Int. J. Innovation and Learning, Vol. 4, No. 1, 2007
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Mobile learning as a mobile business application Krassie Petrova Auckland University of Technology 55 Wellesley Street East Auckland 1020, New Zealand E-mail:
[email protected] Abstract: The predominant mode of electronic learning today is ‘online’ (web-based). This type of learning relies on student access to personal computers – both on and off campus. Mobile technologies offer other educational alternatives. This article defines mobile learning (mLearning) as a new paradigm of flexible learning and describes scenarios for blended learning, which integrates the mobile and the online learning environments. A research model for the study of mLearning adoption is proposed, and hypotheses are derived that allow the study of mLearning adoption from a pedagogical and from a business perspective and can be applied to a variety of learning scenarios. Keywords: mobile learning; Short Message Service (SMS); eLearning; mLearning; flexible learning; situation-based learning; constructivism; mobile business; mobile application; adoption. Reference to this paper should be made as follows: Petrova, K. (2007) ‘Mobile learning as a mobile business application’, Int. J. Innovation and Learning, Vol. 4, No. 1, pp.1–13. Biographical notes: Krassie Petrova is a Senior Lecturer in Computer and Information Systems at Auckland University of Technology and Programme Leader of the Master of Computer and Information Sciences programme within the School of Computer and Information Sciences. She lectures in the areas of networking, data communications, information security, and eBusiness technologies and has professional experience as a manager and a consultant in information systems development and information management. Her current research focuses on mobile business applications, mobile and online learning and student capability development in industry supported learning.
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Introduction
Handheld devices connected to a wireless network that operates at all times and in any location within the network coverage have become widely spread and used. According to the research and analysis company Gartner Group, the current number of mobile users exceeds 1.5 billion and is predicted to reach 3 billion by 2010 (MobileTech News, 2005). The penetration of mobile communication devices is especially strong amongst the young. A survey from Norway, for example, reports that all adults in the range of 16 to 19 years of age have a mobile phone (Smart Mobs, 2004). A survey of the Asia-Pacific youth market finds that 14% of the leisure spending of the Asia-Pacific young people (5 to 24 years of age) is mobile related (Wireless World Forum, 2004). Copyright © 2007 Inderscience Enterprises Ltd.
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Against the backdrop of such significant ‘mobile’ penetration into the youth market and given the fact that students of both genders are typically among the heaviest users of cellular technologies (DeBaillon and Rockwell, 2005), it would seem that integrating mobile devices in the educational process would be a straightforward task. However, the pace of progress in this area has been rather uneven. While Stone et al. (2002) and Thornton and Houser (2004) identify some useful applications and evaluate positively their experimental implementation, Jones et al. (2004) find that handheld devices are not sufficiently well integrated in teaching and learning. The level of adoption of mobile learning applications and services remains relatively low (Wagner, 2005). This article proposes a research model for the study of mobile learning adoption, identifying mobile learning (mLearning) as a mobile service offered through a mobile business (mBusiness) model. In the next section, the evolution of flexible learning is discussed and four contemporary paradigms for student-centred learning are compared, positioning mLearning in the general context of electronic learning (eLearning). The section following interprets mLearning from an mBusiness perspective and introduces a framework of mobility-related informational and technological application characteristics. In Sections 4 and 5, two scenarios for mLearning are introduced and hypotheses derived from a research model are formulated. The last section summarises the paper and suggests some directions for further work and research.
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mLearning as a learning paradigm
Four learning paradigms are currently prevalent: open learning, distance learning, face-to-face learning and flexible learning. In this section, an open-ended framework describing these paradigms in terms of their flexibility is introduced, and is extended to include the emerging paradigm of mLearning. Face-to-face learning refers to the traditional teacher-centred model of learning, where the teacher retains significant control over both the teaching and the learning processes. Moving away from teacher-held control towards empowering the learner, distance learning separates the learner from the teacher in time and space through the deployment of a range of suitable supporting technologies (Sherry, 1996). Distance learning is ‘flexible’ in terms of ‘when’ the student works, and ‘where’ the student is located. Technological developments have made it possible to implement ‘within the walls’ of the educational institution some of the platforms used to support distance learning and have facilitated the emergence of ‘flexible learning’. In flexible learning, the student controls to a certain extent ‘what to study’. Flexible learning fits well with the increased flexibility of the work environment and aligns well with the paradigm of lifelong learning (Garrick and Usher, 2000). Both flexible and distance learning allow students to take control of their own learning. These two types of learning coexist under the umbrella of ‘open learning’ as defined by Brophy et al. (1998). Typically, an ‘open learner’ will study towards a recognised qualification using specialised course material, but will not rely on immediate teacher support. Instead, educational technology tools designed to take advantage of the emerging information and communication media will be implemented.
