  • Lin, B., & Vassar, J. A. (2009). Determinants for success in online learning communities, International Journal of Web-based Communities, 5(3), 340–350. https://doi.org/10.1504/IJWBC.2009.025210
  • Mahande, R., & Akram. (2021). Motivational factors underlying the use of online learning system in higher education: an analysis of measurement model. Turkish Online Journal of Distance Education, 22(1), 89–105. https://doi.org/10.17718/tojde.849888
  • Min, W., & Yu, Z. (2023). A systematic review of critical success factors in blended learning. Education Sciences, 13(5), 469. https://doi.org/10.3390/educsci13050469
  • Moore, M. (1993). Theory of transactional distance. In D. Keegan (Ed.), Theoretical Principles of Distance Education (v. 1, pp. 22–38). Routledge.
  • Mpungose, C. B. (2020). Emergent transition from face-to-face to online learning in a South African University in the context of the Coronavirus pandemic. Humanities and Social Sciences Communications, 7(1), 1–9. https://doi.org/10.1057/s41599-020-00603-x
  • Nicholson, P. (2007). A History of E-Learning. In B. Fernández-Manjón, J. M. Sánchez-Pérez, J. A. Gómez-Pulido, M. A. Vega-Rodríguez, & J. Bravo-Rodríguez (Eds.), Computers and Education (pp. 1–11). Springer. https://doi.org/10.1007/978-1-4020-4914-9_1
  • Ocak, M. A., & Ünsal, N. Ö. (2021). A Content Analysis of Blended Learning Studies Conducted during Covid-19 Pandemic Period. Akademik Açı, 1(2), s. 175-210. https://dergipark.org.tr/en/download/article-file/1975480
  • OECD (2016). Innovating education and educating for innovation: The power of digital technologies and skills. OECD Publishing. https://doi.org/10.1787/9789264265097-en
  • OECD (2022). Trends shaping education 2022. OECD Publishing. https://doi.org/10.1787/6ae8771a-en
  • Picciano, A., & Seaman, J. (2007). K-12 online learning: A survey of U.S. school district administrators. Sloan Consortium.
  • Rahman, L. A., Omar, N., Fatzel, F. H. M., & Isa, N. S. M. (2022). Predictors of Student Satisfaction and Perceived Learning in Online Distance Learning: The Effects of Self-efficacy and Interaction. International Journal of Academic Research in Business and Social Sciences, 12(10), 785–803. http://dx.doi.org/10.6007/IJARBSS/v12-i10/14804
  • R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Austria.
  • Regmi, A., Mao, X., Qi, Q., Tang, W., & Yang, K. (2024). Students’ perception and self-efficacy in blended learning of medical nutrition course: A mixed-method research. BMC Med Educ 24, 1411. https://doi.org/10.1186/s12909-024-06339-5
  • Revelle, W. (2024). psych: Procedures for Psychological, Psychometric, and Personality Research. R package version 2.4.12. https://CRAN.R-project.org/package=psych
  • Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02
  • Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. The Internet and Higher Education, 6(1), 1–16. https://doi.org/10.1016/S1096-7516(02)00158-6
  • Salas-Rueda, R. A. (2020). Perception of students on blended learning considering data science and machine learning. Campus Virtuales, 9(1), 125–135.
  • Sarıtepeci, M., & Çakır, H. (2015). The Effect of Blended Learning Environments on Student’s Academic Achievement and Student Engagement: A Study on Social Studies Course. Education and Science, 40(177), 203–216. https://doi.org/10.15390/EB.2015.2592
  • Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396–413.
  • https://doi.org/10.1016/j.compedu.2005.09.004
  • Signorell, A. (2024). DescTools: Tools for Descriptive Statistics. R package version 0.99.56. https://CRAN.R-project.org/package=DescTools
  • Song, L., Singleton, E. S., Hill, J. R., & Koh, M. H. (2004). Improving online learning: student perceptions of useful and challenging characteristics. The Internet and Higher Education, 7(1), 59–70. https://doi.org/10.1016/j.iheduc.2003.11.003
  • Vallée, A., Blacher, J., Cariou, A., & Sorbets, E. (2020). Blended learning compared to traditional learning in medical education: Systematic review and meta-analysis. J Med Internet Res, 22(8):e16504. https://doi.org/10.2196/16504
  • Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press. https://doi.org/10.1017/CBO9780511803932
  • Yu, Z. (2022). Sustaining student roles, digital literacy, learning achievements, and motivation in online learning environments during the COVID-19 Pandemic. Sustainability, 14(8), 4388. https://doi.org/10.3390/su14084388
  • Zhao, L., Hwang, W., & Shih, T. (2021). Investigation of the Physical Learning Environment of Distance Learning Under COVID-19 and Its Influence on Students’ Health and Learning Satisfaction. International Journal of Distance Education Technologies, 19(2), 77–98. https://doi.org/10.4018/IJDET.20210401.oa4

