Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Journal of Computers in Education
Erschienen in: Journal of Computers in Education 4/2020

01.04.2020

Students’ performance in blended learning: disciplinary difference and instructional design factors

verfasst von: Minh Hien Vo, Chang Zhu, Anh Nguyet Diep

Erschienen in: Journal of Computers in Education | Ausgabe 4/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Significant enhancement in students’ learning performance has been noticed in blended learning courses. Yet, the differential effect of blended learning as a function of disciplinary difference has not widely been explored. Moreover, studies on the critical factors related to students’ performance measured by objective course grades are recognized to a lesser extent compared with those using self-reported or perceived learning achievement. In the present study, the effect of blended learning in hard and soft courses is discerned. Factors related to students’ performance measured by final course grades are unraveled, controlling for the effects of gender and prior learning achievement. The participants (N = 571) are students following blended learning courses at a public university in Vietnam. A questionnaire is employed to collect data, which is subject to confirmatory factor analysis and hierarchical regression analyses. The results show that students in soft disciplines obtain higher grades than peers in hard disciplines. Clear goals and expectations, material quality, and collaborative learning are significant predictors of students’ performance.
Vorheriger Artikel From organization to elaboration: relationships between university students’ online information searching experience and judgements
Nächster Artikel Direct or indirect effects from “perceived characteristic of innovation” to “intention to pay”: mediation of continuance intention to use e-learning

