nach oben

Journal of Computing in Higher Education

Erschienen in:

03.04.2018

Online learning in higher education: exploring advantages and disadvantages for engagement

verfasst von: Amber D. Dumford, Angie L. Miller

Erschienen in: Journal of Computing in Higher Education | Ausgabe 3/2018

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

As the popularity of online education continues to rise, many colleges and universities are interested in how to best deliver course content for online learners. This study explores the ways in which taking courses through an online medium impacts student engagement, utilizing data from the National Survey of Student Engagement. Data was analyzed using a series of ordinary least squares regression models, also controlling for relevant student and institutional characteristics. The results indicated numerous significant relationships between taking online courses and student engagement for both first-year students and seniors. Those students taking greater numbers of online courses were more likely to engage in quantitative reasoning. However, they were less likely to engage in collaborative learning, student-faculty interactions, and discussions with diverse others, compared to their more traditional classroom counterparts. The students with greater numbers of online courses also reported less exposure to effective teaching practices and lower quality of interactions. The relationship between these engagement indicators and the percentage of classes taken online suggests that an online environment might benefit certain types of engagement, but may also be somewhat of a deterrent to others. Institutions should consider these findings when designing online course content, and encourage faculty to contemplate ways of encouraging student engagement across a variety of delivery types.

Vorheriger Artikel Acceptance and usage of mobile assisted language learning by higher education students

Nächster Artikel A DEMATEL method in identifying design requirements for mobile environments: students’ perspectives

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

Allen, E., & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. Babson Park, MA: Babson Survey Research Group.

Anaya, G. (1999). College impact on student learning: Comparing the use of self-reported gains, standardized test scores, and college grades. Research in Higher Education, 40, 499–526.CrossRef

Astin, A. W. (1993). What matters in college? Four critical years revisited. San Francisco, CA: Jossey-Bass.

Baird, L. (2005). College environments and climates: Assessments and their theoretical assumptions. Higher Education: Handbook of Theory and Research, 10, 507–537.

Braten, I., & Streomso, H. I. (2006). Epistemological beliefs, interest, and gender as predictors of Internet-based learning activities. Computers in Human Behavior, 22(6), 1027–1042.CrossRef

Cabrera, A. F., Crissman, J. L., Bernal, E. M., Nora, A., Terenzini, P. T., & Pascarella, E. T. (2002). Collaborative learning: Its impact on college students’ development and diversity. Journal of College Student Development, 43(1), 20–34.

Chen, P. D., Lambert, A. D., & Guidry, K. R. (2010). Engaging online learners: The impact of web-based learning technology on student engagement. Computers & Education, 54, 1222–1232.CrossRef

Cohen, V. L. (2003). Distance learning instruction: A new model of assessment. Journal of Computing in Higher Education, 14(2), 98–120.CrossRef

Dominguez, P. S., & Ridley, D. R. (2001). Assessing distance education courses and discipline differences in effectiveness. Journal of Instructional Psychology, 28(1), 15–19.

Drysdale, J. S., Graham, C. R., Spring, K. J., & Halverson, L. R. (2013). An analysis of research trends in dissertations and theses studying blended learning. Internet and Higher Education, 17, 90–100. https://doi.org/10.1016/j.iheduc.2012.11.003.CrossRef

Evans, C. (2014). Twitter for teaching: Can social media be used to enhance the process of learning? British Journal of Educational Technology, 45(5), 902–915.CrossRef

Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: Sage Publications.

Friedman, J. (2014). Online education by discipline: A graduate student’s guide. Retrieved from http://www.usnews.com/education/online-education/articles/2014/09/17/online-education-by-discipline-a-graduate-students-guide.

Han, I., & Shin, W. S. (2016). The use of a mobile learning management system and academic achievement of online students. Computers & Education, 102, 79–89. https://doi.org/10.1016/j.compedu.2016.07.003.CrossRef

Hayek, J. C., Carini, R. M., O’Day, P. T., & Kuh, G. D. (2002). Triumph or tragedy: Comparing student engagement levels of members of Greek-letter organizations and other students. Journal of College Student Development, 43(5), 643–663.

Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36–53. https://doi.org/10.1016/j.compedu.2015.09.005.CrossRef

Hu, S., & Kuh, G. D. (2001). Computing experience and good practices in undergraduate education: Does the degree of campus ‘‘wiredness” matter? Education Policy Analysis Archives, 9(49). http://epaa.asu.edu/epaa/v9n49.html.CrossRef

Jacob, S., & Radhai, S. (2016). Trends in ICT e-learning: Challenges and expectations. International Journal of Innovative Research & Development, 5(2), 196–201.

Junco, R. (2012). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Computers & Education, 58(1), 162–171.CrossRef

Junco, R., Elavsky, C. M., & Heiberger, G. (2013). Putting Twitter to the test: Assessing outcomes for student collaboration, engagement, and success. British Journal of Educational Technology, 44(2), 273–287.CrossRef

Junco, R., Heiberger, G., & Loken, E. (2010). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted learning. https://doi.org/10.1111/j.1365-2729.2010.00387.x.CrossRef

Kent, C., Laslo, E., & Rafaeli, S. (2016). Interactivity in online discussions and learning outcomes. Computers & Education, 97, 116–128. https://doi.org/10.1016/j.compedu.2016.03.002.CrossRef

Kim, K. J., & Bonk, C. J. (2006). The future of online teaching and learning in higher education: The survey says…. Educause Quarterly, 4, 22–30.

Kuh, G. D. (2001). The National Survey of Student Engagement: Conceptual framework and overview of psychometric properties. Bloomington, IN: Indiana University, Center for Postsecondary Research.

Kuh, G. D., & Hu, S. (2001a). The effects of student-faculty interaction in the 1990s. Review of Higher Education, 24(3), 309–332.CrossRef

Kuh, G. D., & Hu, S. (2001b). The relationships between computer and information technology use, student learning, and other college experiences. Journal of College Student Development, 42, 217–232.

McMillan, J. H., & Schumacher, S. (2001). Research in education: A conceptual introduction. New York: Longman.

Miller, A. L. (2012). Investigating social desirability bias in student self-report surveys. Educational Research Quarterly, 36(1), 30–47.

Miller, A. L., Sarraf, S. A., Dumford, A. D., & Rocconi, L. M. (2016). Construct validity of NSSE engagement indicators (NSSE psychometric portfolio report). Bloomington, IN: Center for Postsecondary Research, Indiana University, School of Education. http://nsse.indiana.edu/pdf/psychometric_portfolio/Validity_ConstructValidity_FactorAnalysis_2013.pdf.

Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Belmont, CA: Wadsworth.

Murayama, K., & Elliot, A. J. (2009). The joint influence of personal achievement goals and classroom goal structures on achievement-relevant outcomes. Journal of Educational Psychology, 101(2), 432–447. https://doi.org/10.1037/a0014221.CrossRef

National Survey of Student Engagement. (2015). NSSE 2015 overview. Bloomington, IN: Indiana University, Center for Postsecondary Research.

Nelson Laird, T. F., Shoup, R., & Kuh, G. D. (2005). Measuring deep approaches to learning using the National Survey of Student Engagement. Paper presented at the annual meeting of the Association for Institutional Research, Chicago, IL. http://nsse.iub.edu/pdf/conference_presentations/2006/AIR2006DeepLearningFINAL.pdf.

Ormrod, J. E. (2011). Human learning (6th ed.). Upper Saddle River, NJ: Pearson.

Pace, C. R. (1980). Measuring the quality of student effort. Current issues in Higher Education, 2, 10–16.

Parsad, B., & Lewis, L. (2008). Distance education at degree-granting Postsecondary Institutions: 2006–2007 (NCES 2009–044). National Center for Education Statistics, Institute of Education Sciences. Washington, DC: US Department of Education. http://nces.ed.gov/pubs2009/2009044.pdf.

Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research (Vol. 2). San Francisco, CA: Jossey-Bass.

Pike, G. R. (1995). The relationship between self-reports of college experiences and achievement test scores. Research in Higher Education, 36(1), 1–22.CrossRef

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x.CrossRef

Pollack, P. H., & Wilson, B. M. (2002). Evaluating the impact of internet teaching: Preliminary evidence from American national government classes. PS. Political Science and Politics, 35(3), 561–566.CrossRef

Pukkaew, C. (2013). Assessment of the effectiveness of internet-based distance learning through the VClass e-Education platform. International Review of Research in Open and Distance Learning, 14(4), 255–276.CrossRef

Restauri, S. L., King, F. L., & Nelson, J. G. (2001). Assessment of students’ ratings for two methodologies of teaching via distance learning: An evaluative approach based on accreditation. ERIC document 460-148, reports-research (143).