University Students’ Perceptions of the B-Learning Methodology

Year 2025, Volume: 12 Issue: 5, 133 - 153, 01.09.2025
https://doi.org/10.17275/per.25.67.12.5

Abstract

The times of the last pandemic that plagued humanity challenged educators to find alternatives to face-to-face teaching, which arose through technological applications. Based on the symbiosis between face-to-face teaching and distance learning, this study seeks to gain insight into the perceptions of Mathematics and Biomedical Sciences students at a Portuguese public university regarding the b-learning methodology, as well as to identify the underlying factors that determine these perceptions, through their responses to an online survey. Adopting a quantitative approach, this study describes the students’ perceptions of the two courses, identifying characteristics related to their role in this learning modality. An exploratory factor analysis revealed four factors: learner receptivity towards the distance component, learner-learner interaction, learner-instructor interaction and transactional distance. The present findings revealed significant differences between the two courses, particularly in relation to the first factor. Mathematics students had lower factor scores compared to Biomedical Sciences students. Spearman correlation analysis identified effective time management as a crucial work habit associated with all four factors. Regarding the availability of resources, successful adaptation to distance teaching and learning platforms emerged as the primary characteristic correlated to the four factors. Additionally, learner receptivity towards the distance component, learner-instructor interaction and transactional distance were identified as the main factors associated with student motivation towards b-learning. This study suggests that while the b-learning approach proves to be suitable in terms of facilitating dialogue, the distance component has revealed its weakness, especially posing greater challenges for Mathematics students.

Thanks

Work partially supported by CIDMA (Center for Research and Development in Mathematics and Applications, University of Aveiro) under the "Fundação para a Ciência e a Tecnologia" (FCT, https://ror.org/00snfqn58) Multi-Annual Financing Program for R&D Units, project UID/04106; and by CIEd – Research Centre on Education of the Institute of Education of University of Minho (UID/01661/2020), through national funds of FCT/MCTES-PT, namely the Pluriannual Program for the Funding of R&D Units.

