Mobile learning framework study

Mobile Learning Framework Study

Mobile Learning Framework Study The use of mobile technology to deliver and enhance knowledge is quickly becoming an important aspect in learning efforts. Yet there have been few large scale studies addressing the mobile device and the design of mobile learning modules. This proposal attempts to quantify the current design recommendations and determine whether these recommendations result in better learning outcomes.

Jessica Lucas December 2014

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Introduction According to the Pew Research Internet Project, 58% of American adults own a smart phone as of January 2014 compared to only 35% in 2011 1. There is no end in sight for the rise of mobile devices and smart phones as more and more people come to rely on them for their daily activities. This growing resource provides a vast opportunity for the delivery of learning and information that was traditionally delivered via a classroom or even a desktop eLearning module. The thirst for just-in-time information as well as the ubiquitous nature of mobile devices means that a future of anytime, anywhere learning may be within our reach.

While this paints a rosy picture, there are real device constraints that mobile learning modules must overcome in order to create a positive learning outcome. One example is screen size. The latest iPhone 6 has a 4.7 inch screen (iPhone Plus 5.5 inches) while the screen on the Samsung Galaxy Note clocks in with a 5.7 inch screen2. These are larger than previous iterations but mobile screens are still small. Mobile learning designers need to account for this precious little real estate in which to convey and test knowledge.

This goal of this paper is to identify constraints specific to mobile devices that researchers have found when delivering learning modules via mobile devices and examine the current design recommendations to mitigate those constraints. The proposed study will test these design recommendations on new university students.

With this information, mobile learning designers can create learning modules that work seamlessly with the user and mobile device itself.

Literature Review Although there is no definitive definition of what constitutes mobile learning, the literature agrees on certain characteristics. Mobile learning can be described as knowledge or skills acquired through the use of a mobile device (Nagi, 2008). Ozuorcun and Tabak argue that mobile learning is derived from e-Learning but is more informal and situational by the nature of the go-anywhere device (2012). Another important defining characteristic of mobile learning is connectivity, hence the learner is connected through a wireless network in order to learn anywhere (Hashemi, et al., 2011). Both informality and connectivity are important and influence the way people use mobile devices, as well as the way they learn on them. This encourages active, rather than passive, participation in learning (Looi, Seow, et al., 2010).

Learning on a mobile device is a relatively new phenomenon. Therefore, a review of the literature on mobile learning often hinges on the idea of perceived usefulness, or how useful the user believes the learning device/module to be, based on interview or survey results. Hye Won and Kyong-Jee found that medical students who accessed clinical videos through a mobile device expressed greater satisfaction than those who accessed the same videos through a personal computer (2014). Rui-Ting, et al. concluded that the higher the perceived flexible advantages the higher the perceived usefulness of the mobile technologies and learning (2014). Tan, et al. found similar results in an empirical study of perceived usefulness (2012). These studies indicate a general acceptance of mobile devices as a learning tool and determining whether a user will actually use the device as a learning tool is the first step in discussing mobile learning.

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While studies have confirmed that learners perceive the mobile device as a useful tool in learning outcomes, the device itself has significant constraints that may be limiting.

Pereira & Rodrigues point out that many different operating systems on many different devices prove a logistical problem for designers of mobile learning. (2013) Adding to the problem are obsolete OS systems on smartphones still being used by potential learners (Pereira & Rodrigues, 2013). While a solution in the form of standardization would be optimal, the reality is that designers will have to deal with these types of issues for some time.

Legacy data also proves problematic when converting to mobile devices. Many corporations have legacy learning materials that would be too costly to transfer to mobile formats (Kuehner-Herbert, 2014). Specific brands of devices have issues relating to legacy material. The iPad users had some difficulty with cases on the .pdf document format (Kaganer, 2013). Learners must reliably access materials on a mobile device in order for mobile learning projects to work.

Small screen sizes reduce the amount of information that can be displayed at any one time (Hashemi, et al., 2011) (Pereira & Rodrigues. 2013) (Chen et al., 2008). Designers must also take into account that fact that screen sizes differ among brands. Multimedia and graphical images are sometimes comprised due to issues with older devices, lowbandwidth or an outdated mobile OS (Hashemi, et al., 2011) (Pereira & Rodrigues.

2013) (Kuehner-Herbert, 2014) (Chen et al., 2008).

In addition to physical device limitations, cognitive load issues have the potential to derail mobile learning outcomes. Cognitive load “can be defined as a multidimensional construct representing the load that performing a particular task imposes on the learner’s cognitive system” (Paas et al., 2003). People only have so much working memory with which to process and store information. For mobile learning, researchers have wondered whether the additional mental load, imposed by the environment, affects learning.

