Know the Mobile Learning Application Users

Transactional Distance Perspective

Pakapan Limtrairut, Stuart Marshall and Peter Andreae

School of Engineering and Computer Science, Victoria University of Wellington,

PO Box 600, Kelburn, Wellington, 6140, New Zealand

Keywords:

Distance Education, Mobile Learning, Transactional Distance Theory, Persona.

Abstract:

We developed a mobile learning application grounded on Transactional Distance Theory. The aim is to engage

learners and decrease their feelings of isolation and emptiness when learners and instructor are physically

separated. This study was launched in an effort to understand our target learners and provide an indication

towards the practicality, possibility, and appropriateness of such theory-based design. The application provides

text, video, and recorded audio as media, and includes chat function, game-based learning, and electronic

assignment. This paper explores the method and ﬁndings of a survey study targeted at ﬁrst year Computer

Engineering and Computer Science student learners at the Victoria University of Wellington, New Zealand.

Our survey results indicated that the learners had a positive attitude towards mobile learning, and they had a

lot of experience using the provided media and functions. The theoretical-design was deemed to be practically

appropriate for our learners. However, more encouragement and promotions would be needed in order to

increase the application’s usage and recognition. We performed statistical analysis on the results and clustered

the responses to form a persona which will be used in the next stage of this application’s development process.

1 INTRODUCTION

Along with the popularity and the fast growth of com-

munication technology, an education platform is be-

ing extended to a mobile-based teaching and learn-

ing. The mobility of m-learning helps people to ob-

tain knowledge wherever they are and at convenience

time, which extend the learning to reach a wider au-

dience (Shudong and Higgins, 2005).

Although mobile phones have potential to pro-

mote learning in distance, developing an m-learning

system faces some challenges. Because of hardware

and software limitations, mobile phones cannot de-

liver all learning contents on current personal com-

puters or laptops. This may remind developers that

designing an m-learning system requires a deliberate

plan. At the initial stage of development processes,

identifying and understanding target users can help to

decide what learning contents are possible and mean-

ingful to be delivered on limited resources of mobile

phones.

We developed a mobile learning application on

smart phones grounded on Moore (1973)’s Transac-

tional Distance Theory (TDT). The theory suggested

how distance courses should be structured. We fol-

lowed the guidelines and presented learning content

in text, video, and recorded audio. We included a

chat function in the application, added a game-based

learning in the application, and provided an assign-

ment that was compulsory to be submitted to instruc-

tors through an email.

The theory has been introduced since 1973. To

date, there has been no evidence to suggest if it can

be used to guide the design of modern education plat-

form such as m-learning. Therefore, we launched a

study that observed our target learners’ experience

and attitude towards m-learning, the provided me-

dia, and functions. The study could help us to im-

prove the current design. We clustered the responses

and formed a learners’ archetype. Based upon this

archetype, we created a persona which will be used

by all people involved in the development process.

The remainder of this paper is structured as fol-

lows: section two presents background of m-learning,

persona, TDT, and our proposed design; section three

presents research methodology; section four presents

the persona, quantitative and qualitative results, and

analysis; and the last section is conclusions.

378

Limtrairut, P., Marshall, S. and Andreae, P.

Know the Mobile Learning Application Users - Transactional Distance Perspective.

In Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016) - Volume 2, pages 378-387

ISBN: 978-989-758-179-3

2 BACKGROUND

2.1 Mobile Learning

Mobile learning or m-learning is teaching and learn-

ing processes through mobile devices such as mo-

bile phones, tablets, and Personal Digital Assistants

(PDAs). The devices’ popularity along with the sup-

port of mobile technologies, more people can now

obtain knowledge anywhere and anytime (Liu et al.,

2010). According to Traxler (2007), m-learning has

three remarkable potentials.