References

  • Achuthan, K., Kolil, V. K., Muthupalani, S., & Raman, R. (2024). Transactional distance theory in distance learning: Past, current, and future research trends. Contemporary Educational Technology, 16(1), ep493. https://doi.org/10.30935/cedtech/14131
  • Al-Rawashdeh, A., Mohammed, E. Y., Al Arab, A. R., Alara, M., & Al-Rawashdeh, B. (2021). Advantages and disadvantages of using e-learning in university education: Analyzing students’ perspectives. Electronic Journal of E-Learning, 19(3), 107–117. https://doi.org/10.34190/ejel.19.3.2168
  • Bağrıacık Yılmaz, A. (2023). The Relationship between Satisfaction, Interaction, E-learning Readiness, and Academic Achievement in Online Learning. Open Praxis, 15(3), 199–213. https://doi.org/10.55982/openpraxis.15.3.578
  • Bawaneh, A. (2021). The satisfaction level of undergraduate science students towards using e-learning and virtual classes in exceptional condition covid-19 crisis. Turkish Online Journal of Distance Education, 22(1), 52–65. https://doi.org/10.17718/tojde.849882
  • Bergamin, P., Ziska, S., & Groner, R. (2010). Structural equation modeling of factors affecting success in student’s performance in ODL-programs: Extending quality management concepts. Open Praxis, 4(1), 18–25. https://openpraxis.org/articles/218
  • Bervell, B., & Arkorful, V. (2020). LMS-enabled blended learning utilization in distance tertiary education: establishing the relationships among facilitating conditions, voluntariness of use and use behaviour. International Journal of Educational Technology in Higher Education, 17(1), 1–16. https://doi.org/10.1186/s41239-020-0183-9
  • Bringula, R., Reguyal, J. J., Tan, D. D., & Ulfa, S. (2021). Mathematics self-concept and challenges of learners in an online learning environment during COVID-19 pandemic. Smart Learn. Environ. 8, 22. https://doi.org/10.1186/s40561-021-00168-5
  • Caird, S., & Roy, R. (2019). Blended Learning and Sustainable Development. In W. L. Filho (Ed.), Encyclopedia of Sustainability in Higher Education (pp. 107–116). Springer. https://doi.org/10.1007/978-3-319-63951-2_197-1
  • Cavanaugh, J., Jacquemin, S. J., & Junker, C. R. (2023). Variation in Student Perceptions of Higher Education Course Quality and Difficulty as a Result of Widespread Implementation of Online Education During the COVID-19 Pandemic. Technology, Knowledge and Learning, 28(3), 1787–1802. https://doi.org/10.1007/s10758-022-09596-9
  • Christensen, E. W., Anakwe, U. P., & Kessler, E. H. (2001). Receptivity to distance learning: The effect of technology, reputation, constraints, and learning preferences. Journal of Research on Computing in Education, 33(3), 263-279. https://doi.org/10.1080/08886504.2001.10782314
  • Cobo, C., & Moravec, J. (2011). Aprendizaje invisible: hacia una nueva ecología de la educación [Invisible Learning: Toward a new ecology of education]. Laboratori de Mitjans Interactius. Publicacions i Edicions de la Universitat de Barcelona. https://www.uv.es/bellochc/MasterPoliticas/Cobo_Moravec.pdf
  • Conklin, S., Ovarzun, B., Kim, S., & Dikkers, A.G. (2024). Exploring the Relationships of Learners and Instructors in Online Courses. Online Learning, 28(4), (257-281). https://doi.org/10.24059/olj.v28i4.3934
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(7), 1–9. https://doi.org/10.7275/jyj1-4868
  • Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Pearson.
  • Ekwunife-Orakwue, K. C. V., & Teng, T. L. (2014). The impact of transactional distance dialogic interactions on student learning outcomes in online and blended environments. Computers & Education, 78(1), 414–427. https://doi.org/10.1016/j.compedu.2014.06.011
  • Fox J (2022). polycor: Polychoric and Polyserial Correlations. R package version 0.8-1. https://CRAN.R-project.org/package=polycor
  • Freitas, A., Neves, A. J., & Carvalho, P. (2020). Perceção de estudantes de Matemática sobre a aprendizagem a distância: um caso de estudo no contexto da pandemia COVID-19. Indagatio didactica, 12(5), 273–285. https://doi.org/10.34624/id.v12i5.23472
  • Gall, M., Gall, P., & Borg, W. (2003). Educational research: An introduction. Allyn and Bacon.
  • Garrison, D. R., & Vaughan, N. D. (2008). Blended learning in higher education: Framework, principles, and guidelines. Jossey-Bass.
  • Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk, & C. R. Graham (Eds.), The Handbook of Blended Learning: Global Perspectives, Local Designs (pp. 2–21). Pfeiffer Publishing.
  • Gross, J., & Ligges, U. (2015). nortest: Tests for Normality. R package version 1.0-4. https://CRAN.R-project.org/package=nortest
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Pearson.
  • Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and Psychological Measurement, 34(1), 111–117. https://doi.org/10.1007/s11528-019-00375-5
  • Karaoglan-Yilmaz, F., Zhang, K., Ustun, A., & Yilmaz, R. (2022). Transactional distance perceptions, student engagement, and course satisfaction in flipped learning: A correlational study. Interactive Learning Environments, 32(2), 447–462. https://doi.org/10.1080/10494820.2022.2091603
  • Kintu, M. J., Zhu, C. (2016). Student characteristics and learning outcomes in a blended learning environment intervention in a Ugandan University. Electronic Journal of e-Learning, 14(3), 181–195. https://files.eric.ed.gov/fulltext/EJ1107126.pdf
  • Kuo, Y-C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35–50. http://dx.doi.org/10.1016/j.iheduc.2013.10.001
  • Lencastre, J. A., & Coutinho, C. (2015). Blended learning. In M. Khosrow-Pour (Org.), Encyclopedia of Information Science and Technology, Third Edition, Volume II, 1360-1368. IGI Global. https://doi.org/10.4018/978-1-4666-5888-2.ch129
