Working Towards Technology-Enabled Personalized Learning

I watched a great TED talk by Richard Culatta (2013) this week. It helped me reflect on what he calls the digital divide (the disparity between educators using technology to replicate old practices versus transforming teaching and learning). One key to transformative learning with technology is personalization so I found two research articles on personalized learning to help me dig deeper into the topic.

The first article, Fostering Personalized Learning in Science Inquiry Supported by Mobile Technologies (Song, Wong, & Looi, 2012), raised the idea that technology can be a mediator in developing student agency in personalized learning.

The study examined the effects of giving fourth-graders cell phones connected to an experiential learning study (Song et al., 2012). Students used their devices to participate in a mobile learning environment (MLE), scaffolding their entry into personalized, inquiry-based learning of the life cycles of animals and plants. The researchers investigated whether technology could be used not as the learning goal but simply a tool to access the MLE across contexts (i.e., school, farm, and home) and engage in documentation, reflection, research, and the creation of learning artifacts. They found that students who used the MLE and participated in continued learning after the field trip (e.g., raising a butterfly or growing spinach at home) experienced deeper learning. I appreciated their insight into creating a systematic process to help students become lifelong learners by providing tools (devices and a MLE) that guide them to consciously personalize their learning by actively making choices to reach their learning goals.

The second article, An Asynchronous, Personalized Learning Platform – Guided Learning Pathways (GLP) (Shaw, Larson, & Sibdari., 2014), proposed personalizing learning with a platform to transform how students learn.

The researchers shared a design for a new platform that students could access in formal and informal learning environments (Shaw et al., 2014). It combines students’ unique data to construct a personalized “guided learning pathway” that would constantly update in response to how students’ learn. The platform would be structured around expert-created content maps, guiding students to learn specific content. These maps are layered onto personalized visualizations, such as a map of baseball stadiums if the student reports being interested in baseball. As the student enters a stadium, she can choose from different “nuggets” of content which offer varied ways of learning (e.g., text, games, video) and at any point, students can take an assessment to measure mastery of the content. The available nuggets change in response to the assessment using a recommendation algorithm. This helps scaffold students’ mastery and provides the types of content that help them learn best before moving to the next place in their content map.

Like Culatta (2013), these articles suggest that technology can overcome the challenge of teaching all learners the same by developing customizable environments for every student. Tools like the GLP could allow students to set their own schedules. Students progress to the next piece of content when they are ready and their performance data helps them see when they have reached mastery. The MLE study also supports the idea of technology promoting student agency. The students in the study exhibited agency in deciding what artifacts to create and what to study, thanks to the resources on their devices and in the MLE. They became creators of content, designing animations and presentations that demonstrated their knowledge and added to the class database about life cycles.

I am hopeful that these tools will allow students to engage in constructivist learning in new ways (O’Donnell, 2012). What if the GLP platform also included experiential learning, whether through field trips or authentic, meaningful maker tasks? A nugget could consist of students learning by doing (trial and error) with scaffolding through tutorial videos or experts available by Skype. Then, students could upload artifacts they create to the GLP to be assessed and added to their learner/maker portfolio. Maybe some of their work could require them to solve problems in their community and engage in teamwork (O’Donnell, 2012). Technology would be the tool that helps them engage in and capture that work. How well their solution solves the problem could be part of the measurement of whether students reach mastery. As a learner, I would much prefer to engage in learning that is personalized to my current levels of mastery, relevant to my life, authentic, and supports me in constructing deeper understandings through hands-on creation and fieldwork.

References

Culatta, R. (2013). Reimagining learning: Richard Culatta at TEDxBeaconStreet [Video file]. Retrieved from https://youtu.be/Z0uAuonMXrg 

O’Donnell, A. (2012). Constructivism. In APA Educational Psychology Handbook: Vol. 1. Theories, Constructs, and Critical Issues. K. R. Harris, S. Graham, and T. Urdan (Editors-in-Chief). Washgington, DC: American Psychological Association. DOI: 10.1037/13273-003.

Shaw, C., Larson, R., & Sibdari, S. (2014). An asynchronous, personalized learning platform-guided learning pathways (GLP). Creative Education, 5(13), 1189-1204. Retrieved from http://ezproxy.msu.edu.proxy1.cl.msu.edu/login?url=http://search.proquest.com.proxy1.cl.msu.edu/docview/1553761060?accountid=12598

Song, Y., Wong, L., & Looi, C. (2012). Fostering personalized learning in science inquiry supported by mobile technologies. Educational Technology, Research and Development, 60(4), 679-701. doi: http://dx.doi.org.proxy1.cl.msu.edu/10.1007/s11423-012-9245-6

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