Improving a Mobile Learning Companion for Self-regulated Learning
using Sensors
Haeseon Yun
1
, Albrecht Fortenbacher
1
and Niels Pinkwart
2
1
University of Applied Science in Berlin, Wilhelminenhofstr 75a, 12459 Berlin, Germany
2
Humboldt University of Berlin, Rudower Chaussee 25, 12489 Berlin, Germany
Keywords:
Intelligent Tutoring System, Learning Companion, Self-regulated Learning, Mobile Learning, Sensor-based
Learning, Pervasive Learning.
Abstract:
The ability to efficiently manage learning is linked to positive learning experience and outcome. However, at-
taining the ability of self-regulating is not a matter of course for students and it requires an external assistance.
A learning companion stemming from intelligent tutoring system (ITS) has non-authoritative, co-present ef-
fect as a learning support and with available sensor technology, self-regulated learning can be better promoted.
Sensor enhanced learning companion can detect learning states, learning behaviours and context and provide
valuable feedback to learners to increase their awareness of learning progress and to effectively manage their
learning. Considering the mobility, autonomy of learners along together with current trend in open online
learning resources and contents, available embedded sensors are suitable for realising the concept of learning
companion for self-regulated learning. The paper reviews a self-regulated learning concept, a learning com-
panion pedagogy and proposes sensor technology for self-regulated learning and learning companion design
considerations.
1 INTRODUCTION
Successful learning does no longer depend on lack
of learning resources or contents nor types of tech-
nology, whereas it remains on availability of learning
support and learner’s ability to motivate and manage
learning. Learner’s determination and regulation on
what to know and how to learn becomes an especially
important skill for learners with constant distractions,
especially when students tackle their learning con-
tents in various places and time of their choice. Hase
and colleagues advocated the need of learner centric
learning environment approach (Hase and Kenyon,
2001) and various recent research (Fernandez-Rio
et al., 2017; Lee and Recker, 2017; Kizilcec et al.,
2017; Guy et al., 2017; Mega et al., 2014) discussed
the importance of self-regulated learning for success-
ful learning.
The aim of this position paper is to discuss the
possibility of promoting self-regulated learning us-
ing a learning companion pedagogy and the possibil-
ity of improvement of a learning companion for self-
regulated learning using current available sensor tech-
nology. The paper will first present previous studies
on self-regulated learning and learning companion.
Following the literature review, sensors which can
provide information on the state of the self-regulated
learning are discussed to suggest the feasibility of
self-regulated learning support using current sensor
technology. Lastly, based on the literature on learning
companion and newly found limitation on sensors for
self-regulated learning state detection, the paper dis-
cuss the design consideration for a learning compan-
ion to facilitate self-regulated learning support using
sensors.
2 STATE OF THE ART
2.1 Self-regulated Learning
Self-regulated learning is a process where a learner
autonomously converts intellectual competence to
academic proficiency through control of thoughts,
feelings and behaviours based on a set goal (Zim-
merman, 2002). When faced with a learning task, a
self-regulated learner will actively follow four phases
explicitly or in a tacit way (Pintrich, 2004) : 1) fore-
thought, planning and activation, 2) monitoring, 3)
Yun H., Fortenbacher A. and Pinkwart N.
Improving a Mobile Learning Companion for Self-regulated Learning using Sensors.
DOI: 10.5220/0006375405310536
In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017), pages 531-536
ISBN: 978-989-758-239-4
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
control and 4) reaction and reflection. All phases
concern learner’s cognition, motivation, behaviour as-
pects along with context for external learning sur-
roundings. Self-regulated learning includes how to
attain knowledge better as well as how to control
learners’ emotion and motivation. Therefore, meta-
cognition which deals with knowing how to cogni-
tively learn is a component of a self-regulated learn-
ing framework as well as meta-motivation (how to
motivate oneself).
Although having self-regulated learning skills is
helpful, challenges remain. Zimmerman (2002) dis-
cussed few instructors engage learners to be equipped
with effective self-regulated learning skills and learn-
ers are not often provided with choices to choose their
own learning task. Trainings for self-regulated learn-
ing ability are available, yet trainings are mainly pro-
vided within a specific learning content or as a general
skill which are difficult for learners to apply in other
tasks of their choice.
As a mean to facilitate self-regulated learning,
Azevedo and Cromley (Azevedo and Cromley, 2004)
advocated the possible integration of self-regulated
learning process in a learning companion which pro-
vide a feeling of non-authoritative, friendly pedagog-
ical support for learning enhancement.
