Skip to main content
SpringerLink
Log in

Evidence-Based Teaching and Real-Time Assessment: Adoption of Mobile Interactive Apps

  • Living reference work entry
  • First Online:
Handbook of Mobile Teaching and Learning

  • 129 Accesses

Abstract

Mobile-based in-class educational approaches should help faculty provide an evidence-driven teaching environment. This chapter is going to discuss the theoretical background for such mobile-based approaches and its need in the classroom to provide both students and faculty with a real-time understanding about learning and to help students engage more into traditional lecturing. Additionally, the chapter is going to discuss the way such mobile-centric interactive systems could facilitate more evidence-driven teaching. Finally, the chapter will discuss issues that need to be considered for such adoption and present an example of mobile-based system to facilitate evidence-based teaching.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., … and Wittrock, M. C. 2001. A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives, abridged edition. White Plains, NY: Longman.

    Google Scholar 

  • Avery, Zanj, Mauricio Castillo, Huiping Guo, Jiang Guo, Nancy Warter-Perez, Deborah S. Won, and Jane Dong. 2010. Implementing Corcllaborative Project-Based Learning using the Tablet PC to enhance student learning in engineering and computer science courses. In Frontiers in education conference (FIE), 2010 IEEE, F1E-1. IEEE.

    Google Scholar 

  • Babcock, P., and M. Marks. 2010. The falling time cost of college: Evidence from half a century of time use data, NBER working paper no. 15954. Cambridge, MA: National Bureau of Economic Research.

    Book  Google Scholar 

  • Biesta, Gert. 2007. Why “what works” won’t work: Evidence-based practice and the democratic deficit in educational research. Educational Theory 57 (1): 1–22.

    Article  Google Scholar 

  • Bonwell, Charles C., and James A. Eison. 1991. Active learning: Creating excitement in the classroom, 1991 ASHE-ERIC higher education reports. Washington, DC: ERIC Clearinghouse on Higher Education, The George Washington University.

    Google Scholar 

  • Braxton, John M., Willis A. Jones, Amy S. Hirschy, and Harold V. Hartley III. 2008. The role of active learning in college student persistence. New Directions for Teaching and Learning 2008 (115): 71–83.

    Article  Google Scholar 

  • Caldwell, Jane E. 2007. Clickers in the large classroom: Current research and best-practice tips. CBE Life Sciences Education 6 (1): 9–20.

    Article  Google Scholar 

  • Campbell, Scott W. 2006. Perceptions of mobile phones in college classrooms: Ringing, cheating, and classroom policies. Communication Education 55 (3): 280–294.

    Article  Google Scholar 

  • Cometa, M. 2011. Use of technology-rich learning environment reveals improved retention rates. Rochester University of Technology, Nov 16.

    Google Scholar 

  • Costa, J.C., Timo Ojala, and Jani Korhonen. 2008. Mobile lecture interaction: Making technology and learning click. In IADIS international conference mobile learning, 119–124.

    Google Scholar 

  • De Ridder-Symoens, H., and L. Brockliss. 1996. In Curricula. A history of the university in Europe, ed. H. de Ridder-Symoens, vol. II, 565–620. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Deb, Debzani, M. Muztaba Fuad, and Waleed Farag. 2014. Developing interactive classroom exercises for use with mobile devices to enhance class engagement and problem-solving skills. In Frontiers in education conference (FIE), 2014 IEEE, 1–4. IEEE.

    Google Scholar 

  • Deb, Debzani, Mohammad Muztaba Fuad, and Mallek Kanan. 2017. Creating engaging exercises with mobile response system (MRS). In Proceedings of the 2017 ACM SIGCSE technical symposium on computer science education, 147–152. ACM.

    Google Scholar 

  • Deb, Debzani, Muztaba Fuad, James Etim, and Clay Gloster. 2018. MRS: Automated assessment of interactive classroom exercises. In Proceedings of the 49th ACM technical symposium on computer science education, 290–295. ACM.

    Google Scholar 

  • Derek, B. 2012. Classroom response system bibliography. Vanderbilt Center for Teaching. http://cft.vanderbilt.edu/docs/classroom-response-system-clickers-bibliography/.

  • DeWitt, Dorothy, Norlidah Alias, and Saedah Siraj. 2014. The design and development of a Collaborative mLearning prototype for Malaysian secondary school science. Educational Technology Research and Development 62 (4): 461–480.