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A flexibility framework comprising five operational dimensions can be used to describe the learning paradigms discussed. The framework is based on the already mentioned ‘when’, ‘where’ and ‘what’ questions, and includes two additional questions related to the learning style and the level of assumed prior knowledge: •
Timeframe (‘When does learning occur?’)
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Geographical location (‘Where does learning occur?’)
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Personal learning style (‘How does learning occur?’)
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Prescribed and/or negotiated subject content (‘What is being learnt?’)
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Assumed prior knowledge (‘Who is the learner?’).
In Table 1, the four learning paradigms are ranked with regard to the extent to which the learner is in control of each dimension of the framework. The rankings are derived from the literature already reviewed and also from Epper (1997), Harasim (1999) and Higgins (1998). Table 1
Learning paradigms
Time-frame (‘when’)
Geographical location (‘where’)
Personal learning style (‘how’)
Prescribed/ negotiated subject content (‘what’)
Assumed prior knowledge (‘who’)
Open learning
High
High
High
High
Medium to high
Face-to-face learning
Low
Low
Low
Low to medium
Low
Flexible learning
Medium to high
High
Medium to high
Low to medium
Low
Distance learning
High
High
High
Low
Low
Level of student control Paradigm
For each learning paradigm, the entries in the table correspond to the most common answer to each question. A three-point scale was used, where ‘High’ means a significantly high level of student control, and ‘Low’ means ‘almost no control by the student’. It can be seen that all paradigms exhibit a degree of flexibility along at least one dimension, with all paradigms becoming more flexible as they move towards more student-centred learning and less teacher-held control. Consequently, the term ‘flexible learning’ has subsumed open and distance learning. As an umbrella term, it can be broadly defined as a mix of face-to-face and distance models of teaching and learning (Petrova, 2001). Within flexible learning, the specific modes of delivery and learning differ in their use of technology. Currently, online learning is associated with the use of public networks and platforms such as the internet and the World Wide Web (Paulsen, 2003,pp.25–26), while mLearning is typically supported by platforms and networks owned by mobile telephone operators.
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Flexible learning facilitated through any kind of electronic communication (the internet, or the mobile telecommunication networks) is also known as eLearning. Online learning and mLearning are two forms of eLearning. Table 2 provides a timeline of the evolution of flexible learning models. It uses Harasim’s (1999) perspective on distance and online learning and extends it by including mLearning. Each learning model is associated with a learning pedagogy, based on the classification provided by Edgar (1995) and Nyíri’s (2002) discussion on the philosophical approaches towards mLearning. Table 2
Stages of flexible learning
Time period
Technology and platforms
Learning models
Learning pedagogies
Mid-1970s
Wide area computer networks (mainframes)
Distance learning
Behaviorism (Skinner)
Early 1980s
The internet in the early 1980s (personal computers)
Online learning
Cognitive constructivism (Piaget)
Early 1990s
The World Wide Web (personal computers, client/server architecture)
Online learning
Social constructivism (Vygotsky)
Early 21st century
Mobile networks (personal handheld mobile devices)
Mobile learning
Constructivism pragmatism (Dewey)
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mLearning as a mobile application
While mLearning is a form of eLearning and shares some similarities with online learning, as a mobile application it complies with the basic definition of a commercial mobile application as ‘any transaction conducted via a mobile device’ (Muller-Versee, 2000; Andreou et al., 2005; Chen and Nath, 2004). As a mobile application, mLearning is characterised by the use of specific mobile infrastructure, which includes personal subscriber network access and location- and time-independent connectivity, as explained below: •
Communication network access mLearning occurs through the use of portable devices (including different types of mobile phones and handheld computers) linked to a commercial public network to which the user needs to be subscribed.