The relatively few studies testing cognitive load and mobile learning have been decidedly mixed (Chu, 2013) (Liu et al., 2012). Although in Chu’s study the mobile learners had a higher mental load than the control resulting in a negative learning outcome, the relatively young age and field trip environment may have also contributed (2013). In a 2012 study of the effects of split attention and redundancy on mobile learning, the effects were detected but only produced a slightly negative learning outcome for redundancy (Liu et al., 2012). Both studies were conducted on elementary students in a controlled environment and both authors concluded with recommendations for further study.

Many of the mobile learning device constraints, such as an outdated OS, lie beyond the reach of mobile learning designers. Pereira & Rodrigues suggest that many of the physical or technological limitations of the device, such as size of screen and multimedia issues are easily overcome by use of a tablet (2013). However the increasing 3G and 4G network capabilities will decrease these limitations allowing for more videos and multimedia features to become part of mobile learning modules.

The literature does contain recommendations for the design of mobile learning projects.

In their paper on ‘Message design for mobile learning’, Wang & Shen (2012) have developed four principles for designing for multiple devices: LUCAS 4

1. “Design for the least common denominator” – When building a mobile learning system, create it for the most limited device that may use it.

2. “Design for eLearning, adapt for mLearning” – Many of the best practice advice for eLearning is still applicable to mobile learning. Designers should make small adaptations for the device after the eLearning project.

3. “Design short, condensed materials for smartphones” – Due to limited screen size, material needs to compact for smartphones, which constitutes a large number of mobile devices.

4. “Be creative when designing for mobile devices with 3G and 4G technologies” – Keeping interest is always a factor in education. (Wang & Shen, 2012).

Wang and Shen also encourage designers to use audio as both an output and input mechanism, as well as captions and offer guidelines for the use of color on mobile devices (2012).

As for research studies, the literature is rather sparse on tested recommendations that mitigate the constraints of the physical device.

Mobile Learning Study Koole provides a framework for mobile learning she calls the Framework for the Rational Analysis of Mobile Education (FRAME) model (2009). The model consists of three aspects: device, social and learner, the characteristics of which combine to create a mobile learning process. The usefulness of this model is that the device aspect is “on equal footing” as the more conventional social and learner aspects (Koole, 2009).

Hypothesis: Using the FRAME model, this study proposes that learning modules created with a combination of a single concept module coupled with scaffolding features that deliver just-in-time support will mitigate the cognitive load, screen size, and any current multimedia constraints.

The hypothesis’s design recommendations appears in Koole’s FRAME model at the intersection of the Learner Aspect, Device Usability, Interaction Learning and Mobile Learning (Figure 1.) See Appendix A for a checklist describing the features of these sections.

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Figure 1

Single Concept Modules Research has indicated that offering a single concept at a time reduces the effect of cognitive overload. Mayer and Moreno suggest replacing visual text with auditory information, providing signaling cues such as headings or maps, and allowing time before introducing new concepts (2003). Meyer recommends designers of mobile learning products should focus on single concept modules based on ‘thin-slicing’ (2014).

She describes thin slicing as “a term used in psychology to describe how our brains can intuitively spot narrow windows of experience, and with very limited information draw powerful and surprisingly accurate conclusions” (Meyer, 2014).

In a 2008 study by GD. Chen et al., university students were introduced to “ubiquitous learning environment…integrating a cell phone and learning website” (Chen et al, 2008). The ‘learning status awareness module’ delivered (by SMS message) the learning concept deemed highest priority for the specific student by the system, after determining what the student needed to know (Chen et al., 2008). The qualitative data, or student satisfaction were positive while the quantitative data on test scores only indicated improvement for middle to lower level students (Chen et al., 2008). This further confirms the ‘thin-slicing’ or single concept approach to mobile learning modules. It also agrees with Wang & Shen’s second principle for mobile learning design (2012).

Scaffolding Features While single concept models may reduce cognitive load, integrating scaffolding techniques may be key to mobile learning’s potential. Scaffolding provides support when the student needs it but then gradually reduces that support (Chengjiu, Yanjie, et al., 2013). In an early study done in 2003, Chen et al. examined the use of the scaffolding, an instructional model for “supporting learning activities that reflect authentic task situations”, in a mobile based bird watching application geared toward elementary students (Chen et al., 2003). Chen et al. argued that the following mobile learning characteristics dovetail neatly into the structure of the scaffolding technique: LUCAS 6

     

“Urgency of learning need” “Initiative of knowledge acquisition” “Mobility of learning setting” “Interactivity of the learning process” “Situating of instructional activity” “Integration of the instructional content” (Chen et al., 2003)

The quantitative analysis of the pre and post test results indicated that the students “improved their learning, above and beyond what would normally be expected they would learn” (Chen et al., 2003). Chengjiu et al. used scaffolding features, such as ‘point out mistake’, ‘hint’ and ‘discussion’, in a mobile learning module for teaching complex algorithms with a positive outcome (2013). Incorporating these small ‘helps’ also works with the device’s screen size and may even be useful for devices with lower bandwidth as the system only provides information as needed.