    Article  Google Scholar 

  • Draper, S.W. 2002. Evaluating effective use of PRS: Results of the evaluation of the use of PRS in Glasgow University, October 2001–June 2002. Glasgow University.

    Google Scholar 

  • Echo360. 2017. http://echo360.com.

  • E-Clicker. 2017. http://www.bignerdranch.com/apps/eclicker-presenter-ios.

  • Ertmer, Peggy A., and Timothy J. Newby. 1993. Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly 6 (4): 50–72.

    Article  Google Scholar 

  • Fairweather, James. 2008. Linking evidence and promising practices in science, technology, engineering, and mathematics (STEM) undergraduate education. Washington, DC: Board of Science Education, National Research Council. The National Academies.

    Google Scholar 

  • Fang, Berlin. 2009. From distraction to engagement: Wireless devices in the classroom. Educause Quarterly 32 (4): 4–9.

    Google Scholar 

  • Fredericksen, E. E., and Ames, M. 2009. Can a $30 piece of plastic improve learning? An evaluation of personal responses systems in large classroom settings. Educause-Community Contributions. https://library.educause.edu/~/media/files/library/2009/4/csd2690-pdf.pdf.

  • Fuad, M. Muztaba. 2017. Interactive problem solving using mobile devices in the classroom. In Proceedings of the 2017 ACM SIGCSE technical symposium on computer science education, 640–641. ACM.

    Google Scholar 

  • Fuad, Mohammad Muztaba, and Debzani Deb. 2016. Evidence-based teaching with the help of mobile response system (MRS). In Proceedings of the 2016 ACM conference on innovation and technology in computer science education, 242–243. ACM.

    Google Scholar 

  • Fuad, M. Muztaba, Debzani Deb, and James Etim. 2014. An evidence-based learning and teaching strategy for computer science classrooms and its extension into a mobile classroom response system. In Advanced learning technologies (ICALT), 2014 I.E. 14th international conference on, 149–153. IEEE.

    Google Scholar 

  • Fuad, M. Muztaba, Debzani Deb, James Etim, and Clay Gloster. 2016. Using interactive exercise in mobile devices to support evidence-based teaching and learning. In Proceedings of the 2016 ACM conference on innovation and technology in computer science education, 17–22. ACM.

    Google Scholar 

  • Hao, Shuang, Vanessa P. Dennen, and Mei Li. 2017. Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development 65 (1): 101–123.

    Article  Google Scholar 

  • iTestMe. 2017. http://itunes.apple.com/us/artist/lei-bao/id482868428.

  • Jones, Ann, and Kim Issroff. 2007. Motivation and mobile devices: exploring the role of appropriation and coping strategies. Alternatives Journal 15: 247–258.

    Google Scholar 

  • Karavirta, Ville, and Clifford A. Shaffer. 2016. Creating engaging online learning material with the JSAV JavaScript algorithm visualization library. IEEE Transactions on Learning Technologies 9 (2): 171–183.

    Article  Google Scholar 

  • Katsioloudis, P., and T.D. Fantz. 2012. A comparative analysis of preferred learning and teaching styles for engineering, industrial, and technology education students and faculty. Journal of Technology Education 23 (2): 61–69.

    Google Scholar 

  • Khalaf, S. 2014. Apps solidify leadership six years into the mobile revolution. Accessed at http://flurrymobile.tumblr.com/post/115191864580/apps-solidify-leadership-six-years-into-the-mobile.

  • Kim, Paul, Teresita Hagashi, Laura Carillo, Irina Gonzales, Tamas Makany, Bommi Lee, and Alberto Garate. 2011. Socioeconomic strata, mobile technology, and education: A comparative analysis. Educational Technology Research and Development 59 (4): 465–486.

    Article  Google Scholar 

  • Knight, Jennifer K., and William B. Wood. 2005. Teaching more by lecturing less. Cell Biology Education 4 (4): 298–310.

    Article  Google Scholar 

  • Kvernbekk, Tone. 2015 Evidence-based practice in education: Functions of evidence and causal presuppositions, Routledge research in education, Vol. 147. Routledge.

    Google Scholar 

  • Land, Susan M., and Heather Toomey Zimmerman. 2015. Socio-technical dimensions of an outdoor mobile learning environment: A three-phase design-based research investigation. Educational Technology Research and Development 63 (2): 229–255.

