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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?"
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
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
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
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.
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
References


Evans, C. (2008). The effectiveness of m-learning in the form of podcast. ScienceDirect , 491-

498.


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

undergraduate. Good Practice in Practice. Proceedings of the Student Experience Conference 5-

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,

       49(2), 183-192. doi: 10.1016/j.compedu.2006.03.004.


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:


       An empirical assessment of a new educational tool. The Turkish Online Journal of

       Educational Technology, 7(3), 15-21.


Zurita, G., & Nussbaum, M. (2004). A constructivist mobile learning environment supported by

a wireless handheld network. Journal of Computer Assisted Learning, 20(4), 235-243.

doi:10.1111/j.1365-2729.2004.00089.x.
Yang, J., & Lin, Y. (2010). Development and Evaluation of an Interactive Mobile Learning

Environment with Shared Display Groupware. Educational Technology & Society, 13(1), 195-

207. Retrieved from ERIC database.


McKinney, D., Dyck, J. L., & Luber, E. S. (2009). iTunes university and the classroom: Can

       podcasts replace professors? Computers & Education, 52(3), 617-623.


Wang, Y., We, M., & Wang, H. (2009). Investigating the determinants and age and gender

       differences in the acceptance of mobile learning. British Journal of Educational

       Technology, 40(1), 92-118.


Mobile Learning and Student Retention.

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Research+proposal

  • 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- 498. 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 undergraduate. Good Practice in Practice. Proceedings of the Student Experience Conference 5- 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, 49(2), 183-192. doi: 10.1016/j.compedu.2006.03.004. 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: An empirical assessment of a new educational tool. The Turkish Online Journal of Educational Technology, 7(3), 15-21. Zurita, G., & Nussbaum, M. (2004). A constructivist mobile learning environment supported by a wireless handheld network. Journal of Computer Assisted Learning, 20(4), 235-243. doi:10.1111/j.1365-2729.2004.00089.x.
  • 8. Yang, J., & Lin, Y. (2010). Development and Evaluation of an Interactive Mobile Learning Environment with Shared Display Groupware. Educational Technology & Society, 13(1), 195- 207. Retrieved from ERIC database. McKinney, D., Dyck, J. L., & Luber, E. S. (2009). iTunes university and the classroom: Can podcasts replace professors? Computers & Education, 52(3), 617-623. Wang, Y., We, M., & Wang, H. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92-118. Mobile Learning and Student Retention.