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Connection type As a subscriber, the mobile learner has access to a private personalised service typically not shared with anyone else. Most mobile networks use encryption and provide security transparently for the user.
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Location and time independence mLearning is independent of the location of the learner, as the device literally travels with its owner. Mobile learners have access to the same personal device on a 24/7 basis, and these types of devices are permissible for use in a broad variety of environments (i.e., at work).
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In addition to meeting the infrastructure requirements described above, results from the literature indicate that mobile applications need to conform to content-related (informational) requirements: information density, information relevance and ease of use. •
Information density Information sent to a mobile user needs to be concise but very useful. Typically, users do not use mobile devices as a primary tool to access information but rather employ them in an ‘alert raising’ mode. Partially owing to the small size of the screen display, users prefer to receive ‘dense’ information – specific, concise and precise, packed with meaning, but not too detailed (Wuthrich et al., 2003).
•
Information relevance The information provided by a mobile application must be able to support users’ activities, i.e., it needs to be expected and anticipated by the user. Examples include driving and mobile navigation support, and shopping and mobile promotions (Wuthrich et al., 2003). In addition, an interactive mobile application should expect minimal response from the user, if one is needed – in other words, the information supplied to the user must also be ‘dense’ (Uther, 2002).
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Ease of use This characteristic refers to the lack of constraints to network access. It is typical for mobile phone connectivity, and makes mobile applications both ubiquitous (the user is able to access information provided by a mobile application anytime and from anywhere) and unobtrusive, as the device is also highly personal (Hino et al., 2002; Sharples et al., 2002).
Instances of successful mLearning experiments show that the applications developed exhibit all three informational characteristics. One of the examples is ‘mobile testing’, as described by Wuthrich et al. (2003). It is a relatively simple rendition of a multichoice examination with instant feedback. Another application – a collaborative ‘mobile seminar’ – operates as a blended model, with an online platform used for the early stages of the discussion and mobile e-mail used to conclude it (Hino et al., 2002; Leung and Chan, 2003). Hino et al. (2002) note that ‘mobile’ student seminar contributions demonstrate high quality and are both ‘concise’ and ‘precise’. The technological and informational requirements identified above are specific for mLearning as a mobile application and do not apply to online learning. Online learning utilises the insecure internet and organisational intranets, and is based predominantly on stationary computers which are often public and are not always available. Online learning applications often involve multimedia, and are highly interactive. Information supplied online is not expected to be ‘dense’ or ‘precise’ and ‘concise’, and is not necessarily relevant. Information mining is almost always required and ‘searching’ is a recognised online-learning activity. As a mobile application, mLearning is substantially different from online learning and therefore is capable of generating its own value-based on its mobility-related features and coupled with appropriate pedagogy (Seng and Lin, 2004; Vavoula and Sharples, 2002). Similarly to online learning, mLearning provides a communication channel between teachers and learners but physically separates them (Kurbel and Hilker, 2003).
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However, mLearning is much more ubiquitous than online learning (mLearning takes place not only any time, but anywhere). In addition, mLearning can become highly personalised, as it is a service to a subscriber with a known profile (Yang et al., 2004). The relationship between eLearning, online learning and mLearning is shown in Figure 1, which was modified from Brown (2004). Online learning and mLearning are represented as subsets of eLearning. Figure 1
eLearning, online learning and mLearning
eLearning (using electronic media)
Online learning (using web-based platforms)
Mobile learning (using mobile phones and PDAs) Infrastructure: includes mobile voice/data, WAP, the internet
Source: modified from Brown (2004)
It is possible to use both online learning and mLearning within the same teaching and learning context, if a multiplatform approach is developed and implemented. This model is an instance of the so-called ‘blended’ or ‘hybrid’ model of learning (Petrova, 2001) and is symptomatic of the convergence of information and communication technologies (Sharples et al., 2002). A blended model can be well suited for students of multicultural backgrounds and with diverse learning needs (Eddy et al., 1997; McGinnity et al., 1999).