2.2 Learning Companion
Learning companion is a non-authoritative educa-
tional agent that has human characteristics to facili-
tate social learning. The concept of learning compan-
ion derived from an intelligent tutoring system (ITS)
and the term, pedagogical agent is interchangeably
used in related research. The main role of ITS is pro-
viding an appropriate one-on-one feedback to learn-
ers by taking a specific role (domain export, student,
pedagogical or interface) (Wenger, 1987). Similar to
other ITS, the study on learning companion explains
the role of learning companion as a peer and available
collaborator that encourages students to learn cooper-
atively (Goodman et al., 2016). Additionally, another
study introduces three roles of learning companion as
competition, suggestion and collaboration. Competi-
tion refers to competing with peer in a form of com-
paring their work with each other upon independent
work (Chan and Baskin, 1988). Suggestion role is to
collaboratively work and observe each other’s work.
Collaboration role refers to collaborative work with
shared responsibility.
The difference between ITS and learning compan-
ion is not easily distinguishable as the common in-
teraction behaviours such as coherent dialogue with
a student and pedagogical decision making regard-
ing on what information and when to provide can
be found in studies in ITS, pedagogical agent and
learning companion (Johnson et al., 2000). 5 types
of learning companions (PHelpS, People Power, Dis-
tributed West, LC and SAM) were reviewed and they
reveal unique interaction characteristics between a
system and a learner:
PHelpS (Peer Help System) : It offers assistance
to find others to provide help in learning and it me-
diates communication without being directly in-
volved in learning activities (Greer et al., 1998).
People Power : It questions students to seek an-
swers by themselves and interacts with a student
as a co-learner (Dillenbourg and Self, 1992).
Distributed West : It acts as a non-adaptive coach,
allowing students to see each other in a system
and play as learning companions (Chan et al.,
1992) .
LC : It initiates a dialogue and starts with a rap-
port building. During learning, it intervenes and a
learners is provided with learning status and learn-
ing strategy use (VanLehn et al., 2016).
SAM : It has learner’s characteristics such as age
and communicate style and initiates dialogue with
a learner. It takes turns to communicate and
encourages a learner with compliments (Ryokai
et al., 2003).
Learning companion guides learners through
communication (PHelpS, People Power, LC, LuCy
and Sam) and it uses various pedagogical strategies
to motivate and provide opportunities for learners to
reflect (LC, LuCy and SAM). The most distinctive
difference between other ITS and learning compan-
ion is the non-authoritative, co-learner characteris-
tics. Unlike ITS, students perceive learning com-
panion as a fellow student or peer who has similar
knowledge level (Ryokai et al., 2003). It also cares
about a learner’s progress and shows social connec-
tion by working together and shows emotions to make
learners interact with them continuously (Woolf et al.,
2009). However, the current research on learning
companion is limited to a single subject (domain),
therefore the design of learning companion which can
integrate numerous domains continuously to stay as a
lifelong friend should be explored (Chou et al., 2003).
The following section explores mobile device plat-
form for learning companion.
2.3 Mobile Learning Companion
Usage of mobile device in daily life has been grown
and the device has been penetrated in various aspects
in life. The statistics shows that more than 70 %
of people in America own smartphone in 2016 and
smartphone dependency over time is gradually grow-
ing (PewResearchCenter, 2017). In Europe, the simi-
lar phenomena can be observed. The number of peo-
ple using internet on the move by using mobile de-
vices has grown to 57 % in 2015 compared to 36 % in
2012 (Eurostate, 2016).
For learning, mobile learning provides a person-
alised, unobtrusive, instant way for learners to access
learning materials and tools and extends the availabil-
ity of education to all people (Traxler and Kukulska-
Julme, 2005). Searching for information is possible
anytime using a mobile device and sharing informa-
tion became much more instant and intuitive. The
mobile device provides a rich learning contents in
inexpensive way to people and it serves a key role
to make information globally accessible (Wellman,
2007). People do not need to sit or even be in one
place to learn but they can learn whenever they want
and wherever they want.
The mobile device also proposes a distinctive op-
portunity to support learners continuously at the ap-
propriate time. Available technology in a mobile de-
vice allows learners to interact with others online,
share their work with others and with other devices in
learners’ household. Information can be found easily
and scheduling with alert function helps learners man-
age their time. The embedded sensors in a mobile de-
vice are also a beneficial means as they provide infor-
mation regarding on learner’s location and state. Cur-
rent learning companion systems are designed mainly
on stable devices (computer), which fails to consider
mobility of learners.