• Facilitating Personalized Learning: M-learning

allows an instructor to send personalized feedback

or receive personalized requests from an individ-

ual learner

• Supporting Authentic Learning: M-learning

provides opportunities for learners to access to

available resources online that help them to ap-

proach real world problems. It also facilitates col-

laborative working when a group of learners are

physically remote from each other

• Providing Flexible Learning: While an actual

class room requires a pre-set up in a multi-

campus classroom environment or a ﬁeld study,

m-learning provides ﬂexible learning environ-

ments

Despite its promises, m-learning faces some im-

portant challenges. Technical restrictions of mobile

phones (e.g., small screen size, low screen resolu-

tion, lack of efﬁcient data entry capability, small stor-

age, small bandwidth, slow processor speed, and short

battery life) may inﬂuence m-learning adoption (Liu

et al., 2010). From the pedagogical viewpoint, m-

learning causes the difﬁculties in following up learn-

ers’ achievements. Because learning activities can

happen anywhere and anytime, without instructor su-

pervision there may be issues in trusting that the

answers of an assignment or exam are truly com-

pleted by the registered learner (Shudong and Hig-

gins, 2005).

Even though much research (e.g., Alshalabi and

Elleithy (2012), Melhuish and Falloon (2010), Crow

et al. (2010), Cochrane and Bateman (2010), Jones

and Marsden (2006), Jones and Marsden (2004)) sug-

gested design strategies for m-learning that can elim-

inate the technical limitations of mobile phones, de-

signing an m-learning requires an initial research to

specify if the design is suitable for the target learners.

In the next section, we will introduce persona tech-

nique that raises understanding of learners.

2.2 Persona

Cooper et al. (1999) introduced persona in 1999 to

explain customers’ behavior in marketing research.

The ﬁrst persona was created to point out that the end

users and their needs were important in the develop-

ment processes (Idoughi et al., 2012). According to

Cooper et al. (1999), persona is a character which is

created by gathering behavioural data from real users.

It presents a lot of users’ information and helps de-

velopers to make decisions in a product development

process.

Idoughi et al. (2012) deﬁned persona as “a de-

scriptive model of the user, encompassing informa-

tion such as user characteristics, goals, and needs”

(Idoughi et al., 2012, p.288). Similarly, Pruitt and

Adlin (2010) deﬁned persona as a ﬁctitious character

that represented target users and raised focus towards

them in the development process.

We deﬁned persona within learning environment

as a representative of a group of learners who had

same goals and showed similar behavior and attitude

when they made decisions. These behavior and atti-

tude were regardless of age, gender, and education. It

helped us to decide which functions were meaningful

and should be provided in the m-learning application.

According to Pruitt and Adlin (2010), the beneﬁts

of persona are:

• Clarifying Assumptions about Users: A design

team may has many conﬂict hypothesis on their

users. Persona can assist them to make a con-

crete design decision and focus on the same ex-

plicit deﬁnition of users

• Identifying Speciﬁc Groups of Users: In gen-

eral, products are created to target as many cus-

tomers as possible; however, there are also many

speciﬁc users who are not recognized by a design

team. Persona helps them to identify these users,

hence the design can be expanded to everyone

• Helping a Design Team to make the most from

Limited Resources: At the beginning of the de-

velopment processes, there are many proposed

ideas on the design and someone has to choose

which idea is possible and worth developing.

“Personas offer a consistence target-audience vi-

sion” (Pruitt and Adlin, 2010, p.18), hence there

is a high possibility that many people will like the

design

• Engaging a Development Team: Pruitt and

Adlin (2010) claimed that personas were different

from other user centred design techniques because

they were fun as cartoon characters, which could

inspire imagination, provide compelling, intense,

and memorable information about users

Know the Mobile Learning Application Users - Transactional Distance Perspective

379

Much research placed importance on users and in-

cluded personas in their design processes. For exam-

ple, Rahim et al. (2014) created personas that pre-

sented users’ background, and their behavior when

they purchased food products. These personas aimed

to investigate users’ requirement, and contribute to

their Halal-Checker Mobile Application (I-scan) de-

sign. Calvo et al. (2014) also created personas in the

study that aimed to improve performance of a syn-

chronous chat in an m-learning application for stu-

dents and teachers with disabilities.

We created a persona at the initial stage of our m-

learning application development. The persona pre-

sented learners’ proﬁle, m-learning goals, experience,

and attitude toward m-learning. The application was

designed grounded on Transactional Distance Theory.