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Mobile learning scenarios
The ultimate success of an mLearning application developed according to the requirements identified in the previous section will depend on the level of its adoption by learners (Vrechopoulos et al., 2003). The adoption processes associated with mLearning can be investigated using the value chain approach already applied to studying the mBusiness phenomenon. According to the value chain approach, the successful mobile application allows the players in the value chain to generate revenue streams (Petrova, 2004). For this purpose, the application must propose a clear and identifiable value to the paying user (customer) – a value that can offset customer costs. The revenue streams of the actors in the value chain depend on the level of customer adoption. In this section, we propose a set of scenarios for mLearning using Short Message Service (SMS) and a framework for studying the learner-adoption processes, based on the mLearning value chain adapted from Petrova (2004) and shown in Figure 2.
Mobile learning as a mobile business application Figure 2
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The mLearning value chain
Activity: Developing application content Actor: Educational institutions
Activity: Providing network services Actor: Mobile operators
Activity: Providing a mobile portal Actor: Integrators
Activity: Providing services to mobile users Actor: Educational institutions
Activity: Developing the mobile application Actor: Software developers
Source: adopted from Petrova (2004)
The scenarios described here blend mLearning with online learning and construct a blended learning environment (Figure 3). The SMS application portal can handle different scenarios for mLearning. Figure 3
The blended learning environment Platforms: SMS server, web server
Blended learning
Students SMS platform development
Web platform development
Pedagogical models (moderated scenario) development
Self-paced and independent learners. They use two flexible learning environments.
The choice of the platform (SMS) was determined by several factors: first, the high rate of mobile phone penetration already mentioned; second, SMS is relatively cheap (Divitini, 2002). Trifonova and Ronchetti (2003) also support the use of SMS, noting that SMS-based services are significantly more personal compared to the web or to e-mail. The SMS platform meets the technological requirements of a mobile application discussed in Section 3 (personalisation, security, and location/time independence).
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The informational requirements (information density, information relevance and ease of use) of the application are met by the following two scenarios: 1
Scenario 1. A ‘question-and-answer’ test-revision session with feedback.
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Scenario 2. A ‘decision-making’ session (helpdesk and customer support queries simulation).
The pedagogies underpinning Scenario 1 and Scenario 2 (Figure 4) are constructivism and situated learning (Adams, 2004; Blackmore, 1996; Bostock, 1998; Herrington and Bunker, 2002). Figure 4
Two SMS-based mLearning scenarios
Scenario 1 1) SMS server: sends periodically a new revision question (split in four revision rounds) 2) Learner: responds (coded or free text) 3) SMS server: sends feedback (true/false or a short answer, as appropriate) 3a) Learner: can request the same question 3b) Same as 3) 4) SMS server: sends a notification when the complete answers are available on AUTOnline 5) SMS server: sends total score for each round
Scenario 2 1) The client sends a text message to the SMS server with a request (a preferred biometric characteristic, related to the group project) 2) SMS server: broadcasts message to the group 3) The group ‘votes’, sending votes to the SMS server 4) The SMS server scores the votes and notifies the group and the client 5) If the biometrics is not accepted, the client has to choose another one and the cycle is repeated 6) The client has the option to interrupt the process and to convene an online meeting (AUTOnline). 7) The SMS server broadcasts the message regarding the meeting 8) The group responds 9) If there is a majority, the meeting goes ahead 10) The voting cycle might be repeated within the time frame (3 days)
The test-revision scenario (Scenario 1) builds up students’ confidence and provides a scaffolding structure leading to independent learning. It is an example of a concrete experience suitable for novice learners (Blackmore, 1996; Bostock, 1998). The decision-making scenario (Scenario 2) develops time management and leadership skills and the capability to work under pressure and as a team. The situation is authentic and very close to industry reality. It is an example of an active experience suitable for more mature students who prefer to be involved in problem solving (Adams, 2004; Blackmore, 1996; Bostock, 1998; Herrington and Bunker, 2002).