Method Previous studies in mobile learning applications, while technically mobile, were limited to a specific physical location and time, such as instructor-led nature walks (Chen, et al., 2003) (Liu, et al., 2012) or field trips (Chu, 2014). Other studies using university students either focused solely on perceived usefulness (Rui-Ting, Chia-Hua, et al., 2014) (Wei-Han Tan, et al., 2012) or were limited to math or biology specific class subjects (Won, Jang, Kyong-Jee, 2014), ignoring the university humanities population entirely.

This study aims to broaden the subject base to include all incoming university students and test the recommended design (Hypothesis). The New Jersey Institute of Technology enrolls [1,000] incoming freshmen. All incoming students will receive an email with a link to download a mobile application.

Two mobile learning applications will be created for teaching the proper citation in an academic paper consisting of three subjects:   

Styles of citation – The first section will discuss the two dominant styles of citation: bibliography and author-date styles.

Using Quotations – The second section will discuss the use of quotations in papers and proper citation and punctuation of such quotations.

Original Work – The third section will discuss the ethical considerations of what is original work, cited work and common knowledge.

The actual text will be paraphrased from A Manual for Writers of Research Papers, Theses, and Dissertations by Kate L. Turabian, Eighth Edition (2013). This text focuses on the Chicago style, which while not used by all instructors, still provides a solid and relevant background to the subject matter and will be useful to the incoming students.

Both apps will be available for free download to a mobile device to incoming students.

Students will be randomly directed by email to download either the control application or the test application.

Control Application The control learning applications will begin with a short pre-test of three questions to indicate prior knowledge. This is followed by single long module that LUCAS 7

covers proper citation followed by a seven question test of this knowledge. While delivered on a mobile device it will be similar to reading a document. After completing the module, students will be given a survey with Likert-style questions about their mobile learning experience.

Test Application The test learning applications will begin with a short three question pre-test to indicate prior knowledge. This is followed by a series of learning modules that follow current design recommendations and cover proper citation – styles of citation, using direct quotes, and original work. The learning modules will give a short test after each concept. After completing all modules, students will be given a survey with Likert-style questions about their mobile learning experience. The test application should also conform to the FRAME model checklist included in Appendix A. The test learning module will use the single concept method to teach each of the three main subjects in the learning module. In addition, the test module will provide additional links within the text to examples, providing scaffolding support as needed. The test questions relevant to the subject will be given after each single concept portion is completed.

While it may seem that creating two applications is double the work, it is important to note that the exact content of both applications will be the same. The difference is that one application will have several modules following design recommendations while the other has one. All pre-test and test questions will be the same.

The students will be asked to complete a survey upon completion of the mobile learning module. The questions are the same for both the control and the test group. Figure 2 provides a mock-up of what the survey may look like in the mobile learning application.

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Figure 2

Assumptions and Limitations The population is university students, which tend be relatively young and have at least a high school diploma (as they are entering a university). The ratio of male and female participants should reflect the university’s population. The students should also presumably have or have access to a mobile device.

We are relying on students to download and complete the learning module. With the university’s cooperation we can compel the students to complete the module through email reminders.

Analyzing the Data The data collected will be analyzed in the following way: 1. The pre-test results gathered and calculated for percentage correct mean and standard deviation.

2. The learning module test results gathered and calculated for percentage correct mean and standard deviation.

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3. The survey results gathered and calculated for mean and standard deviation.

Results Table for Pre-Test and Test Questions Pre-test Group

Test

Control

Test

Control

Test

Sample Size (n) Mean Score SD

For the survey questions, Question 2 and Question 3 relate directly to this proposal’s hypothesis and thus will be used to in the discussion of the study’s results.

Results Table for Survey Questions Question 1 Group

Control

Question 2 Test

Control

Question 3 Test

Control

Test

Sample Size (n) Mean Score Margin of Error Strongly Agree Agree Neither Disagree Strongly Disagree

Schedule The following steps outline the schedule for the study: 1. Create the learning plan for the modules on proper citation, including lesson and post-test. Include in the plan scaffolding devices such as ‘hint’ and ‘point out mistake’. (2 weeks) 2. Create pre-test and post-survey. (1 week) 3. Build learning applications using the learning plan. (1 month) 4. Link both learning applications to a server for recording the data.

