Handling Procrastination in Mobile Learning Environment
Proposal of Reminder Application for Mobile Devices
Aneta Bartuskova and Ondrej Krejcar
Dept. of Information Technologies, Faculty of Informatics and management, University of Hradec Kralove,
Rokitanskeho 62, Hradec Kralove, Czech Republic
Keywords: Mobile Learning, e-Learning, Procrastination, Forming of Habit.
Abstract: This paper deals with the issue of procrastination in e-learning. Suggested approach is based on
compensating e-learning shortcomings and applying principles of forming a habit. Technical
implementation is possible through use of mobile devices, incorporated in e-learning strategy. Respective
habit loop would consist of immediate trigger (delivered by a reminder application), desired behavior
(engagement in learning session) and immediate reward. Requirements on learning strategy, software and
hardware are discussed, as well as a reminder mechanism and relevant system of rewards. Data processing
in the reminder application is outlined for computing initial settings of the application.
1 INTRODUCTION
Procrastination is known as a tendency to delay
performance of important tasks, followed by feeling
of distress (Solomon and Rothblum, 1984). This
phenomenon is commonly found in the academic
domain and there are many studies focused
especially on undergraduate and graduate students. It
was proven that many students procrastinate in
relation to academic activities (Steel, 2007). This
can be significant issue in e-learning, as e-learning
courses tend to be mostly organized by student
himself. Procrastination in e-learning courses can
also emerge because online learning tasks can
quickly overwhelm students, as well as existence of
too much data and information to read and to
respond to (Roberts, 2003). Procrastination not only
causes discomfort or anxiety but also often results in
unsatisfactory performance (Solomon and
Rothblum, 1984).
M-learning or mobile learning brings many
opportunities for learning and e-learning as well.
Smart phones, tablets and other devices can be used
for enhancing learning experience for students and
teachers as well, therefore increase productivity and
learning results (Dittmar, 2013). Opportunities
offered by these mobile devices can be roughly
defined as:
 opportunity in the sense of mobility - it is
possible to bring the device along all the time
 opportunity in immediate use, trigger and
response - it is possible to use the device
anytime, the device can react according to its
pre-set functions without previous interaction
and user can respond immediately
 opportunity in form of a new platform for
applications - for smart phones, tablets and other
mobile devices with Android, iPhone and other
operating systems
 opportunity in usage of current location - limited
use for learning
Mobile learning as well as traditional web-based
e-learning suffers from absence of face-to-face
communication between teacher and student, and
also communication between students themselves.
Generally, e-learning in its online form lacks face-
to-face educational experience (Garrison et al.,
2003). Another issue of m-learning is the fact, that
learners can be more easily distracted in mobile
environment (Joo et al., 2013). Both e-learning and
mobile learning allows for managing learning
process solely by student, therefore increasing risk
of procrastination.
This paper explores a possibility of handling
procrastination in e-learning with use of mobile
devices.
220
Bartuskova A. and Krejcar O..
Handling Procrastination in Mobile Learning Environment - Proposal of Reminder Application for Mobile Devices.
DOI: 10.5220/0004960702200225
In Proceedings of the 6th International Conference on Computer Supported Education (CSEDU-2014), pages 220-225
ISBN: 978-989-758-022-2
Copyright
c
 2014 SCITEPRESS (Science and Technology Publications, Lda.)

2 PROPOSED APPROACH
Approach which is suggested in this paper for
dealing with procrastination is based on
compensating e-learning shortcomings and applying
principles of forming a habit. Technical
implementation is possible through use of mobile
devices, incorporated in e-learning strategy. Such
mobile learning would use advantage of both
mobility of the device and custom application linked
with existing e-learning system as well.