In the next section, we will introduce the theory and

show how we applied it in the application design.

2.3 Transactional Distance Theory and

Mobile Learning Application Design

Much research (e.g., Shearer (2007), Park (2011))

found that learners felt disconnected and perceived

isolation when they were separated from instructors

and had to take control of their own learning. Later

Moore (1993) named this feeling and perception as

“transactional distance”. Moore (1973) introduced

Transactional Distance Theory (TDT) to explain the

factors that varied level of transactional distance and

affected learning quality. According to Moore (1973),

there are three factors that take control of transac-

tional distance:

1. Dialogue: Positive interactions and communica-

tions between learners and instructors that im-

prove their understanding. It is direct proportional

with transactional distance (i.e., if an instructor

has a conversation or other of types communica-

tion with learners, transactional distance will be

decreased)

2. Structure: The way teaching program is con-

structed and organized. Moore (1973) suggested

six instructional processes that helped educational

providers to form a distance learning program.

These processes are:

• Presentation: Recorded media such as audio

and video can engage learners better than a

plain text presentation

• Motivation: Feedback from instructors can

help learners to maintain their interest and en-

courage them to learn

• Analysis and Criticism: Opportunities to hear,

analyze, and challenge experts’ discussions can

improve learners’ cognitive skills

• Advice and Counsel: Guidances on how to

use learning materials and which learning tech-

niques are effective for learning can help learn-

ers to solve their problems and build their learn-

ing skills

• Practice and Evaluation: Writing assign-

ments allow learners to practice what they have

learnt, and can be used to evaluate their knowl-

edges

• Creation of Knowledge: Opportunities to dis-

cuss some ideas with instructors can improve

learners’ professional skills

3. Learner Autonomy: The process that learners

set their own learning goals and control over their

own learning. Moore (1980) suggested that each

and every educational provider should examine

learners’ autonomy and seek for a suitable pro-

gram structure for them. This factor does not

guide the design but guide the observation of

learners’ behavior

For the application design, we only adopted di-

alogue and structure. We translated Moore (1973)’s

guidelines into the design guidelines of m-learning

application. The functions we presented in the ap-

plication are:

Figure 1: An example of chat function in the application.

1. Chat: An available communication channel for

learners to contact their instructors. It supports

both synchronous and asynchronous as well as

a public mode (i.e., many learners and an in-

structor are in the same chat room) and a private

mode (i.e., one-on-one communication between a

learner and an instructor) communications. Fig-

ure 1 shows an example of asynchronous, public

mode chat room design

2. Media: There are text, recorded audio, and video

presentations available in the application, which

are used to deliver learning contents

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380

Figure 2: An example of assignment given in the applica-

tion.

3. Game-based Learning: An opportunity for

learners to practice what they have learnt

4. Assignment: In order to improve learners’ cog-

nitive skills, we provided a short case study for

them to read (Figure 2). It is compulsory that they

answer (i.e., a short answer) the given question,

send the answer to an instructor’ email and they

will receive feedback from the instructor

3 RESEARCH METHODOLOGY

We recruited ﬁrst year computer engineering, and

computer science students from School of Engineer-

ing and Computer Science, Victoria University of

Wellington, New Zealand to participate in our online-

based, anonymous questionnaire by sending a class-

less email. From an approximately 150 students, 30

of them completed the survey.

Our participants were students who enrolled ei-

ther in Engineering Modelling and Design or Intro-

duction to Data Structures and Algorithm courses. We

conducted the study on October, 2015 during second

trimester. Our participants have passed at less one

basic computer course, hence they had experience in

learning in university.

In order to create a persona, we adopted Cooper

et al. (2007), and Mulder and Yaar (2006)’s sugges-

tions. Cooper et al. (2007) suggested seven prin-

ciple steps (i.e., identify behavioral variables, map

interview subjects to the variables, identify signiﬁ-

cant behavior patterns, synthesize characteristics and

relevant goals, check for redundancy and complete-

ness, expand description of attributes and behaviors,

and designate persona types) while Mulder and Yaar

(2006) suggested three types of research (i.e., Quali-

tative research, Qualitative research with quantitative

validation, Quantitative research) that helped devel-

opers to create personas.