5. Distribute the applications to incoming university students, through the university’s website and email. Indicate deadline for completing the modules. (23 weeks) 6. Analyze data. (1 week)

Conclusion Mobile learning continues to interest researchers as the technology grows and global access expands. This is a large scale study with the aim of providing real data on how to best deliver mobile learning to mobile devices. The quantitative and qualitative results produced by this study could further researchers in mobile learning on best practices and recommendations for future applications.

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Appendix A This is Marguerite L. Koole’s FRAME checklist for designing and assessing mobile learning systems (see references for source). I have included the relevant sections to this proposal.

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References Chen, G., Wang, C., & Chang, C. (2008). Ubiquitous learning website: Scaffold learners by mobile devices with information-aware techniques. Computers And Education, 50(1), 77-90.

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Chen, Y., Kao, T., & Sheu, J. (2003). A mobile learning system for scaffolding bird watching learning. Journal Of Computer Assisted Learning, 19(3), 347-359.

Chengjiu, Y., Yanjie, S., Yoshiyuki, T., Hiroaki, O., & Gwo-Jen, H. (2013). Developing and Implementing a Framework of Participatory Simulation for Mobile Learning Using Scaffolding.

Journal Of Educational Technology & Society, 16(2), 137-150.

Chu, H., Hwang, G., Tsai, C., & Tseng, J. (2010). A two-tier test approach to developing locationaware mobile learning systems for natural science courses. Computers And Education, 55(4), 1618-1627.

Frohberg, D., C. Göth, and G. Schwabe. "Mobile Learning projects – a critical analysis of the state of the art." Journal Of Computer Assisted Learning 25, no. 4 (August 2009): 307-331.

Hashemi, Masoud, Masoud Azizinezhad, Vahid Najafi, and Ali Jamali Nesari. 2011. "What is Mobile Learning? Challenges and Capabilities." Procedia - Social And Behavioral Sciences 30, no. 2nd World Conference on Psychology, Counselling and Guidance - 2011: 2477-2481.

Hui-Chun, Chu. 2014. "Potential Negative Effects of Mobile Learning on Students' Learning Achievement and Cognitive Load--A Format Assessment Perspective." Journal Of Educational Technology & Society 17, no. 1: 332-344.

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Looi, C., Seow, P., Zhang, B., So, H., Chen, W., & Wong, L. (2010). Leveraging mobile technology for sustainable seamless learning: a research agenda. British Journal Of Educational Technology, 41(2), 154-169.

Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal Of Educational Psychology, 93(1), 187-198.

Mayer, R. E., & Moreno, R. (2003). Nine Ways to Reduce Cognitive Load in Multimedia Learning. Educational Psychologist, 38(1), 43-52.

Meyer, Stephanie, smeyer@rapidlearninginstitute.com. "The Effect of the 'New' E-Learning on Soft Skills Training." T+D 68, no. 7 (July 2014): 56-59.

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Nagi, K. "Using mobile devices for educational services-a case study of student expectations."

IEEE Region 10 Annual International Conference, Proceedings/TENCON no. 2008 IEEE Region 10 Conference, TENCON 2008 (January 1, 2008) Ozuorcun, Nilcan Ciftci, and Feride Tabak. 2012. "Is M-learning Versus E-learning or are They Supporting Each Other?." Procedia - Social And Behavioral Sciences 46, no. 4th WORLD CONFERENCE ON EDUCATIONAL SCIENCES (WCES-2012) 02-05 February 2012 Barcelona, Spain: 299-305.

Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. M. (2003). Cognitive Load Measurement as a Means to Advance Cognitive Load Theory. Educational Psychologist, 38(1), 63-71.

Pereira, Orlando R. E., and Joel J. P. C. Rodrigues. 2013. "Survey and Analysis of Current Mobile Learning Applications and Technologies." ACM Computing Surveys 46, no. 2: 27:127:35.

Rui-Ting, Huang, Hsiao Chia-Hua, Tang Tzy-Wen, and Lien Tsung-Cheng. 2014. "Exploring the Moderating Role of Perceived Flexibility Advantages in Mobile Learning Continuance Intention (MLCI)." International Review Of Research In Open & Distance Learning 15, no. 3: 140-156.

Stanton, Genevieve, and Jacques Ophoff. 2013 “Towards a Method for Mobile Learning Design.”

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Wang, M., & Shen, R. (2012). Message design for mobile learning: Learning theories, human cognition and design principles. British Journal Of Educational Technology, 43(4), 561-575.

Wei-Han Tan, Garry, Ooi Keng-Boon, Sim Jia-Jia, and KongKiti Phusavat. 2012. "Determinants of Mobile Learning Adoption: An Empirical Analysis." Journal Of Computer Information Systems 52, no. 3: 82-91.

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