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Cited by 96Online and Blended LearningOnline Learning and AnalyticsEducational Outcomes and Influences

Quality of e-learners’ time and learning performance beyond quantitative time-on-task

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Romero, Margarida, Barberà, Elena·2011·The International Review of Research in Open and Distributed Learning
DOI 10.19173/irrodl.v12i5.999
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Abstract

Along with the amount of time spent learning (or time-on-task), the quality of learning time has a real influence on learning performance. Quality of time in online learning depends on students’ time availability and their willingness to devote quality cognitive time to learning activities. However, the quantity and quality of the time spent by adult e-learners on learning activities can be reduced by professional, family, and social commitments. Considering that the main time pattern followed by most adult e-learners is a professional one, it may be beneficial for online education programs to offer a certain degree of flexibility in instructional time that might allow adult learners to adjust their learning times to their professional constraints. However, using the time left over once professional and family requirements have been fulfilled could lead to a reduction in quality time for learning. This paper starts by introducing the concept of quality of learning time from an online student-centred perspective. The impact of students’ time-related variables (working hours, time-on-task engagement, time flexibility, time of day, day of week) is then analyzed according to individual and collaborative grades achieved during an online master’s degree program. The data show that both students’ time flexibility (r = .98) and especially their availability to learn in the morning are related to better grades in individual (r = .93) and collaborative activities (r = .46).

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  • For adult e-learners, evening time-slot is closely related to better academic performance in collaborative activities (r = 0.6).

    “Romero and Barber` a (2011) observed the time spent by adult e-learners on learning activities and reported a close relationship between evening time-slot and better academic performance ( r = 0 . 6) in collaborative activities, whereas both morning ( r = 0 . 9) and evening ( r = 0 . 8) were closely related to academic performance for individual activities.”

    From When Do Learners Study? (2022) · cites this paper as Romero and Barber` a (2011)

  • For adult e-learners, both morning (r = 0.9) and evening (r = 0.8) are closely related to academic performance for individual activities.

    “Romero and Barber` a (2011) observed the time spent by adult e-learners on learning activities and reported a close relationship between evening time-slot and better academic performance ( r = 0 . 6) in collaborative activities, whereas both morning ( r = 0 . 9) and evening ( r = 0 . 8) were closely related to academic performance for individual activities.”

    From When Do Learners Study? (2022) · cites this paper as Romero and Barber` a (2011)

  • Romero and Barberà (2011) found adult e-learners' academic performance more strongly related to morning and evening.

    “This is in contrast to the observation by Romero and Barber` a (2011), where adult e-learners' academic performance was more strongly related to morning and evening.”

    From When Do Learners Study? (2022) · cites this paper as Romero & Barberà, 2011

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Research works printed but not linked: 32