There are two behavioral variables (i.e., experi-

ence, and attitude) that we observed. We adopted

quantitative research to observe learners’ experience

in using mobile phones for both general and edu-

cational purposes, and learners’ attitude towards m-

learning, the given media and the given functions.

Likert scale was used to rank the experience and mea-

sure the attitude. This could help us to predict if the

given media and functions (e.g., video, recorded au-

dio, chat) are practically possible to facilitate learn-

ing.

We adopted qualitative research to observe what

activities other than calling and texting learners per-

formed using their mobile phones, how they used

their mobile phones to support learning, and their goal

of learning on mobile phones. Text box ﬁelds were

used to collect free-form, text-based responses.

In order to identify signiﬁcant behavior patterns,

we plotted the quantitative results into graphs and

seek for learners’ archetype. In the next section,

we will present our persona, the process we used to

ﬁnd learners’ behaviour patterns, and general ﬁndings

from the survey.

4 RESULTS AND ANALYSIS

From a total of 30 participants, 26 are male students,

and four are female students. The average age of par-

ticipants is 22 year old.

4.1 Persona of M-Learning Learners

We gathered all the results, categorized them, found

a behavioural pattern, and created a persona (Figure

3). This persona will be used to represent our target

learners by all people involved in this m-learning ap-

plication development process.

This persona was created based on the results of

participants’ responses to 24 quantitative questions.

In an attempt to ﬁnd a behaviour pattern from these re-

sponses, we plotted them alongside each other (Figure

4). This graph helped us to see each individual trend

clearly; however, it could not help us to ﬁnd learners’

behaviour pattern.

Next, we plotted them on the same graph (Figure

5). we found that the resulting graph was overly dis-

tributed and was not effective for determining learn-

ers’ behaviour pattern, therefore we performed three

rounds of clustering.

• First Round Attribute Clustering: The ques-

tions were divided into seven clusters (i.e., expe-

Know the Mobile Learning Application Users - Transactional Distance Perspective

381

Figure 3: Persona represents learners of m-learning appli-

cation on mobile phones.

rience in using a mobile phone in daily life, ex-

perience in using a mobile phone for educational

purpose, experience in using the provided media,

experience in using the provided functions, at-

titude towards m-learning, attitude towards pro-

vided functions, and learning preference). For the

clusters that consisted of more than one question,

we plotted the graph based on their average values

(Figure 6).

• Second Round Cutting Off: Based on the av-

erage values, we set an allowed interval for each

cluster. If a participant provided two or more

responses outside the allowed interval, we cut

through the participant off the considered group.

There were 17 participants who were cut off in

this round (Figure 7).

• Third Round Cutting Off due to the Extreme

Value: There were three participants who were

qualiﬁed from the second round cutting off, but

were excluded in this round, as their responses

to one question varied greatly from other partic-

ipants in the same cluster.

The ﬁnal ten participants showed a similar trend.

They were the archetype and we created a persona

based on them. We assigned a pseudonym which was

“John” to represent the persona. According to the par-

ticipants’ responses in Figure 8:

Figure 4: The graph of 30 participants’ responses to 24

quantitative questions plotted alongside each other. The x-

axis presents the 24 qualitative questions, while the y-axis

presents the level of learners’ experience and attitude to-

ward m-learning, the provided media and functions in the

range one to ﬁve. We plotted this graph to see each individ-

ual’s responses and the trend in graphs. However, it could

not help us to ﬁnd learners’ behaviour pattern.

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382

Figure 5: This graph is the compressed 30 participants’

responses (from Figure 4) in a single scale. The x-axis

presents 24 quantitative questions, while the y-axis presents

the responses in the range one to ﬁve.

Figure 6: The graph after ﬁrst round attribute clustering.

The x-axis presents seven clusters, while the y-axis presents

the responses in the range one to ﬁve.

Figure 7: The graph after second round interval cutting-off.

Figure 8: The graph after third round the extreme value

cutting-off.