Goodman and colleagues proposed the impor-
tance of designing a learning companion which can
interact with learners in more natural way (Goodman
et al., 2016) and considering learners’ mobility in
learning environment can be fulfilled by investigating
a mobile technology. With the progress of technology
advancement, more affordable, refined sensors will be
embedded in a mobile device which will serve a good
tool to realise a learning companion.
3 SENSORS FOR A MOBILE
LEARNING COMPANION
Smartphone that people carry contains at least 15 sen-
sors which detect user’s behaviour and context which
can monitor learner state and context in unobtrusive
ways. This part proposes that sensors can support
self-regulated learning and how a learning companion
should be designed to serve as an effective interactive
co-learner.
3.1 Sensors for Self-regulated Learning
Based on the self-regulated learning framework (Pin-
trich, 2004), sensors that are used in health, learning
and everyday life have been reviewed and identified as
possible sensors to detect each self-regulated learning
state as shown in the Table 1.
Table 1: Sensors to detect self-regulated learning area and
phase (Cog. : Cognition, Motiv: Motivation, Prox.: Prox-
imity Sensor, HR : Heart Rate Sensor, Accel.: Accelerom-
eter, Mic.: Microphone, EDA: Electrodermal Activity Sen-
sor, Light: Ambient Light Sensor).
Cog. Motiv. Behaviour Context
1 Bluetooth
- - - Prox.
Light
2 Camera Camera Camera Camera
Prox. HR Prox. Mic.
Prox.
3 Mic. Mic. GPS Mic.
EDA Wifi Prox.
Touch Gyro
Accel. Baro
Gyro GPS
Prox. Wifi
Baro Accel.
4 HR Camera
EDA EDA - -
HR
Sensors such as camera can detect learner’s con-
trol phase (phase 2) for all areas(cognition, motiva-
tion, behaviour and context) of self-regulated learn-
ing. For instance, camera can recognise a learn-
ers’ gaze, a presence of a user and facial expression
(Horvitz and Apacible, 2003). Detection of learner’s
attention to learning material can be done through
gaze detection (Sharma et al., 2016). A wearable
camera can recognise learner’s behaviour, specifically
the task that he or she is involved in (Fathi et al.,
2012). Camera on a smartphone could detect phys-
iological state such as fatigue (He et al., 2013). Emo-
tion detection is possible with a camera by detect-
ing head position and movement (Woolf et al., 2009).
Mobile application, inSightDemo (shown in Figure 1)
uses front end camera to detect facial expression and
shows 7 states (neutral, happiness, surprise, anger,
disgust, fear and sadness).
Figure 1: InSightDemo (AppStore).
Microphone is also widely used to detect ambient
noise, context and activity of users. In one study, mi-
crophone is used to define learners’ activity by relat-
ing speaking behaviour with being in a conversation
(Siewiorek et al., 2003). It also detects and validates
the use of self-regulated learning strategy (phase 3).
For instance, learners’ positive self talk to boost their
self-efficacy (Bandura, 1997) and minimise negative
affect (Zeidner, 1998) to regulate their cognition and
motivation can be sensed by microphone.
Contextual information such as perceiving learning
environment can be detected through Bluetooth, prox-
imity sensor, camera and ambient light sensor. Prox-
imity and ambient light sensors detect the bright-
ness, artificial sources and the presence of other peo-
ple or object (Schmidt et al., 1999). Bluetooth used
in Miluzzo’s study detects the people who are using
the same mobile application and provides feedback to
learners to provide contextual information .
Motivation awareness and monitoring (phase 2) can
be detected using a heart rate sensor and galvanic
skin conductance (or electro dermal activity sensor so
called EDA) (Mandryk and Atkins, 2007). Especially
for cognition and motivation monitoring, heart rate
and EDA sensor can be used (Mandryk and Atkins,
2007). Even though current EDA sensor is restricted
in a wearable technology which needs to be resolved
for unobtrusive monitoring, the heart rate sensor has
been adopted in current smartphone using a rear end
camera (Samsung, 2016).
Location of a person and user’s activity inference
through detecting the location of the sensor device
can be realised through WiFi, GPS and touch sensor
(Kern and Schiele, 2003; Miluzzo et al., 2008; Ben-
Zeev et al., 2015; Hinckley et al., 2000). Learner’s
forfeiting behaviours can be detected using Wifi, GPS
and touch sensor as giving up refers to leaving the
place where they set to study and enabling a mobile
device which can distract them from learning. Also
learner’s action to change or leave a context to con-
trol their learning environment can also be detected
using GPS, WiFi, accelerometer and even gyro and
barometer sensors.