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Hypotheses about mLearning adoption
To study the process of the adoption of the scenarios described above, we propose a research model (Figure 5) based on the general model for mobile commerce adoption introduced in (Petrova, 2004). It assumes that adoption is driven by the value proposition of the mLearning application. The model constructs match the components of the blended learning environment in Figure 3 and contribute to the creation of mobility-related value. The actors in the mLearning value chain are part of the model, represented by the ‘Intermediaries’ construct. The intermediary Company X develops hosts, operates the mobile application and integrates the different operators’ networks. It also handles subscription, payment and interaction. The intermediary Company Y develops and provides content. The intermediary Company Z promotes content use through advertising or through the inclusion of the application in the curriculum.
Mobile learning as a mobile business application Figure 5
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A research model for studying mLearning adoption
mLearning-related value is generated using the platform, through the activities of the intermediaries and through the pedagogical models. The main research objectives of the model are to determine which features of the application generate the highest perceived value and to investigate the relationship between learners’ profile and perceived mLearning value. The three hypotheses below were partially derived from the literary sources reviewed so far. They link mLearning value with infrastructure and informational characteristics: •
H1. Learners value the proactive ‘anytime, any place’ approach, which fits in with their lifestyles.
•
H2. Learners value the instant feedback, which helps them to achieve the learning outcomes.
•
H3. Learners value the relevance of the mLearning scenarios to the assessment.
Hypotheses H4.1 – H4.4 are based on the results from a mobile-readiness survey of the target student population reported by Petrova and Sutedjo (2004). The survey identified four critical success factors for mLearning – three of them personal (age, English language skills, prior experience with SMS), and one technology related (mobile phone functionality). The first three hypotheses below refer to the learning style and the last one refers to mobile device quality: •
H4.1. Younger learners will value Scenario 1 more, while mature learners will value Scenario 2 more.
•
H4.2. Learners with English as a second language will value the services more compared to learners with English as a first language.
•
H4.3. More experienced SMS users will demonstrate a higher level of adoption.
•
H4.4. Ownership of a higher-quality device will influence adoption positively.
SMS scenarios are perhaps the simplest form of mLearning, as they do not require extended handset functionality. Despite the obvious limitations, such as text length or the lack of graphical user interface, SMS provides a productive platform for experimenting with a variety of academic and administrative services, such as sending reminders of important dates; sending enrolment information; multiple choice assessment with immediate feedback; sending motivational messages; sending assessment results; sending
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exam dates; assigning learning tasks; sending web links; announcing schedule changes; sending reading lists; and sending revision questions with individual feedback. The proposed research model can be adapted and used to study the adoption of any of these implementations, as well their impact on improving student achievement (Davis, 2005).
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Concluding remarks
Many students study and work at the same time, and expect significant cost and time savings from the use of information and communication technologies (Attewell and Gustafson, 2002; Petrova and Sutedjo, 2004; Uther, 2002). It is envisaged that all participants in mLearning will be highly motivated by the use of ‘cutting-edge’ technology mLearning. mLearning might be used to improve the learner’s communication- and learning-process management skills and might be especially beneficial to novice students with English as a second language. It was postulated that mLearning adds value to the educational experience, as the use of a mobile device is location independent and utilises a ubiquitous personal device; and also that the added value drives the adoption of mLearning as an mBusiness application. Seven research hypotheses were formulated, based on the interactions between the actors of the mLearning value chain and the specific characteristics of the blended learning environment. The research model and the hypotheses can be adapted to other possible scenarios for mLearning. The directions for further work include building the corresponding mobile applications and testing the hypotheses. Possible directions for further research include the development and implementation of other blended scenarios (including WAP-based services), and exploring mLearning through a mobile business research framework (Fouskas et al., 2005). Results from studies of mLearning adoption will be of interest to decision makers concerned with the implications of the inclusion of blended models and mLearning in an institutional learning environment, and to market analysts interested in the further development of value-added mobile services (Bai et al., 2005; Vrechopoulos et al., 2003).
Acknowledgement The Auckland University of Technology ‘Women on campus’ Chancellor’s Research Fund (2004) publication grant is gratefully acknowledged, as well as the constructive feedback provided by the anonymous reviewers.
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