Is learning anytime, anywhere a good
strategy for success? Identifying successful
spatial-temporal patterns of on-the-job
and full-time students
Xu Du and Juan Yang
National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
Department of Educational Technology, Boise State University, Boise, Idaho, USA, and
Department of Educational Technology, Boise State University College of Education, Boise, Idaho, USA and
National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, China
Purpose –Online learning is well-known by its ﬂexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning
anytime and anywhere inﬂuence learning outcomes are still unknown.
Design/methodology/approach –This study proposed concepts of time and location entropy to depict students’spatial-temporal patterns. A total of
5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporallearning
patterns, including the relationships between identiﬁed patterns and students’learning performance.
Findings –Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher
tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent ﬁndings
on students’spatial-temporal patterns, but partial ﬁndings could be supported by evidence in neural science or by cultural and geographical differences.
Research limitations/implications –A more accurate approach for categorizing position and location might be considered. Some ﬁndings need
more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as
ﬁnancial situation, motivation, personality and major) and the type of learning patterns.
Practical implications –The ﬁndings gained from this study can help improve the understandings of students’learning behavioral pattern s and
design as well as implement better online education programs.
Originality/value –This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and
Keywords Online learning, Student performance, Entropy analysis, Full-time students, On-the-job students, Spatial-temporal patterns
Paper type Research paper
With the rapiddevelopment of informationand communication
technology,online learning has ﬂourished in recentyears due to
the ﬂexibility of engaging anytime and anywhere. Sixty-ﬁve per
cent of higher education institutions in the USA have claimed
that online learning is critical to their long-term strategy (Allen
and Seaman, 2013). One report indicated that more than 6.3
million Americanstudents took at least one online coursein fall
2016 (Seamanet al.,2018). However,due to the lack of face-to-
face interactions, online learning is signiﬁcantly different from
traditional in-classroom learning. Online education often
requires speciﬁc management strategies and instructional
designs. Therefore, it is crucial for highereducation institutions
to understandonline learners’behavioral patterns to design and
implementbetter online education programs.
Although online instructors cannot directly observe how
students act or behave in asynchronous online learning
environments, online learning systems have the ability to track
and store student’s online activities,which makes it possible for
researchers to investigate students’learning patterns (Ramos
et al., 2016:Kahan et al.,2017:Hung et al.,2019). Using
learning management system (LMS) data to assist in
investigating learning patterns is identiﬁed as one of the most
popular topics in online learning (Papamitsiou and
Economides, 2014;Duet al.,2019).
The current issue and full text archive of this journal is available on
Emerald Insight at: www.emeraldinsight.com/2398-6247.htm
Information Discovery and Delivery
47/4 (2019) 173–181
© Emerald Publishing Limited [ISSN 2398-6247]
Received 7 September 2019
Revised 23 September 2019
Accepted 24 September 2019