• Cluster 1: John “all of the time” uses his mo-

bile phone for things other than calling and tex-

ting. We used a qualitative question to specify the

activities and we found that John watched Videos

on YouTube

, listened to pre-downloaded musics,

went to Google

website to search for some in-

formation, and used his mobile phone’s camera to

capture photoes. He also used his mobile phone

to communicate with his friends and family.

• Cluster 2: John “often” uses his mobile phone

for educational purposes. We also used a qualita-

tive question to specify the educational activities.

The answers were varied (e.g., he used his mo-

bile phone’s camera to capture lecture notes’ pho-

tos, he went to school’s website and viewed online

learning contents on his mobile phone)

• Cluster 3: John “sometimes” uses the media (i.e.,

video, recorded audio, and text) that we provided

in the application (i.e., he sometimes watches

videos, listens to pre-downloaded musics, and

read online articles)

• Cluster 4: The responses in this cluster were

varied between two (rarely performed the given

tasks) to four (often performed the given tasks),

therefore we could not form a behavior pattern

based on the whole cluster. However, when

we looked into each question (i.e., each given

task) within this cluster, we found patterns (i.e.,

John “sometimes” uses his mobile phone to

send/receive emails, he “rarely” plays games or

works on an assignment on his mobile phone)

• Cluster 5: John agreed that mobile phones have

helped him in his learning

• Cluster 6: John has positive attitude towards pro-

vided functions (e.g., he realizes that contacting

instructors either face to face or via email have

helped him to engage with learning)

• Cluster 7: John prefers to learn on a laptop or

PC rather than a mobile phone. Therefore, more

encouragement and promotions may be required

in order to increase the application’s usage and

recognition

https://www.youtube.com

https://www.google.co.nz

Know the Mobile Learning Application Users - Transactional Distance Perspective

383

4.2 General Findings

4.2.1 Learners’ Experience in Using Mobile

Phones for General and Educational

Purposes

We adopted Likert scale to measure how often (i.e.,

1= never, 2 = rarely, 3 = sometimes, 4 = often, and

5 = all of the time) learners used their mobile phones

in daily life, and whether they used it for educational

purpose. Our ﬁndings (Table 1) shows that learners

used their mobile phone “all the time”. We also asked

them to list the activities that they performed on their

mobile phones other than calling and texting. The fol-

lowing list shows the usage categories.

• Communication purpose: They used their mo-

bile phones to access social media websites (e.g.,

Facebook

, Reddit

, and sending and receiving e-

mails

• Entertainment purpose: They used their mobile

phones to watch video online from video-sharing

websites (e.g., YouTube), listen to music, and play

games

• Researching purpose: Participants mentioned that

they accessed Google whenever they wanted to

ﬁnd any information. They also read news,

and participated in online-forums on their mobile

phones

• Personal Management purpose: They used many

basic functions available on their mobile phones

to manage their daily life. For example, they used

note function to list activities to be completed,

used calendar to receive notiﬁcations regarding

when these tasks should be completed, used alarm

clock to wake them up in the morning and they

managed their bank account online

• Other purpose: They mentioned many others ba-

sic functions of mobile phones (e.g., ﬂash light,

map, camera) which they used regularly

Table 1: Learners’ experience in using a mobile phone for

general and educational purpose in their daily life.

Question Mean SD

For things other than

calls and texts

4.53 0.97

For educational purpose 3.57 1.04

When we speciﬁed the usage of mobile phones to

educational purposes, the usage level was slightly

decreased to “often”. We also asked them to explain

https://www.facebook.com

https://www.reddit.com

how they used their mobile phones to support their

learning. The results show the same usage categories

(listed previously) being applied by students in the ed-

ucational context. The interesting ﬁndings are:

• They used their mobile phones to access to social

media websites to contact their classmates and

discuss their lessons and assignments

• They also downloaded lecture notes, slides, and

videos and used these downloaded materials from

their mobile phones when they were outside uni-

versity

• For research purpose, they read articles on their

mobile phones and used online search engines

(i.e., Google) when they had questions and wanted

to ﬁnd more information

• They visited the school’s website to keep them-

selves updated, and accessed learning resources

via University’s Blackboard system

• They used mobile phones’ basic functions such as

clock, calendar, and note to help them to manage

their learning schedule, and they also used their

camera to capture their lecture notes

Based on these ﬁndings, mobile phones are con-

ﬁrmed to be powerful tools for supporting ubiquitous

learning. The use of mobile phones as an educational

tool is now very much an established part of our target

learners’ daily learning environment.

