The document presents a predictive model for mobile learning (mLearning) advancement created by the author. It outlines the current state of mLearning and proposes a methodology to forecast its evolution and relationships with contextual learning. Literature on the topic from 2008-2018 is analyzed using bibliometric networks and machine learning to identify terms frequently associated with mLearning and their usage trends over time. Results show terms like engineering education and augmented reality have increasing connections to mLearning in recent years.

1. Predictive Modeling Concerning
Mobile Learning Advance
Malinka Ivanova
Technical University of Sofia
College of Energy and Electronics
The author would like to thank the Research and Development Sector at the Technical
University of Sofia for the financial support.

2. The aims
1. The current state of mLearning to be outlined and a
predictive model for its implementation to be created;
2. A methodology for forecasting the directions for
evolvement of mLearning and its relationships with
contextual learning to be proposed

3. eLearning
Informatics
Informatics
Methods
IC
Technologies
Software and
Hardware
Products
eLearning
Technologies
Educational
Theories
• for better understanding the
static and dynamic features of a
system
Modeling algorithms
•to automate identification of
patterns and trends in the domain
of teaching and learning
Machine learning
approaches
•complex systems to be explored
and studied
Predictive modeling
with machine
learning
•to facilitate understanding the
challenging issues, assumptions
permission and decision making
Predictive analysis
Usage of Informatics methods
in context of mLearning

4. mLearning as future of eLearning
mLearning
bridges formal
and informal
learning
improves
collaborative
and
conversational
learning
stimulates
self-directed
and
personalized
learning
is a driving
force for open
teaching,
achieving
flexibility and
efficacy

5. Predictive modeling in mobile learning
based on machine learning
for predicting
the students’
performance
and
effectiveness
for
identifying
the students’
at-risk
for improving
retention
and
engagement

6. Proposed methodology
I procedure: Data search and extraction from SCOPUS
and bibliometric networks construction (VOSviewer)
II procedure: Creation of preparatory matrixes (with
data from VOSviewer)
III procedure: Building a predictive model forecasting
the effect of changes in the terms regarding the values
of occurrences and total link strengths and
dependences between years and occurrences (Octave)
IV procedure: Construction of a fuzzy inference system
for predicting the context of mobile learning usage
(FisPro)

7. Connected terms to the term mLearning
for 2018 year
mLearning in context O TLS mLearning in context O TLS
mLearning 506 1886
Collaborative/
Cooperative learning
12 49
eLearning 252 1394 Experimental learning 5 34
Engineering education 43 290 Adaptive learning 7 33
Higher education 34 165 Online learning 7 35
Learning through augmented
reality
34 164 Ubiquitous learning 21 92
Game-based learning 28 154 Interactive learning 7 43
Personalized learning 7 39 Secondary schools 5 32
Learning in virtual reality 18 112 Distance education 5 23
Language learning 15 63 Informal learning 9 38
Technology enhanced learning 14 84 Flipped classroom 5 12
Problem-based learning 5 25 MOOC 13 77
Blended learning 12 66
Legend: O-occurrences; TLS-total link
strengths

8. Occurences and total link strengths for the term mLearning
during the examined period
2018 2017 2016 2015
O TLS O TLS O TLS O TLS
506 1886 433 1985 452 2279 441 2344
2014 2013 2012 2011
442 1902 383 1580 543 2821 419 1886
2010 2009 2008
463 2423 326 1779 253 728
Legend: O-
occurrences; TLS-
total link strengths

9. Bibliometric network for the term
mLearning for 2018 year

10. Results from linear regression
a) O/TLS for the term
mLearning
b) O/TLS for the term
blended learning
c) O/TLS for the term
learning with augmented
reality
d) O/Year for the term
mlearning
e) O/Year for the term
blended learning
f) O/Year for the term
learning with augmented
reality

11. Results from linear regression
g) O/TLS for the term game-
based learning
h) O/TLS for the term language
learning
i) O/TLS for the term informal
learning
j) O/Year for the term game-
based learning
k) O/Year for the term
language learning
l) O/Year for the term informal
learning

12. Results from linear regression
m) O/TLS for the term Higher
education
n) O/TLS for the term
engineering education
o) O/TLS for the term
personalized learning
p) O/Year for the term Higher
education
q) O/Year for the term
engineering education
r) O/Year for the term
personalized learning

13. Results
In Brief:
• If a comparison related to the usage of mLearning in
context of learning through augmented reality and
learning through games is performed, then the analysis
shows very close intersect and slope coefficients that
reflect on the similar line steepness.
• The topics related to mLearning through augmented
reality and games take the similar attention of
researchers.
• The lines steepness is positive that outlines an
increasing tendency for utilization of these terms in the
scientific production.

14. Results
mLearning is often applied in the context of:
• engineering education,
• Higher education,
• learning through augmented reality,
• game-based learning,
• learning in virtual reality,
• language learning,
• MOOCs,
• blended learning,
• collaborative/cooperative learning,
• informal learning,
• adaptive learning,
• online learning,
• personalized learning,
• interactive learning.

15. The constructed Fuzzy Inference System
and its response
The aim is to understand the future utilization of mobile technology for
teaching and learning – 125 rules are generated in FIS.

16. Results
• The terms mobile technology, learning and teaching are
examined for the period of eleven years – from 2008 to
2018 year.
• The final result shows different usage of the terms teaching
and learning in the context of mLearning during the
examined years.
• The tendency is that the term mobile technology will be
closer to the term learning than to the term teaching.
• Also, according to the selected values of the terms teaching
and learning in the constructed FIS could be found a
solution that is closer to teaching or to learning as well as
an approach for balanced utilization of mobile technology
in teaching and learning.

17. Conclusion
The proposed methodology and created predictive model are
useful for:
(1) gathering results about challenging issues and its further
understanding,
(2) for hypothesis construction and its acceptance/rejection
and
(3) for decision making taking account the found tendency.
In the context of this work, the reached findings outline:
• The explored topics by researchers in context of
mLearning and
• the tendency regarding their examination during the
years.
Such findings can give orientation to teachers and researchers
about the current state and can indicate the future
directions for research in the area of mLearning.

18. Thank you for your attention!
• Pictures are taken from:
• https://mimio.boxlight.com/mimiomobile-school-app/
• https://www.academicimpressions.com/blog/information-overload-to-collaborative-learning/
• https://www.cmswire.com/digital-workplace/8-augmented-reality-companies-changing-the-digital-
workplace/
• https://www.upsidelearning.com/blog/index.php/2011/06/21/mobile-learning-revolution-round-
up-of-our-best-mlearning-posts/
• https://www.sas.com/en_id/insights/analytics/machine-learning.html
• https://blog.commlabindia.com/elearning-development/instructional-design-mlearning