According to The U.S. Mobile App Report, 60% of all digital media time in the U.S. is spent through smartphones and tablets (Lella & Lipsman, 2014). There is an interest to further explore and understand how mobile and digital technologies can be integrated into community programs (Center for Advancement of Informal Science Education [CAISE], 2015). While there is demonstrated interest in reaching young underrepresnted groups (URG’s), or those with histories of low interest and success rates in the STEM disciplines in informal education programs (e.g., Hudson, Duncan, Reeve, 2015; National Research Council, 2015; The White House, 2015), and an interest to explore the added benefits of incorporating mobile learning in informal contexts (e.g., Cahill et al., 2011; O’Hara et al., 2007), there is not much focus on which types of content, tools, and motivational assets are most useful and engaging to this particular group of learners in informal education programs. Moreover, it is not clear if the URG’s in these contexts would have the means to, or even prefer to, engage through a mobile device if given the choice. It is essential, as urged recently by national leaders, to consider barriers for this population and discuss possible ways to provide access (The White House, 2016).
This supports our CEOs most recent research (e.g., Anderson, 2015; Anderson & Tredway, 2009; Anderson, Tredway, & Calice, 2015) which focused on the importance of understanding the external and internal influences in not only understanding the learning context (Kaptelinin, 2013), but also for designing instruction. The work that Luma has performed over the past 8 years supports the notion that learning experiences are situated and tied to context, culture, and activity. Further, multimedia tools and readings in context of the learning activities, together, shape learning and influence internalization (Brown et al., 1989; Cobb, 1994; Gibson, 1977; Kaptelinin & Nardi, 2006). Most traditional learning experiences include the same type of learning content delivered the same way to all types of learners across learning platforms. However, Luma has found that content design and selection for one particular learning context and audience cannot necessarily be applied to all learning environments.
Consider what your learning environments look like for your learners. If you are delivering the same thing to all learners in all learning contexts, you may not be meeting their learning needs. By collecting data while learners are engaging in the content, you can learn a great deal about your learner’s needs and thus have measurably better learning outcomes.
Partner with Luma today to help you advance the knowledge of learning at your organization. Contact us at firstname.lastname@example.org
Anderson, G. L. (2015). An exploration of multimedia use in an online RN-BSN program (Doctoral dissertation). Retrieved from IUScholarWorks. (http://hdl.handle.net/2022/20945)
Anderson, G. L., & Tredway, C. (2009). Transforming the nursing curriculum to promote critical thinking online. Journal of Nursing Education, 48(2), 111-115.
Anderson, G. L., Tredway, C., & Calice, C. (2015). A longitudinal study of nursing students’ perceptions of online course quality. Journal of Interactive Learning Research, 26(1), 5-21.
Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32-42. Retrieved from http://www.jstor.org/discover/10.2307/1176008?uid=3739256&uid=2129&uid=2&uid=70&uid=4&sid=56013087773
Center for Advancement of Informal Science Education. (2015). Design considerations for integrating mobile technology into informal science learning environments. Retrieved from http://www.informalscience.org/knowledge-base/design-considerations-integrating-mobile-technology-informal-science-learning-environments
Cobb, P. (1994). Where is mind? Constructivist and sociocultural perspectives on mathematical development. Educational Researcher, 23(7), 13-20.
Gibson, J. J. (1977). The theory of affordances. In R. Shaw & J. Bransford (Eds.), Perceiving, acting, and knowing (pp. 67-82). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
Hudson, R. C., Duncan, S., & Reeve, C. (2015). Affinity spaces for informal science learning: Developing a research agenda. Retrieved from http://www.informalscience.org/sites/default/files/AffinitySpacesFinalReport.pdf
Kaptelinin, V. (2013). Activity theory. In M. Soegaard & R. F. Dam (Eds.), The encyclopedia of human-computer interaction (2nd ed.). Aarhus, Denmark: The Interaction Design Foundation.
Kaptelinin, V., & Nardi, B. A. (2006). Acting with technology: Activity theory and interaction design. Cambridge, MA: The MIT Press.
Lella, A., & Lipsman, A. (2014). The U.S. mobile app report. Retrieved from http://informalscience.org/research/ic-000-000-009-950/The_US_Mobile_App_Report
National Research Council. (2015). Identifying and supporting productive STEM programs in out-of-school settings. Washington, DC: National Academies Press. doi: 10.17226/21740
The White House. (2016, October 28). Inclusive STEM education for youth of color [Video file]. Retrieved from https://www.youtube.com/watch?v=DVz2X8RExIw
The White House, Office of the Press Secretary. (2015). FACT SHEET: President Obama Announces Over $240 Million in New STEM Commitments at the 2015 White House Science Fair [Press release]. Retrieved from https://www.whitehouse.gov/the-press-office/2015/03/23/fact-sheet-president-obama-announces-over-240-million-new-stem-commitmen