This document discusses the use of mobile learning (m-learning) in undergraduate education. It begins by introducing m-learning and how it allows students to access educational materials anywhere through mobile devices. It then states the purpose is to study the effects of m-learning tools on student academic achievement. Finally, it reviews literature that has found m-learning can enhance the learning process and student performance when used as a supplement to traditional classroom learning.

1. Abstract
Introduction
In a time when mobile devices such as smart phones and digital media players are
ubiquitous accessories of college students, it stands to reason that these devices are an avenue
down which educational information could be delivered at the undergraduate level. Several
studies indicate that the use of supplementary aids, such as podcasts, provide learners with
alternative access to lectures, notes, and summaries of curricular information.
With the incorporation of mobile learning into many undergraduate classrooms, students
are realizing benefits of an expanded classroom and access to an abundance of resources. Mobile
learning, or m-learning, provides the traditional student with opportunities for anytime learning
through the use of everyday devices, such as cell phones, netbooks, and laptops (REF).
Instruments for m-learning in this research proposal will be provided to the experimental group
and the course professor via a restricted access web-enabled smart phone to facilitate sampling
that does not require the personal economic means to own one. The students in the experimental
group and the professor use the provided iPhone smart phones to facilitate the delivery of online
materials that is available only to the experimental group.
The purpose of this research proposal is to address the effects of m-learning tools on
students in classes which incorporate m-learning as part of the curriculum, a learning trend that is
forecasted to expand. The research question to be considered in this proposal is, "Does m-
learning in undergraduate education affect academic achievement?"

2. Literature Review
M-learning is defined by McConatha, Praul, and Lynch as “learning that is accomplished
with the use of small, portable computing devices” (2008). As e-learning enables learning
outside of a classroom, m-learning enables learning irrespective of location (Wang, Wu, &
Wang, 2009). It is a relatively new tool in the pedagogical arsenal that provides the traditional
student with opportunities for learning anytime through the use of everyday devices such as cell
phones, netbooks, and laptops. Researcher Brian Alexander views the term m-learning as one
that often incorrectly implies wireless capabilities in conjunction with mobile technology (2004).
In addition to the portability and efficiency, Alexander notes that the perceived privacy of mobile
technology is a clear advantage. Mobile technology allows research to reach a new dimension in
which collaboration is not limited to the lab. It is able to extend into the field with increased ease
enabling collaboration outside of the local community to partners throughout the world for both
sharing and feedback (Alexander, 2004). With the incorporation of m-learning into many
undergraduate classrooms through both teaching and research, students are able to realize the
benefits of an expanded classroom and receive access to an abundance of resources.
Fozdar and Kumar discuss m-learning as an effective tool for enhancing the teaching-
learning process (2007). The study measures students’ attitudes and perceptions on the
effectiveness of m-learning. After a pilot test of 25 students, the authors conducted a 33 item
questionnaire using a Likert scale to determine the perceived effectiveness of mLearning. There
were 32 female and 33 males who responded for a 65% response rate, and though the sample
size is relatively small, the results of their study clearly indicate that m-learning can be an
effective way of learning. Zurity and Nussbaum research the supplementation of mobile
technological resources specifically within constructivist learning environments (2004). Findings

3. confirm that m-learning can be applied in constructivist settings with positive impacts on student
learning. While the study was developed based on face-to-face student interaction, these
researchers successfully transferred the key principles of “constructive, active, significant,
reflexive, collaborative and based on consultation” to the handheld technology setting.
Educational content was not merely provided as a complement to direct instruction, but m-
learning was successfully utilized as the key component in the creation of authentic student
work, extending knowledge based upon peers’ contributed work via the handheld devices (2004).
Student access to immediate feedback when using the m-learning devices may stand as a
contributing factor in the resulting increased post-test scores.
It has been proposed that digital audio in particular is an inexpensive and easy way to
produce elements that are successful in affecting attention, motivation, and interest (Chan & Lee,
2005). Podcasts, delivered via downloadable files from the internet, is one format that enables a
student to choose content and view it when desired, potentially creating listening time that would
otherwise be spent doing automatic tasks such as walking home or riding on the bus. Evans
(2008) suggests that “podcasting can fill an important needs gap by allowing learners to continue
learning activities when it might not normally be possible.” This offers students more control
over their learning process and provides the learner with an active relationship with the class
material, ultimately constructing their own understanding of it. Material is delivered to students
through a push method, allowing the ease of acquisition to become a tangible benefit and
ensuring that it is an “efficient, effective, engaging, and easily received learning tool for
revision” (2008). Evans and others intended to measure m-learning but did not control
technology to ensure strictly mobile access to content. In fact, over 80% of the participants in
Evans’ experiment were discovered to have chosen personal computers to access the material, a