Richardson, J. T. E., Morgan, A., & Woodley, A. (1999). Approaches to studying distance education. Higher Education, 37, 23–55.CrossRef

Robinson, C. C., & Hullinger, H. (2008). New benchmarks in higher education: Student engagement in online learning. Journal of Education for Business, 84(2), 101–108.CrossRef

Serwatka, J. A. (2002). Improving student performance in distance learning courses. Technological Horizons in Education THE Journal, 29(9), 48–51.

Shuey, S. (2002). Assessing online learning in higher education. Journal of Instruction Delivery Systems, 16, 13–18.

Stallings, D. (2002). Measuring success in the virtual university. The Journal of Academic Librarianship, 28, 47–53.CrossRef

Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon.

Tess, P. A. (2013). The role of social media in higher education classes (real and virtual)—A literature review. Computers in Human Behavior, 29(3), A60–A68.CrossRef

Thurmond, V., & Wambach, K. (2004). Understanding interactions in distance education: A review of the literature. International Journal of Instructional Technology & Distance Learning, 1, 9–33. http://www.itdl.org/journal/Jan_04/article02.htm.

Tomei, L. A. (2006). The impact of online teaching on faculty load: Computing the ideal class size for online courses. Journal of Technology and Teacher Education, 14, 531–541.

Umbach, P. D. (2007). How effective are they? Exploring the impact of contingent faculty on undergraduate education. Review of Higher Education, 30(2), 91–123. https://doi.org/10.1353/rhe.2006.0080.CrossRef

U.S. Department of Education, National Center for Education Statistics. (2016). Digest of education statistics, 2014 (NCES 2016-006), Table 311.15. Retrieved from https://nces.ed.gov/fastfacts/display.asp?id=80.

Whitley, B. E. (2002). Principles of research in behavioral science (2nd ed.). New York, NY: Routlegde.

Wijekumar, K., Ferguson, L., & Wagoner, D. (2006). Problems with assessment validity and reliability in wed-based distance learning environments and solutions. Journal of Educational Multimedia and Hypermedia, 15(2), 199–215.

Wojciechowski, A., & Palmer, L. B. (2005). Individual student characteristics: Can any be predictors of success in online classes? Online Journal of Distance Learning Administration, 8(2), 13.

Wu, W., Wu, Y. J., Chen, C., Kao, H., & Lin, C. (2012). Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59, 817–827. https://doi.org/10.1016/j.compedu.2012.03.016.CrossRef

Xu, D., & Smith Jaggars, S. (2013). Adaptability to online learning: Differences across types of students and academic subject areas (CCRC Working Paper). New York, NY: Teachers College, Columbia University. Retrieved from http://ccrc.tc.columbia.edu/publications/adaptability-to-online-learning.html.

Zhou, L., & Zhang, D. (2008). Web 2.0 impact on student learning process. In K. McFerrin et al. (Eds.), Proceedings of society for information technology and teacher education international conference (pp. 2880–2882). Chesapeake, VA: AACE.

Titel: Online learning in higher education: exploring advantages and disadvantages for engagement
verfasst von: Amber D. Dumford
Angie L. Miller
Publikationsdatum: 03.04.2018
Verlag: Springer US
Erschienen in: Journal of Computing in Higher Education / Ausgabe 3/2018
Print ISSN: 1042-1726
Elektronische ISSN: 1867-1233
DOI: https://doi.org/10.1007/s12528-018-9179-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2018

Acceptance and usage of mobile assisted language learning by higher education students

The design, development, and implementation of student-facing learning analytics dashboards

How ready are our students for technology-enhanced learning? Students at a university of technology respond

The importance of students’ motivational dispositions for designing learning analytics

The role of behavioral expectation in technology acceptance: a CEGEP case study

Cyberloafing in IT classrooms: exploring the role of the psycho-social environment in the classroom, attitude to computers and computing courses, motivation and learning strategies