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
Akkoyunlu, B., & Yılmaz-Soylu, M. (2008). Development of a scale on learners' views on blended learning and its implementation process. The Internet and Higher Education, 11(1), 26–32.
Anders, A. (2015). Theories and applications of massive online open courses (MOOCs): The case for hybrid design. International Review of Research in Open and Distributed Learning, 16(6), 39–61.
Anderson, T., Rourke, L., Garrison, D., & Archer, W. (2001). Assessing teaching presence in a computer conferencing context. Journal of Asynchronous Learning, 5, 1–17.
Arbaugh, J. B. (2010). Sage, guide, both, or even more? An examination of instructor activity in online MBA courses. Computers & Education, 55(3), 1234–1244.
Arbaugh, J. B. (2013). Does academic discipline moderate CoI-course outcomes relationships in online MBA courses? The Internet and Higher Education, 17, 16–28.
Arbaugh, J. B., Cleveland-Innes, M., Diaz, S. R., Garrison, D. R., Ice, P., Richardson, J. C., et al. (2008). Developing a community of inquiry instrument: Testing a measure of the community of inquiry framework using a multi-institutional sample. The Internet and Higher Education, 11(3–4), 133–136.
Asarta, C. J., & Schmidt, J. R. (2017). Comparing student performance in blended and traditional courses: Does prior academic achievement matter? The Internet and Higher Education, 32, 29–38.
Asoodar, M., Vaezi, S., & Izanloo, B. (2016). Framework to improve e-learner satisfaction and further strengthen e-learning implementation. Computers in Human Behavior, 63, 704–716.
Ausburn, L. J. (2004). Course design elements most valued by adult learners in blended online education environments: An American perspective. Educational Media International, 41(4), 327–337.
Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44(9), 1175.
Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: From the general to the applied. Journal of Computing in Higher Education, 26(1), 87–122.
Biglan, A. (1973). The characteristics of subject matter in different academic areas. Journal of Applied Psychology, 57, 195–203.
Bjorklund, S. A., Parente, J. M., & Sathianathan, D. (2004). Effects of faculty interaction and feedback on gains in student skills. Journal of Engineering Education, 93(2), 153–160.
Borokhovski, E., Bernard, R. M., Tamim, R. M., Schmid, R. F., & Sokolovskaya, A. (2016). Technology-supported student interaction in post-secondary education: A meta-analysis of designed versus contextual treatments. Computers & Education, 96, 15–28.
Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281.
Cai, Z., Fan, X., & Du, J. (2017). Gender and attitudes toward technology use: A meta-analysis. Computers & Education, 105, 1–13.
Chang, H. H., & Chuang, S. S. (2011). Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator. Information & Management, 48(1), 9–18.
Chang, H. Y., Wang, C. Y., Lee, M. H., Wu, H. K., Liang, J. C., Lee, S. W. Y., et al. (2015). A review of features of technology-supported learning environments based on participants’ perceptions. Computers in Human Behavior, 53, 223–237.
Chyung, S. Y. Y. (2007). Age and gender differences in online behavior, self-efficacy, and academic performance. Quarterly Review of Distance Education, 8(3), 213–222.
Day, B. W., Lovato, S., Tull, C., & Ross-Gordon, J. (2011). Faculty perceptions of adult learners in college classrooms. The Journal of Continuing Higher Education, 59(2), 77–84.
Diep, N. A., Cocquyt, C., Zhu, C., & Vanwing, T. (2016). Predicting adult learners’ online participation: Effects of altruism, performance expectancy, and social capital. Computers & Education, 101, 84–101.
Eom, S. B., Wen, H. J., & Ashill, N. (2006). The determinants of students' perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215–235.
Fryer, L. K., & Bovee, H. N. (2018). Staying motivated to e-learn: Person-and variable-centred perspectives on the longitudinal risks and support. Computers & Education, 120, 227–240.
Garrison, D. R., & Vaughan, N. D. (2008). Blended learning in higher education: Framework, principles, and guidelines. New York: Wiley.
Garrison, D. R., Anderson, T., & Archer, W. (2003). A theory of critical inquiry in online distance education. Handbook of Distance Education, 1, 113–127.
Ginns, P., & Ellis, R. A. (2009). Evaluating the quality of e-learning at the degree level in the student experience of blended learning. British Journal of Educational Technology, 40(4), 652–663.
Gonulal, T. (2019). Missing data management practices in L2 research: The good, the bad and the ugly. Erzincan University Education Faculty Journal, 21(1), 1–18.
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. 3–21). San Francisco, CA: Pfeiffer.
Hung, M. L., & Chou, C. (2015). Students' perceptions of instructors' roles in blended and online learning environments: A comparative study. Computers & Education, 81, 315–325.
Jaggars, S. S., & Xu, D. (2016). How do online course design features influence student performance? Computers & Education, 95, 270–284.
Johnson, C. S. (2017). Collaborative technologies, higher order thinking and self-sufficient learning: A case study of adult learners. Research in Learning Technology, 25, 1981.
Keller, J. H., Hassell, J. M., Webber, S. A., & Johnson, J. N. (2009). A comparison of academic performance in traditional and hybrid sections of introductory managerial accounting. Journal of Accounting Education, 27(3), 147–154.
Kim, A. Y. A., & Kim, H. J. (2017). The Effectiveness of instructor feedback for learning-oriented language assessment: Using an integrated reading-to-write task for English for academic purposes. Assessing Writing, 32, 57–71.
Laurillard, D. (2012). Teaching as a design science: Building pedagogical patterns for learning and technology. New York: Routledge.
Law, K. M., Geng, S., & Li, T. (2019). Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence. Computers & Education, 136, 1–12.
Lee, L. T., & Hung, J. C. (2015). Effects of blended e-Learning: A case study in higher education tax learning setting. Human-centric Computing and Information Sciences, 5(1), 13.
Lee, S. J., Srinivasan, S., Trail, T., Lewis, D., & Lopez, S. (2011). Examining the relationship among student perception of support, course satisfaction, and learning outcomes in online learning. The Internet and Higher Education, 14(3), 158–163.
Lo, C. C. (2010). How student satisfaction factors affect perceived learning. Journal of the Scholarship of Teaching and Learning, 10, 47–54.
Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717.
Martin, A. J., Wilson, R., Liem, G. A. D., & Ginns, P. (2013). Academic momentum at university/college: Exploring the roles of prior learning, life experience, and ongoing performance in academic achievement across time. The Journal of Higher Education, 84(5), 640–674.