  1. Baddeley, A., Hatter, I., Scott, D., & Snashall, A. (1970). Memory and time of day. Quarterly Journal of Experimental Psychology, 22, 605-60.
  2. Belmont, J. M., & Butterfield, E. C. (1971). Learning strategies as determinants of memory deficiencies. Cognitive Psychology, 2(4), 411-420.
  3. Boisot, M. (1998). Knowledge assets: Securing competitive advantage in the information economy. Oxford: Oxford University Press.
  4. Chi, M. T. H., & Glaser, R. (1985). Problem solving ability. In R. Sternberg (Ed.), Human abilities: An information-processing approach (pp. 227-257). San Francisco: W. H. Freeman & Co. Collis, B. (1998). New didactics for university instruction: Why and how? Computers and Education, 31(4), 373-393.
  5. Collis, B., Vingerhoets, J., & Moonen, J. (1997). Flexibility as a key construct in European training: Experiences from the TeleScopia project. British Journal of Educational Technology, 28(3), 199-217.
  6. De Boer, W., & Collis, B. (2005). Becoming more systematic about flexible learning: Beyond time and distance. ALT-J, 13(1), 33-48.
  7. Dev, P. C. (1997). Intrinsic motivation and academic achievement: What does their rela- tionship imply for the classroom teacher? Remedial and Special Education, 18(1), 12-19.
  8. Douthitt, R. A. (2000). Time to do the chores? Factoring home production needs into mea- sures of poverty. Journal of Family and Economic Issues, 21(1), 7-22.
  9. Ehlers, U. (2004). Quality in e-learning from a learner's perspective. European Journal of Open Distance and E-Learning. Retrieved from http://www.eurodl.org/index.ph p?p=archives&year=2004&halfyear=1&article=101
  10. Felix, U. (2001). A multivariate analysis of students' experience of web-based learning. Australian Journal of Educational Technology, 17(1), 21-36.
  11. Greenwald, A. G., & Gilmore, G. M. (1997b). No pain, no gain? The importance of mea- suring course workload in student ratings of instruction. Journal of Educational Psychology, 89(4), 743-751.
  12. Grundspenkis, J., Lavendels, J., Novickis, L., & Shitikov, V. (2006, February). Compat- ibility of the principles of computer-based and classical teaching. Proceedings of the International Conference on Web-based communities, San Sebastian, Spain.
  13. Harasim, L., (1990). Online education: An environment for collaboration and intellectual amplification. In L. Harasim (Ed.), Online education: Perspectives on a new envi- ronment (pp. 133-169). New York: Praeger.
  14. Hinrichsen, J. J. (1972). Prediction of grade point average from estimated study behaviors. Psychological Reports, 31(3), 974-975.
  15. Kanuka, H. (2002). A principled approach to facilitating diverse strategies for web-based distance education. Journal of Distance Education, 17(2), 71-87.
  16. Kasprzak, J., & Nixon, M. (2004). Cheating in cyberspace: Maintaining quality in online ed- ucation. American Association of Computing in Education Journal, 12(1), 85-99.
  17. Kobasigawa, A., & Metcalf-Haggert, A. (1993). Spontaneous allocation of study time by first-and third-grade children in a simple memory task. The Journal of Genetic Psychology, 154, 223-235.
  18. Kolari, S., Savander-Ranne, C., & Viskari, E-L. (2008) Learning needs time and effort: A time-use study of engineering students. European Journal of Engineering Educa- Quality of Learners' Time and Learning Performance Beyond Quantitative Time-on-Task Romero and Barberà Vol 12 | No 5 Research Articles June 2011 136 tion, 33(5-6), 483-498.
  19. Lambropoulos, N., Faulkner, X., & Culwin, F. (2011). Supporting social awareness in collaborative e-learning. British Journal of Educational Technology, 42. doi: 10.1111/j.1467-8535.2011.01184.x · DOI
  20. Levinsen, K. (2006). Collaborative on-line teaching: The inevitable path to deep learning and knowledge sharing? Electronic Journal of E-learning, 4(1), 41-48.
  21. Lewis, G., Robertson, I., Watson, P., & Datta, A. (1998). Circadian rhythm of sleepiness and vigilance studied in man. Journal of Physiology, 506.
  22. Romero, M., & Lambropoulos, N. (2011). Internal and external regulation to support knowledge construction and convergence in CSCL. Electronic Journal of Research in Educational Psychology, 9(1), 309-330.
  23. Schrum, L. (2002). Oh, what wonders you will see: Distance education past, present, and future. Learning and Leading with Technology, 30(3), 6-9.
  24. Schuman, G.E., E.M. Taylor, Jr., F. Rauzi, & B.A. Pinchak. 1985. Revegetation of mined land: Influence of topsoil depth and mulching method. Journal of Soil and Water Conservation, 40, 249-252.
  25. Sullivan, P. (2001). Gender differences and the online classroom: Male and female college students evaluate their experiences. Community College Journal of Research & Practice, 25(10), 805-818.
  26. Trautwein, U. (2007). The homework-achievement relation reconsidered: Differentiating homework time, homework frequency, and homework effort. Learning and In- struction, 17(3), 372-388.
  27. Valenta, A., Therriault, D., Dieter, M., & Mrtek R. (2001). Identifying student attitudes and learning styles in distance education. Journal of Asynchronous Learning Net- works, 5(2), 111-127.
  28. Van den Brande, L. (1994). Flexible and distance learning. Chichester: John Wiley & Sons.
  29. Vergidis, D., & Panagiotakopoulos, C. (2002). Student dropout at the Hellenic Open Uni- versity: Evaluation of the graduate program studies in education. International Review of Research in Open and Distance Learning, 3(2).
  30. Wellman, G. S., & Marcinkiewicz, H. (2004). Online learning and time-on-task: Impact of proctored vs. un-proctored testing. Journal for Asynchronous Learning Net- works, 8(4), 93-104.
  31. Wolk, S. (2001). The benefits of exploratory time. Educational Leadership, 59(2), 56-59.
  32. Zhang, D., Zhao, J. L., Zhou, L., & Nunamaker, J. F. Jr. (2004). Can e-learning replace classroom learning? Communications of the ACM, 47(5), 75-79.

Cited by

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  • When Do Learners Study?

    Sher, V., Hatala, M., & Gašević, D. (2022) · Journal of Learning Analytics · doi:10.18608/jla.2022.6697 · Cited by 12 · see citing works

    “This is in contrast to the observation by Romero and Barberà (2011), where adult e-learners’ academic performance was more strongly related to morning and evening.”
    BackgroundMethodResultInfluential

Authors
Romero, M., & Barberà, E.
Affiliations
Universitat Oberta de Catalunya (ES)
ORCID
Barberà, Elena
Venue
The International Review of Research in Open and Distributed Learning
Publisher
Athabasca University Press
Published
2011-06-29
Volume
12
Issue
5
Pages
125
ISSN
1492-3831 (ISSN-L)
DOI
10.19173/irrodl.v12i5.999
OpenAlex
W1491151700
Cited by
96
References
0
Citations by year
2025: 2 · 2024: 2 · 2023: 4 · 2022: 10 · 2021: 9 · 2020: 5 · 2019: 7 · 2018: 10 · 2017: 8 · 2016: 7 · 2015: 10 · 2014: 9 · 2013: 11 · 2012: 2
FWCI
33.85
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • The International Review of Research in Open and Distributed Learning (publishedVersion, cc-by)
  • HAL (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Érudit (Université de Montréal) (submittedVersion, cc-by)
Topics
  • Social Sciences › Education › Online and Blended Learning
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Education › Educational Outcomes and Influences
Keywords
online students, adult learners, time on task, temporal flexibility, time availability, online learning, student-centered learning, working hours, time of day, cooperative learning, learning performance, online master's degree

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