4.2.2 Learners’ Goals toward M-Learning

We asked learners to explain the term “mobile

learning”. Most of them showed that they understand

the term. For example a student answered that

“Mobile learning to me is being able to gain

and apply knowledge anywhere at anytime including

when an internet connection is unavailable”

The results showed that learners could differen-

tiate m-learning from other types of learning. They

mentioned learning on the go, learning anywhere,

and convenient when they were not stationary.

We asked learners to identify their goals of

learning on mobile phone. It appeared that they did

not have a clear goal for m-learning. Most of them

considered mobile phones as tools to help them to

learn outside of their classrooms when they were

not in front of their laptop and PC. Examples of the

answers are:

“I do not consider myself a person who learns

from a mobile phone, only one who uses it for time

management and organising my learning, which

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384

Table 2: Learners’ experience in using a mobile phone to perform the given tasks.

Task Mean SD

Reading long text such as an on-line article from the screen of mobile phone 3.50 1.20

Using a scroll-up and scroll-down bar to go through text 3.17 1.53

Using zoom-in and zoom-out to read text 3.43 1.17

Playing video on mobile phone 3.50 1.11

Using play, pause, rewind, fast forward, or repeat functions of video player 3.03 1.19

Listening to recorded audio or music on mobile phone 4.00 1.17

Using play, pause, rewind, fast forward, or repeat functions of music player 3.37 1.33

Playing game on mobile phone 2.57 1.22

Sending/Receiving e-mail on mobile phone 3.97 1.07

Text chatting on mobile phone 3.93 1.23

Doing homework/assignment on mobile phone 2.07 1.28

comes from other sources unless it is an extreme

scenario (i.e. there are absolutely no computers

available to me)”

“I just want to be able to ﬁnd out what I need

to know very quickly. I can do this already!”

These results pointed out that our target learners

did not take m-learning as a primary source of

learning and they did not set up a learning goal

even though they adopted the phones for many

learning activities. It raised awareness about learners’

m-learning acceptance and that encouragement and

promotions (e.g., use rewarding technique) may be

required.

4.2.3 Learners’ Experience in Using Provided

Functions

In this section, we observed if learners had experience

in using the functions that we planed to provide in our

application. We adopted Likert scale (i.e., 1= never, 2

= rarely, 3 = sometimes, 4 = often, and 5 = all of the

time) to indicate how often they had performed the

tasks.

As outlined in our results collated in Table 2,

learners pointed out that they “sometimes” to “often”

perform the given tasks. Base on these results, learn-

ers should be able to learn by reading text, watching

video presentation, and listening to recorded audio. If

they have questions, want to share their ideas, or dis-

cuss their ideas with instructor they should be able to

use the provided communication channels (e.g., send-

ing an e-mail to instructor, entering chat room).

On the other hand, they expressed that they

“rarely” played games and worked on assignments

on their mobile phones. These ﬁndings raised some

concerns that they might not play the provided game-

based learning and submit the given assignment. We

may need to ﬁnd strategies to promote our game-

based learning (e.g., arrange competitions in which

highest score winners receive rewards). For the as-

signment, we may need to arrange a submission dead-

line or use rewarding technique to encourage their

participations.

4.2.4 Learners’ Attitude toward M-Learning

We observed learners’ attitude toward m-learning and

the functions that we planed to develop in the appli-

cation. We adopted Likert scale (i.e., 1= strongly dis-

agree, 2 = disagree, 3 = neutral, 4 = agree, and 5 =

strongly agree) to indicate how much they agreed with

the given statement. Table 3 presents the results.

Based on the results in Table 3, learners showed

that they had positive attitudes toward using mobile

phones to support their learning process. They fur-

ther indicated that learning through video presenta-

tion was the most popular learning style, followed

by learning by reading text. Even though they ex-

pressed that they had the most experience listening to

recorded audio or music, they preferred it the least.