  • Lin, B., & Vassar, J. A. (2009). Determinants for success in online learning communities, International Journal of Web-based Communities, 5(3), 340–350. https://doi.org/10.1504/IJWBC.2009.025210
  • Mahande, R., & Akram. (2021). Motivational factors underlying the use of online learning system in higher education: an analysis of measurement model. Turkish Online Journal of Distance Education, 22(1), 89–105. https://doi.org/10.17718/tojde.849888
  • Min, W., & Yu, Z. (2023). A systematic review of critical success factors in blended learning. Education Sciences, 13(5), 469. https://doi.org/10.3390/educsci13050469
  • Moore, M. (1993). Theory of transactional distance. In D. Keegan (Ed.), Theoretical Principles of Distance Education (v. 1, pp. 22–38). Routledge.
  • Mpungose, C. B. (2020). Emergent transition from face-to-face to online learning in a South African University in the context of the Coronavirus pandemic. Humanities and Social Sciences Communications, 7(1), 1–9. https://doi.org/10.1057/s41599-020-00603-x
  • Nicholson, P. (2007). A History of E-Learning. In B. Fernández-Manjón, J. M. Sánchez-Pérez, J. A. Gómez-Pulido, M. A. Vega-Rodríguez, & J. Bravo-Rodríguez (Eds.), Computers and Education (pp. 1–11). Springer. https://doi.org/10.1007/978-1-4020-4914-9_1
  • Ocak, M. A., & Ünsal, N. Ö. (2021). A Content Analysis of Blended Learning Studies Conducted during Covid-19 Pandemic Period. Akademik Açı, 1(2), s. 175-210. https://dergipark.org.tr/en/download/article-file/1975480
  • OECD (2016). Innovating education and educating for innovation: The power of digital technologies and skills. OECD Publishing. https://doi.org/10.1787/9789264265097-en
  • OECD (2022). Trends shaping education 2022. OECD Publishing. https://doi.org/10.1787/6ae8771a-en
  • Picciano, A., & Seaman, J. (2007). K-12 online learning: A survey of U.S. school district administrators. Sloan Consortium.
  • Rahman, L. A., Omar, N., Fatzel, F. H. M., & Isa, N. S. M. (2022). Predictors of Student Satisfaction and Perceived Learning in Online Distance Learning: The Effects of Self-efficacy and Interaction. International Journal of Academic Research in Business and Social Sciences, 12(10), 785–803. http://dx.doi.org/10.6007/IJARBSS/v12-i10/14804
  • R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Austria.
  • Regmi, A., Mao, X., Qi, Q., Tang, W., & Yang, K. (2024). Students’ perception and self-efficacy in blended learning of medical nutrition course: A mixed-method research. BMC Med Educ 24, 1411. https://doi.org/10.1186/s12909-024-06339-5
  • Revelle, W. (2024). psych: Procedures for Psychological, Psychometric, and Personality Research. R package version 2.4.12. https://CRAN.R-project.org/package=psych
  • Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02
  • Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. The Internet and Higher Education, 6(1), 1–16. https://doi.org/10.1016/S1096-7516(02)00158-6
  • Salas-Rueda, R. A. (2020). Perception of students on blended learning considering data science and machine learning. Campus Virtuales, 9(1), 125–135.
  • Sarıtepeci, M., & Çakır, H. (2015). The Effect of Blended Learning Environments on Student’s Academic Achievement and Student Engagement: A Study on Social Studies Course. Education and Science, 40(177), 203–216. https://doi.org/10.15390/EB.2015.2592
  • Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396–413.
  • https://doi.org/10.1016/j.compedu.2005.09.004
  • Signorell, A. (2024). DescTools: Tools for Descriptive Statistics. R package version 0.99.56. https://CRAN.R-project.org/package=DescTools
  • Song, L., Singleton, E. S., Hill, J. R., & Koh, M. H. (2004). Improving online learning: student perceptions of useful and challenging characteristics. The Internet and Higher Education, 7(1), 59–70. https://doi.org/10.1016/j.iheduc.2003.11.003
  • Vallée, A., Blacher, J., Cariou, A., & Sorbets, E. (2020). Blended learning compared to traditional learning in medical education: Systematic review and meta-analysis. J Med Internet Res, 22(8):e16504. https://doi.org/10.2196/16504
  • Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press. https://doi.org/10.1017/CBO9780511803932
  • Yu, Z. (2022). Sustaining student roles, digital literacy, learning achievements, and motivation in online learning environments during the COVID-19 Pandemic. Sustainability, 14(8), 4388. https://doi.org/10.3390/su14084388
  • Zhao, L., Hwang, W., & Shih, T. (2021). Investigation of the Physical Learning Environment of Distance Learning Under COVID-19 and Its Influence on Students’ Health and Learning Satisfaction. International Journal of Distance Education Technologies, 19(2), 77–98. https://doi.org/10.4018/IJDET.20210401.oa4
There are 53 citations in total.

Details

Primary Language English
Subjects Higher Education Studies (Other)
Journal Section Research Articles
Authors

Adelaide Freitas 0000-0002-4685-1615

António Jorge Neves 0000-0001-8435-5842

Floriano Viseu 0000-0002-8221-6870

Márcia Lemos-silva 0000-0001-5466-0504

Natacha Oliveira 0000-0002-2570-8916

Carolina Alves 0000-0003-0629-3759

Ana Luisa Abreu 0000-0003-0220-3963

Paula Carvalho 0000-0002-9463-4340

Early Pub Date September 6, 2025
Publication Date September 1, 2025
Submission Date March 14, 2025
Acceptance Date July 3, 2025
Published in Issue Year 2025 Volume: 12 Issue: 5

Cite

APA Freitas, A., Neves, A. J., Viseu, F., … Lemos-silva, M. (2025). University Students’ Perceptions of the B-Learning Methodology. Participatory Educational Research, 12(5), 133-153. https://doi.org/10.17275/per.25.67.12.5