2.1 Need for a Reminder System
Challenges and opportunities, regarding
responsibility and control in teaching and learning,
are different from traditional learning. Crucial step
in ensuring successful outcome in e-learning is
having a learner accept responsibility for one´s
learning, in both knowledge aspect and cognitive
abilities which are needed for continuous learning
(Garrison et al., 2003). Continuous or routine
learning is more difficult for learner to maintain than
in traditional learning, because there are no
reminders in traditional face-to-face learning
sessions. With no reminders (only e.g. deadlines in
online e-learning platform, which has to be accessed
first in order to reveal this reminder), students can
easily put learning aside and engage in other
activities, therefore suffer from procrastination. The
opportunity of more effective reminder system lies
in usage of mobile devices, which will be discussed
further in section 3.4 Reminder Mechanism.
2.2 Forming a Habit
The ultimate goal of implementing a reminder
application into e-learning strategy should be
creation of a habit. With self-controlled learning
such as e-learning, the amount and distribution of
time dedicated to learning is usually not fixed and is
likely to be postponed continuously, ending in
procrastination. Solution to this problem can be
creation of a habit loop, with help of reminder
application. Duhigg specifies that we can create new
neurological routines and therefore a new pattern,
which will become automatic behavior. Forming a
habit loop involves three phases (Duhigg, 2012):
 trigger / cue - this is to be delivered by the
reminder application
 habit / routine - desired behavior (in this case
generally participation in learning)
 reward - important is that a reward must be
immediate after fulfilled learning session
This principle is similar to incentives and rewards in
social computing, on workplaces and other areas.
Reminders through mobile device would serve as
immediate incentive, followed by a small but also
immediate reward, e.g. gain of points towards a final
score. The immediate nature would solve problem
that effort level always drops following an
evaluation if the agent views the time until the next
evaluation as too long (Scekic et al., 2013). Figure 1
depicts suggested sequence of actions for habit loop
in e- learning.
trigger
habit
OR
reward
OR
Figure 1: Suggested habit loop for e- learning with use of
a mobile device as a reminder.
2.3 Adjusting e-Learning Strategy
There are three components of e-learning - enabling
technology, learning content and learning design.
Good e-learning is then a combination of technology
that works, meaningful content and effective
learning design (Fee, 2009). Incorporating reminder
system into e-learning strategy would:
 pertain into learning design
 be conditioned by learning content
 take an advantage of mobile technology
Integration into learning design would require
careful planning regarding time management,
estimated difficulty of learning sections and system
of rewards. It would be conditioned by amount,
diversity and structure of learning content. The
organization of content could be inappropriate for
immediate implementation of reminder system and
could need restructuring. Mobile technology is a
requirement for successful interference into learner´s
awareness without need of entering online course or
even without internet connection. This is an
advantage of application for mobile devices in
comparison with web-based application.
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3 OVERVIEW OF CONDITIONS
This section includes overview of basic conditions,
which are necessary for implementing mobile
reminder system to e-learning course. The reminder
application is primarily aimed at learners, with a
goal to engage them in learning process, but can also
be modified for active usage by instructors, e.g. for
inspiring them to continuous improvement of
learning content. Basic scenario discussed in this
article is concerned with only learners as users.
E‐learning
components
Conditionsfor
implementinga
remindersystem
Design
Content
Technology
Hardware
requirements
Software
requirements
Organizationof
content
Reminder
mechanism
System
ofrewards
Figure 2: Requirements for implementing mobile reminder
system in relation to three components of e-learning.
3.1 Hardware Requirements
In order to use advantage of mobile device, student
has to own such device. This should be less and less
pressing issue, as an adoption of mobile devices
such as smart phones or tablets increases. In year
2011 was surveyed that 35 percent of Americans
own a smart phone (Pew Internet & American Life
Project, 2011). However, implementation of
reminder system into e-learning still has to be
unobtrusive. The student must be able to perform the
course without reminder application and rewards,
which should be compensated in other form. Web-
based reminder system could be proxy for mobile
reminder system in a case of its absence.
3.2 Software Requirements
New software is needed for implementing mobile
reminder system into e-learning design. Android and
iOS applications for mobile devices are not
compatible, therefore two version of the same
application should be created, one for Android
platform and other for iPhone. The application
should cooperate with existing e-learning system /
platform / portal, so it can gather and update
required information for correct functionality of
mobile reminder application.