Comparison of heart rate and EDA between the
beginning and the end of the learning can also pro-
vide learners with awareness of their learning. Fur-
thermore, camera used for gaze and facial expression
can provide additional information on learner’s atten-
tion and affective reaction during learning. Context
detection and location information that have been al-
tered during learning can be used to evaluate the task
and learner’s behaviour. Overall, reaction and reflec-
tion stage (phase 4) of self-regulated learning can be
fostered by providing various combination of sensor
data, mentioned previously. However, various areas,
especially phase 1 of self-regulated learning, are dif-
ficult to correlate with sensors as they refer to percep-
tion of task, perception of context, task value, plan-
ning and goal orientation.
The sensor technology can provide non-invasive
information to support learning, especially for self-
regulated learning. Nonetheless, for a learning com-
panion to be served as a scaffold to facilitate learn-
ing, comprehensive self-regulated learning areas and
phases should be considered for design.
3.2 Design Consideration for a Sensor
based Learning Companion
The limitation from sensors integration for learning
support as discussed in the previous section and the
need for an adaptive feedback can be resolved by
introducing a learning companion pedagogy. For
instance, the learning companion system, Sam, en-
hanced learning experience by using sensors. Sam en-
gaged younger learners in a storytelling task by using
radio frequency tag embedded in a learning resource
and a microphone to take turns (Ryokai et al., 2003).
Sam integrated sensors technology to use a learning
resource cooperatively with a learner and also com-
municate with him or her in a way that a learner per-
ceives Sam as a peer and even a human.
Sensors adopted in a learning companion also de-
tect physiological state such as stress level and a com-
panion provides messages to relieve stress and con-
tinue engage in learning even learners are facing diffi-
cult tasks (Prendinger and Ishizuka, 2005). This em-
pathic companion co-exists with a learner and main-
tains a long relationship with a learner by providing
an emotional support.
For a learning companion to be successful in con-
necting with a learner and maintains a relationship,
design consideration should be made. Especially, the
design of learning companion should consider the
missing links to incorporate overall aspects of self-
regulated learning with emphasis on characteristics of
learning companion (non-authoritative, intuitive and
friendly). Based on the previous studies on learning
companion, desirable characteristics of learning com-
panion are as follows:
A learning companion should correspond to each
learner’s characteristics (Kim, 2007).
A learning companion should provide instruc-
tional advantages and provide encouragement to
learners (Goodman et al., 2016; Woolf et al.,
2009).
A learning companion should initiate dialogue
and force students to engage in reflection (Good-
man et al., 2016).
A learning companion should have a simple and
stylish visual with task and relation orientation
(Haake and Gulz, 2009).
Learners should regard a learning companion as
a fellow learner and furthermore a real human
(Ryokai et al., 2003).
Learners should enjoy interacting with it and have
positive perception of overall learning experience
(Woolf et al., 2009).
4 CONCLUSIONS
Learners, with numerous choices in educational con-
tents, can benefit by having self-regulated learning
skills as they can support learners to tackle various
obstacles that learners face during learning. Train-
ing for self-regulated learning skills is possible yet
the transfer of this skill in another domain is ques-
tionable and not actively enforced. Mobile device
that has technical capabilities to detect valuable in-
formation related to learning environment, behaviour,
cognition and motivation can facilitate learners to be-
come more capable people and have better learning
experience. However, as Hase and colleagues em-
phasised that learning is only possible when learn-
ers perceive learning as process of one’s improvement
and it is guided without threats (Hase and Kenyon,
2001), learning companion pedagogy can be an ideal
method to meet current technology enhanced learning
environment. Sensor technology can support learners
by providing information on their cognitive and emo-
tional state and guide them to choose an optimal be-
haviour and a suitable ambience for learning.
In this respect, this position paper reviews the con-
cept of self-regulated learning and a learning compan-
ion pedagogy and discusses available mobile sensors
which can be utilised to detect self-regulated learn-
ing aspects. Limitations of sensor utilisation for self-
regulated learning were disclosed and the paper pro-
poses design considerations for a sensor based learn-
ing companion by considering a learning companion
pedagogy. The proposed design consideration for a
learning companion is broad however, the aims of this
paper are to propose that 1) sensors can detect some
areas and phases of self-regulated learning which can
support learners to be aware of their learning progress
and environment and 2) a learning companion peda-
gogy should be integrated when designing an effec-
tive pedagogical support for learning.
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