4. device that does not meet the portability guidelines of most m-learning definitions (2008).
McKinney, Dyck, and Luber (2009) use the same podcast technology but ensure m-learning
content delivery through iTunes University, a website with downloadable educational podcasts.
This study utilizes podcasts to deliver content in lieu of obtaining the notes from a missed lecture
as opposed to previous studies that examined material designed to enhance a lecture. The results
are generously in favor of podcasts for this particular use, with 88% of the experimental group
indicating future preference for podcasts over borrowed class notes in the event of a missed
class. Students in the experimental group performed significantly higher on exams and took more
detailed notes. Students appeared to value the ability to stop, rewind, and pause at will as well as
the opportunity to listen to the podcasts at any time of day.
Since the majority of research in this area showcases positive relationships between the
implementation of m-learning and student learning experiences, Jie Chi Yang and Yi Lung Lin
(2010) use this information to hypothesize positive effects on student-to-student information
sharing and collaborative learning through handheld mobile devices. By utilizing a shared
display groupware, Yang and Lin (2010) create an effective means for students to share
information and work with a group while maintaining the original information when using the
handheld devices. With the implementation of shared display groupware, users are not only able
to apply the handheld devices for course task completion, but to effectively facilitate group
discussion and sharing as well. The quality of material was not sacrificed due to small screen
size and students were still able to work collaboratively, seamlessly employing the technology.
The small screen size is one of the many factors that play into effort expectancy, a construct used
to measure belief of ease of utilization of m-learning and one of the five determinants of m-
learning acceptance studied by Y. Wang, Wu, and H. Wang (2009). The research team also

5. studies performance expectancy (belief that an individual will attain job performance benefits),
social influence (belief that important others view the individual as a m-learning user), perceived
playfulness (level of cognitive spontaneity), and self-management of learning (belief in ability to
engage in self-directed autonomous learning) with respect to age and gender to understand the
acceptance of m-learning technology. They discover that each of the five categories is a
significant determinant of behavioral intention for both genders with the exception of social
influence for women. Additionally, all determinants were significant for both age categories (<
30 and ≥ 30), but social influence and effort expectancy are stronger predictors of m-learning
usage intention for the older group. This information is useful to targeting the audiences of m-
learning with marketing techniques that are valuable for certain demographics and guides
technology improvements that will aid the acceptance of m-learning throughout society.
The rapidly changing and often complex technology found in the m-learning arena can
make it difficult for students to gain the skills and the knowledge through university curricula as
quickly as needed in today’s world. In conjunction with industry, university technical services, as
well as various academic areas, Indiana University developed a graduate level course called
Mobile Application Development to address this issue (Massey, Ramesh, & Khatri, 2006). The
course provided a way to immerse both students and faculty in the development of mobile
technologies using problem based learning techniques with the dual goals of creating future
technical leaders in emerging mobile technologies and to expanding students’ knowledge base
beyond end users to developers and decision makers. Since many students today have
expectations of conducting university coursework from anywhere and at any time, this course
bridged a gap from merely using the mobile technology for learning to actually developing the
technology for future learners.

6. Much research on m-learning is completed in a higher educational setting with the goal of
enhancing student achievement or learning in some way. In a recent study completed by Chao-
Hsiu Chen (2010), m-learning’s versatility is exhibited through a self and peer assessment
endeavor in a teacher education courses. This study demonstrates the use of PDAs to facilitate
assessments in the classroom, thereby allowing students more opportunities for reflection on
their own and others’ presentations. Using mobile devices for peer and self-assessments was
hypothesized to enhance students’ abilities to better evaluate performance standards, more
effectively foster interaction, and better focus attention on in-class presentations. Because the
students could easily both give and receive timely feedback more efficiently on the portable
devices, they were able to compare, reflect on, and improve on their presentation, which in turn
led to improved subsequent performances.
McConatha
Conclusion

7. References
Evans, C. (2008). The effectiveness of m-learning in the form of podcast. ScienceDirect , 491-
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Chan, Anthony and Lee, Mark J.W. (2005) An MP3 a day keeps the worries away: Exploring the
use of podcasting to address preconceptions and alleviate pre-class anxiety amongst
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7th September ’05. Wagga Wagga, NSW: Charles Stuart University. Pp. 59–71.
Chen, C. (2010). The implementation and evaluation of a mobile self- and peer-assessment
system. Computers & Education, 55(1), 229-236. Retrieved from ERIC database.
Massey, A., Ramesh, V., & Khatri, V. (2006). Design, development, and assessment of mobile
applications: the case for problem-based learning. IEEE Transactions on Education,
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Alexander, B. (2004). Going Nomadic: Mobile Learning in Higher Education. EDUCAUSE
Review, vol.39, no. 5 (September/October 2004): 28-35. Retrieved June 2, 2010 from
http://net.educause.edu/ir/library/pdf/ERM0451.pdf
McConatha, D., Praul, M., & Lynch, M. J. (2008). Mobile learning in higher education:
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Zurita, G., & Nussbaum, M. (2004). A constructivist mobile learning environment supported by
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8. Yang, J., & Lin, Y. (2010). Development and Evaluation of an Interactive Mobile Learning
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McKinney, D., Dyck, J. L., & Luber, E. S. (2009). iTunes university and the classroom: Can
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Wang, Y., We, M., & Wang, H. (2009). Investigating the determinants and age and gender
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