For game based learning, they expressed that they

did not have negative nor positive attitudes toward

them even though they rarely played games on mo-

bile phones.

Learners “strongly agree” that assignments had

helped them to improve their learning and they pre-

ferred performing them on a computer rather than

on actual paper, hence assignments showed high po-

tential for improving learning quality and should be

added into m-learning application. However, students

expressed that they felt frustrated typing using small

mobile phone keyboards, therefore the given assign-

ment in the application should be short answers, or

multiple choices types of questions.

For communication, learners agreed that keeping

in touch with instructors could encourage them to

learn and they slightly preferred sending e-mail or

message to instructor rather than having a face-to-face

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385

Table 3: Learners’ attitude towards m-learning and the given functions

Task Mean SD

Mobile phone has helped me in my learning process 3.87 0.82

I prefer learning by watching a video on a mobile phone rather than reading text on a

mobile phone

3.10 1.27

I prefer learning by listening to a recorded audio on a mobile phone rather than reading

text on a mobile phone

2.43 1.07

I like game based learning activities 3.30 1.22

I feel frustrated typing on small key board of a mobile phone 3.37 1.27

Assignments help me to improve my learning 4.60 0.56

I prefer doing an assignment on a computer rather than writing on actual paper 3.83 1.09

Keeping in touch with my instructor can encourage me to learn 3.83 0.70

I prefer sending an e-mail or a message to my instructor rather than having a face-to-

face conversation

3.43 1.25

I prefer having synchronous rather than asynchronous communication with instructor 3.37 0.89

I prefer learning on a mobile phone rather than learning on a laptop or a desktop

computer

1.17 0.92

conversation. Similarly, they slightly preferred syn-

chronous to asynchronous communication.

Lastly, even though learners had positive attitude

towards using mobile phones to support their learn-

ing, they still preferred to learn on a laptop or a desk-

top computer rather than on a mobile phone. This

could be an effect of hardware limitations (e.g., small

size keyboard, screen, low solution).

5 CONCLUSIONS

Distance learning may trigger feelings of isolation or

perceived emptiness in learners as they have to ex-

ercise more self-discipline and control over their one

learning. Transaction Distance Theory suggested how

distance education should be constructed. Grounded

on this theory, we designed an m-learning application.

We observed whether this theoretical-based design is

practically possible for our target learners.

A total of 30 participants, 26 male and four fe-

male, who are ﬁrst year computer engineering and

computer science students at Victoria University of

Wellington, New Zealand answered an online-based

questionnaire. The signiﬁcant ﬁndings are:

• Our target learners had a lot of experience in using

mobile phones. They used them as communica-

tion tools (e.g., access to social media, chatting),

entertainment tools (e.g., watch video, listen to

music), researching tools (e.g., access to Google),

and management tools (e.g., set up notiﬁcation,

alarm clock)

• Our target learners often used their mobile phone

for educational purposes

• Our target learners knew what m-learning was,

but they did not have a cleared goal towards m-

learning

• Our target learners had experience in using the

provided media (i.e., text, video, recorded audio)

and they preferred to learn from video presen-

tation rather than reading text, and listening the

recorded audio

• Our target learners had a lot of experience in us-

ing the chat function but they had less experience

playing game, and working on their assignments

on mobile phones

• Our target learners had positive attitudes towards

m-learning, however they preferred learning on a

laptop or a desktop computer rather than learning

on a mobile phone

Based on these ﬁndings, our Transactional Dis-

tance Theory-based design was practically appropri-

ate for our learners. They should be able to learn

from provided media, and be able to use the pro-

vided chat function to communication with instruc-

tors. However, we need to ﬁnd strategies to encourage

learners to participate in the game-based learning, and

complete the given assignment. We can use reward-

ing techniques and send assignments notiﬁcations to

them.

Our survey results also indicated that our learn-

ers did not consider m-learning on mobile phones as

their major learning source. In order to increase appli-

cation usage, m-learning needs more promotions and

encouragement.

Finally, we created a persona based upon learners’

archetype, which will be shared among the people in-

volved in this application development process.

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386

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