    Article  Google Scholar 

  • Mitchell, David. 2010. Education that fits: Review of international trends in the education of students with special educational needs. Education Counts, University of Canterbury.

    Google Scholar 

  • Mockus, L., Dawson, H., Edel-Malizia, S., Shaffer, D., and Swaggerty, A. 2011. The Impact of Mobile Access on Motivation: Distance Education Student Perceptions. http://learningdesign.psu.edu/assets/uploads/pdf/MLRTWhitePaper.pdf.

  • Myller, Niko, Roman Bednarik, Erkki Sutinen, and Mordechai Ben-Ari. 2009. Extending the engagement taxonomy: Software visualization and collaborative learning. ACM Transactions on Computing Education (TOCE) 9 (1): 7.

    Google Scholar 

  • National Research Council. 2015. Identifying and supporting productive STEM programs in out-of-school settings. Washington, DC: The National Academies Press.

    Google Scholar 

  • Penuel, William R., Christy Kim Boscardin, Katherine Masyn, and Valerie M. Crawford. 2007. Teaching with student response systems in elementary and secondary education settings: A survey study. Educational Technology Research and Development 55 (4): 315–346.

    Article  Google Scholar 

  • Perry, Bruce. 2000. How the brain learns best. Instructor 110 (4): 34–35.

    Google Scholar 

  • Prince, Michael. 2004. Does active learning work? A review of the research. Journal of Engineering Education 93 (3): 223–231.

    Article  Google Scholar 

  • QuizCast. 2017. https://itunes.apple.com/us/app/quizcast/id495598997?mt=8.

  • Romney, Carla A. 2011. Tablet PC use in freshman mathematics classes promotes STEM retention In Frontiers in education conference (FIE), 2011, F1J-1. IEEE.

    Google Scholar 

  • Roschelle, Jeremy, William R. Penuel, and Louis Abrahamson. 2004. Classroom response and communication systems: Research review and theory. In Annual meeting of the American Educational Research Association, 1-8, San Diego.

    Google Scholar 

  • Saville, B.K. 2009. Using evidence-based teaching methods to improve education. Teaching and Learning Excellence 14: 32.

    Google Scholar 

  • Socrative. 2017. https://itunes.apple.com/us/artist/socrative/id477618133.

  • Top Hat Monocle. 2017. https://www.tophatmonocle.com.

  • Urquiza-Fuentes, Jaime, and J. Ángel Velázquez-Iturbide. 2009. A survey of successful evaluations of program visualization and algorithm animation systems. ACM Transactions on Computing Education (TOCE) 9 (2): 9.

    Google Scholar 

  • Vrasidas, Charalambos. 2000. Constructivism versus objectivism: Implications for interaction, course design, and evaluation in distance education. International Journal of Educational Telecommunications 6 (4): 339–362.

    Google Scholar 

  • Weimer, M. 2018. Confronting the myth of multitasking: A collection of tools and resources. Faculty Focus, Magna Publishers.

    Google Scholar 

  • Wu, Wen-Hsiung, Yen-Chun Jim Wu, Chun-Yu Chen, Hao-Yun Kao, Che-Hung Lin, and Sih-Han Huang. 2012. Review of trends from mobile learning studies: A meta-analysis. Computers & Education 59 (2): 817–827.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Winston-Salem State University, Winston-Salem, NC, USA

    Muztaba Fuad

Authors
  1. Muztaba Fuad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muztaba Fuad .

Editor information

Editors and Affiliations

  1. CEO, WEMOSOFT, Wollongong, NSW, Australia

    Yu (Aimee) Zhang

  2. Teaching and Learning, The Ohio State University, Upper Arlington, OH, USA

    Dean Cristol

Section Editor information

  1. College of Education and Human Ecology, Faculty of Mathematics Education, The Ohio State University at Lima, Lima, Ohio, USA

    Hea-Jin Lee

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer-Verlag GmbH Germany, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Fuad, M. (2019). Evidence-Based Teaching and Real-Time Assessment: Adoption of Mobile Interactive Apps. In: Zhang, Y., Cristol, D. (eds) Handbook of Mobile Teaching and Learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41981-2_100-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41981-2_100-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41981-2

  • Online ISBN: 978-3-642-41981-2

  • eBook Packages: Springer Reference Social SciencesReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Search

Navigation