The application should be able to use this data
along with custom data added by user, in order to
manage its user´s schedule of learning sessions and
provide suitable reminders. These reminders should
be both visual and audible on mobile device.
In order to connect reminder application to
particular user´s account in e-learning system, a
connection needs to be established. Parameters of
this connection should arise from implementation
details, user should be prompted to provide these
parameters and of course security issues need to be
taken care of. Transfer of data from e-learning
system to reminder application could proceed by
wireless network without user´s intervention.
3.3 Organization of Content
The course organization should be adapted for use
by the reminder system. Ideally, proportional
distribution of learning content should be ensured.
This includes especially:
 consistent form of individual learning sections
 approximately equal estimated time required for
individual learning sections
 approximately equal difficulty of individual
learning sections
Proportional distribution of learning content
should provide for equally distributed rewards and
also easier evaluation of learners. More importantly,
structuring learning content into equal sections
makes planning of learning process much easier.
This arrangement also justifies usage of reminder
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mechanism. This is because equally time-demanding
and effort-demanding sections can be included into
schedule as the same activity and can create a habit.
3.4 The Reminder Mechanism
Reminder should be activated according to preferred
initial schedule settings. If user does not enter
learning course in defined time interval, reminder
should be activated repeatedly. There should be also
possibility for user to deactivate repeating reminder,
but only if he provides alternative time for it. In this
action the learner creates a commitment, which he
should try to fulfill.
Initial settings of reminder application would be
defined by user, allowing for user´s individual time
schedule. Furthermore, reminder application should
be able to analyze user data concerning time
schedule, which are expected to be developing
continuously, and adapt its reminders appropriately.
Adaptation can be carried out in reaction to start of
previous learning session. As previous appropriate
learning session can be taken:
 learning session performed on previous day
 learning session performed on previous working
day (in working days) or weekend (on weekends)
 learning session on previous day of the week
Preference of individual methods is dependent on
existing weekly schedule of learner, and therefore
should be part of initial settings. More complex
algorithm is also possible, which would decide most
suitable method on the basis of continuous
development regarding starts of sessions.
There should be also mechanism for deciding
successful fulfillment of learning session, and
deactivating current reminder. Frequency of
reminders is expected to be on daily basis, but could
be also adjusted in initial settings or automatically
generated based on previous successfully performed
learning sessions.
3.5 System of Rewards
System of rewards refers to the third phase of habit
loop. Rewards have to be immediate and satisfying.
Immediate rewards are usually much simpler to
implement in online environment than in traditional
environment. When learner interacts with e-learning
course, he can get immediate response from server,
based on performed activities. If learner
accomplishes desirable activity, e-learning system
can immediately deliver reward.
While implementing reminder application,
reminder should lead to desirable activity, and this
activity should result in appropriate reward delivered
to learner. This activity can be specified by:
 generally amount of time actively spent in
learning session (technique for measuring
activity must be further specified)
 fulfilling a daily quota, specified by learner
earlier (technique for measuring activity must be
further specified)
 completed learning section or tasks (conditions
for completion must be specified)
 accomplishing required learning sessions and
tasks before defined deadline
Immediate rewards can have various forms, e.g.:
 gain of points towards a final score, which has
influence on learner´s evaluation
 accomplishment badges, which can be visible to
other participants of the course, therefore
supporting competition
If learning content is proportionally distributed
among learning sessions and appropriately designed
and structuralized, rewards can be also proportional
and expectations therefore stable. This consistency
also contributes to creation of a habit.
4 DATA PROCESSING
Several ideas from previous section will be explored
in greater depth here, regarding especially data and
its processing. This relates also to communication
with e-learning system and gathering data from
learner in the reminder application.
4.1 Data from an e-Learning System
The mobile reminder application should be able to
gather data from relevant e-learning system in order
to function properly. This data includes:
 currently enrolled e-learning courses
 available learning sections (and subsections) in
each course
 estimated required time for individual learning
sections (and subsections)
 deadlines scheduled by e-learning system
The list contains only essential components and
can be further developed in case of extended
cooperation of the application and e-learning system.