Martin, F., Wang, C., & Sadaf, A. (2018). Student perception of helpfulness of facilitation strategies that enhance instructor presence, connectedness, engagement and learning in online courses. The Internet and Higher Education, 37, 52–65.
Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1–47.
Milheim, K. L. (2012). Towards a better experience: Examining student needs in the online classroom through Maslow's hierarchy of needs model. Journal of Online Learning and Teaching, 8(2), 159.
Moore, M. G. (1989). Editorial: Three types of interaction. The American Journal of Distance Education., 3(2), 1–6.
Moskal, P., Dziuban, C., & Hartman, J. (2013). Blended learning: A dangerous idea? The Internet and Higher Education, 18, 15–23.
Mupinga, D. M., Nora, R. T., & Yaw, D. C. (2006). The learning styles, expectations, and needs of online students. College Teaching, 54(1), 185–189.
Owston, R., & York, D. N. (2018). The nagging question when designing blended courses: Does the proportion of time devoted to online activities matter. The Internet and Higher Education, 36, 22–32.
Paechter, M., Maier, B., & Macher, D. (2010). Students’ expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction. Computers & Education, 54(1), 222–229.
Pallant, J. (2007). SPSS survival manual. Checking the reliability of a scale. Crows Nest: Allen & Unwin.
Pelz, B. (2010). (My) three principles of effective online pedagogy. Journal of Asynchronous Learning Networks, 14(1), 103–116.
Poll, K., Widen, J., & Weller, S. (2014). Six instructional best practices for online engagement and retention. Journal of Online Doctoral Education, 1(1), 56–72.
Reaburn, P., Muldoon, N., & Bookallil, C. (2009). Blended spaces work based learning and constructive alignment: Impacts on student engagement. Same places, different space (pp. 820–831). Auckland: Ascilite.
Rienties, B., & Toetenel, L. (2016). The impact of learning design on student behaviour, satisfaction and performance: A cross-institutional comparison across 151 modules. Computers in Human Behavior, 60, 333–341.
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.
Rovai, A. P., & Barnum, K. T. (2007). On-line course effectiveness: An analysis of student interactions and perceptions of learning. International Journal of E-Learning & Distance Education/Revue internationale du e-learning et la formation à distance, 18(1), 57–73.
Schafer, J. L., & Graham, J. W. (2002). Missing data: our view of the state of the art. Psychological Methods, 7(2), 147.
Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Computers & Education, 55(4), 1721–1731.
Shea, P., & Bidjerano, T. (2012). Learning presence as a moderator in the community of inquiry model. Computers & Education, 59(2), 316–326.
Shea, P., Hayes, S., Smith, S. U., Vickers, J., Bidjerano, T., Pickett, A., et al. (2012). Learning presence: Additional research on a new conceptual element within the Community of Inquiry (CoI) framework. The Internet and Higher Education, 15(2), 89–95.
Shukor, N. A., Tasir, Z., & Van der Meijden, H. (2015). An examination of online learning effectiveness using data mining. Procedia-Social and Behavioral Sciences, 172, 555–562.
Smith, G. G., Heindel, A. J., & Torres-Ayala, A. T. (2008). E-learning commodity or community: Disciplinary differences between online courses. The Internet and Higher Education, 11(3–4), 152–159.
Tsai, P. S., Tsai, C. C., & Hwang, G. J. (2012). Developing a survey for assessing preferences in constructivist context-aware ubiquitous learning environments. Journal of Computer Assisted Learning, 28(3), 250–264.
Vo, H. M., Zhu, C., & Diep, N. A. (2017). The effect of blended learning on student performance at course-level in higher education: A meta-analysis. Studies in Educational Evaluation, 53, 17–28.
Volchok, E. (2019). Differences in the performance of male and female students in partially online courses at a community college. Community College Journal of Research and Practice, 43(12), 904–920.
Walker, S. L., & Fraser, B. J. (2005). Development and validation of an instrument for assessing distance education learning environments in higher education: The Distance Education Learning Environments Survey (DELES). Learning Environments Research, 8(3), 289–308.
Wang, M. J. (2010). Online collaboration and offline interaction between students using asynchronous tools in blended learning. Australasian Journal of Educational Technology, 26(6), 830–846.
White, S., & Liccardi, I. (2006, October). Harnessing insight into disciplinary differences to refine e-learning design. In: Proceedings in Frontiers in Education, 36th Annual Conference (pp. 5–10). IEEE.
Wilson, B. G., Ludwig-Hardman, S., Thornam, C. L., & Dunlap, J. C. (2004). Bounded community: Designing and facilitating learning communities in formal courses. The International Review of Research in Open and Distributed Learning, 5(3), 1–22.
Wu, J. H., Tennyson, R. D., & Hsia, T. L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), 155–164.
Xie, K., & Ke, F. (2011). The role of students' motivation in peer-moderated asynchronous online discussions. British Journal of Educational Technology, 42(6), 916–930.
Xu, D., & Jaggars, S. S. (2014). Performance gaps between online and face-to-face courses: Differences across types of students and academic subject areas. The Journal of Higher Education, 85(5), 633–659.
Yang, S. H. (2016). Conceptualizing effective feedback practice through an online community of inquiry. Computers & Education, 94, 162–177.
Zacharis, N. Z. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. The Internet and Higher Education, 27, 44–53.
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82–91.
Metadaten
Titel
Students’ performance in blended learning: disciplinary difference and instructional design factors
verfasst von
Minh Hien Vo
Chang Zhu
Anh Nguyet Diep
Publikationsdatum
01.04.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Computers in Education / Ausgabe 4/2020
Print ISSN: 2197-9987
Elektronische ISSN: 2197-9995
DOI
https://doi.org/10.1007/s40692-020-00164-7

Weitere Artikel der Ausgabe 4/2020

Journal of Computers in Education 4/2020 Zur Ausgabe

OriginalPaper

Direct or indirect effects from “perceived characteristic of innovation” to “intention to pay”: mediation of continuance intention to use e-learning

OriginalPaper

From organization to elaboration: relationships between university students’ online information searching experience and judgements

OriginalPaper

Virtual learning communities (VLCs) rethinking: influence on behavior modification—bullying detection through machine learning and natural language processing

OriginalPaper

Exploring the role of EFL learners’ online self-regulation profiles in their social regulation of learning in wiki-supported collaborative reading activities

OriginalPaper

Learners’ perceptions and experiences of studying psychology online

Premium Partner

    Bildnachweise