E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Las Vegas, NV, United States ISBN 978-1-939797-35-3 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA
The purpose of this study was to investigate the effects of the use of internet and phone during learning on students’ performance in College of Education, and Engineering & Applied Sciences in a southern university. The present study utilized the theory of mind-wandering framework which postulates that if students experience task-unrelated thoughts and do not remain on a single topic for a long period of time, the students will be distracted from the main learning task and consequently will perform poorly on the main task. Researchers employed between the subjects a design method where data for questionnaire was collected using paper-pencil. For reading task, the assignment and collection of data was either digital or paper-based. Participants were 55 students from the College of Engineering & Applied Sciences and 41 from the College of Education. SAS software was used for all analyses. Stepwise variable selection and chi-square test for independence was used to address research questions. Results of this experiment found that irrespective of whether the task was given digitally or paper-based, it was the learning style that affected the assignment-score. In addition, irrespective of the assignment type (digital or hard-copy), it was the amount of time spent on internet and phone that affected the task completion time. Finally, students who were assigned digital copy of the task were more likely to visit the non-assignment related websites as well as access online help.
Patil, R., Brown, M., Ibrahim, M., Callaway, R. & Hamidi, R. (2018). The Effects of the Use of Internet and Phone on Students’ Performance across Different Disciplines. In Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 642-650). Las Vegas, NV, United States: Association for the Advancement of Computing in Education (AACE). Retrieved February 22, 2019 from https://www.learntechlib.org/primary/p/185019/.
© 2018 Association for the Advancement of Computing in Education (AACE)
- Agresti, A. (1996). An introduction to categorical data analysis. New York, NY: WileyInterscience.
- Becker, S.A., Cummins, M., Davis, A., Freeman, A., Hall, C.G., & Ananthanarayanan, V. (2017). NMC horizon report: 2017 higher education edition: The New Media Consortium.
- Bjornsen, C.A., & Archer, K.J. (2015). Relations between college students’ cell phone use during class and grades. Scholarship of Teaching and Learning in Psychology, 1(4), 326.
- Brown, I., Stothers, R., Thorp, S., & Ingram, L. (2006). The role of learning styles in the acceptance of web-based learning tools. Paper presented at the 36th Annual Conference of the Southern African Computer Lecturers Association SACLA2006.
- Buch, K., & Bartley, S. (2002). Learning style and training delivery mode preference. Journal of workplace learning, 14(1), 5-10.
- Collins, A., & Halverson, R. (2018). Rethinking education in the age of technology: The digital revolution and schooling in America: Teachers College Press.
- Dahlstrom, E., Walker, J., & Dziuban, C. (2013). ECAR study of undergraduate students and information technology: 2013.
- Elder, A. (2013). College students' cell phone use, beliefs, and effects on their learning. College Student Journal, 47(4), 585-592.
- End, C.M., Worthman, S., Mathews, M.B., & Wetterau, K. (2009). Costly cell phones: The impact of cell phone rings on academic performance. Teaching of Psychology, 37(1), 5557.
- Felder, R.M., & Silverman, L.K. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7), 674-681.
- Froese, A.D., Carpenter, C.N., Inman, D.A., Schooley, J.R., Barnes, R.B., Brecht, P.W., & Chacon, J.D. (2012). Effects of classroom cell phone use on expected and actual learning. College Student Journal, 46(2), 323-332.
- Halbert, C., Kriebel, R., Cuzzolino, R., Coughlin, P., & Fresa-Dillon, K. (2011). Self-assessed learning style correlates to use of supplemental learning materials in an online course management system. Medical Teacher, 33(4), 331-333.
- Kolb, A.Y. (2005). The Kolb learning style inventory–version 3.1 2005 technical specifications. Boston, MA: Hay Resource Direct, 200, 72.
- Kolb, D.A. (1976). Learning style inventory technical manual: McBer Boston, MA.
- Lawson, D., & Henderson, B.B. (2015). The costs of texting in the classroom. College Teaching, 63(3), 119-124.
- Levinson, D.B., Smallwood, J., & Davidson, R.J. (2012). The persistence of thought: evidence for a role of working memory in the maintenance of task-unrelated thinking. Psychological science, 23(4), 375-380.
- Mayer, R.E. (2005). The Cambridge handbook of multimedia learning: Cambridge university press.
- McVay, J.C., & Kane, M.J. (2012). Why does working memory capacity predict variation in reading comprehension? On the influence of mind wandering and executive attention. Journal of experimental psychology: general, 141(2), 302.
- Michalski, K. (2008). Learning styles and blended learning: Challenges and opportunities in distance education environment. Paper presented at the E-Learn: World Conference on ELearning in Corporate, Government, Healthcare, and Higher Education.
- Rodriguez, J.E. (2011). Social media use in higher education: Key areas to consider for educators.
- Simpson, C., & Du, Y. (2004). Effects of learning styles and class participation on students' enjoyment level in distributed learning environments. Journal of education for library and information science, 123-136.
- Smith, A., & Anderson, M. (2018). Social Media Use in 2018. Pew Research Center, (2018).
- Terrell, S.R., & Dringus, L. (2000). An investigation of the effect of learning style on student success in an online learning environment. Journal of Educational Technology Systems, 28(3), 231-238.
- Unsworth, N., & McMillan, B.D. (2013). Mind wandering and reading comprehension: Examining the roles of working memory capacity, interest, motivation, and topic experience. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(3), 832.
- Willingham, D.T., Hughes, E.M., & Dobolyi, D.G. (2015). The scientific status of learning styles theories. Teaching of Psychology, 42(3), 266-271.
These references have been extracted automatically and may have some errors. If you see a mistake in the references above, please contact email@example.com.
- presentation_3087_53411.pptx (Access with Subscription)
- Patil_Brown_Ibrahim__Callaway_Hamidi_E_Learn_2018__Las_Vegas_Final_53411.pptx (Access with Subscription)