4.2 Data Obtained from User
In order to adapt the reminder application to
individual time schedule of learners, another set of
data is needed from learner. Learner is a sole user of
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this application in context of this paper. This data
are to be obtained in the mobile application settings.
Learner should be prompted to provide this data
before using the application. This data can include:
 preferred time of the day for the reminder (can
be same for every day, different for working
days and weekends, or different for every day)
 enabling only particular days of the week for
reminders, therefore establishing expected
frequency for learning sessions
 possibility of dividing daily learning session into
more parts (and reminders)
 preferred time quota for learning per day / week,
in other words length of daily / weekly session,
in single number or range
 custom deadlines defined by learner
Learning
sections
DeadlinesLearningcourse
Subsections
Requiredtime
Datafrome‐learningsystem
Datafromuser(learner)
Preferred
startingtime
Availabledays Timequota
Deadlines
Daily
Weekly
Initialsettingsoftheapplication
Figure 3: Initial settings of the application.
4.3 Initial Settings of the Application
Data acquired automatically from e-learning system
and manually from learner should form initial
settings of the reminder application. The application
should have then implemented algorithms for
suggesting individual learning plan with relevant
distribution of reminders. Based on available data,
learner could choose between different learning
plans with relevant algorithms.
These learning plans can be changed or adjusted
later either automatically or customized by user.
Automatically e.g. in case of diversion from
expected values in time spent in learning sessions or
in case of change in available learning days.
Customized e.g. by choosing another algorithm or
inserting a new deadline. Two types of learning
plans are proposed as an example.
4.3.1 Minimum Quota with Deadline
This learning plan is based on proportional
distribution of learning sessions in every day
available till deadline. Proportional implies that
approximately the same amount of learning content
should be covered in every day. Every day available
means every day enabled by learner for learning
sessions. Deadline can be official from e-learning
system or custom defined by learner. Required
values for this plan are: number of learning session
and their estimated required time, days available for
learning and deadline (official or custom).
4.3.2 Maximum Quota in Minimum Time
This learning plan is suitable for quick learning - for
covering the most material in minimum time
possible. Required values for computing this plan
are: number of learning session and their estimated
required time, days available for learning and
defined quota for learning per day and per week.
4.4 Runtime Adaptation
At starting the learning session and at ending the
session, feedback should be sent to the reminder
application from e-learning system. This way, the
reminder application can be up-to-date and deliver
accurate performance. Information about starting the
session would deactivate current reminder and
information about ending it would allow computing
data about session length and fulfilled learning
sections. Information about performed learning
would continuously update distribution of learning
sessions and also reminders in the learning plan.
More sophisticated version of the mobile
reminder application could also recommend
alteration of the learning plan. This can be done
according to development of learning sessions,
preferred starting time of sessions and usual duration
of sessions. The application then needs to store user
data about past sessions. With data storage and
appropriate functions, more complex analyses can be
made, which would help learner to distribute
reminders more fittingly into one´s schedule.
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5 CONCLUSIONS
This paper presented idea of the mobile reminder
application for dealing with procrastination in
e-learning. Underlying principle for implementing
the mobile reminder application was the principle of
habit loop, which was chosen as an ideal instrument
for dealing with long-term procrastination.
Presented application of habit loop is possible
due to pervasiveness of mobile devices and its
immediate possibility of interaction. Ideas and
possibilities were discussed in relation to this
approach. Fundamental requirements were outlined
and substantial part of this section was devoted to
reminder mechanism and system of rewards. The
paper also considers basic schema of data processing
in the application. Implementation details were not
covered in this article, this will be subject of future
studies, as well as refinement of suggested ideas.
ACKNOWLEDGEMENTS
This work and the contribution were supported by:
(1) the project No. CZ.1.07/2.2.00/28.0327
Innovation and support of doctoral study program
(INDOP), financed from EU and Czech Republic
funds; (2) project “Smart Solutions in Ubiquitous
Computing Environments”, from the Grant Agency
of Excellence, University of Hradec Kralove, FIM,
Czech Republic; (3) project Smart Solutions for
Ubiquitous Computing Environments” from FIM,
University of Hradec Kralove.
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