Richard Culatta, Director of the Office of Educational Technology for the US Department of Education, believes there is a growing digital divide in education. The digital divide that concerns Culatta is between “those who know how to use technology to reimagine learning and those who simply use technology to digitize traditional learning practices” (Culatta 2013). In his TED talk, Reimagining Learning, Culatta puts forward personalized learning as a framework for harnessing the potential digital technology can bring to education.
In CEP 811 we have been exploring the growing maker movement and it’s potential to provide students with opportunities to take ownership of their own learning, develop critical thinking, and grow in their confidence as learners. What possibilities are there in personalized learning and maker education together? Are these two approaches compatible with one another or do the differences render both approaches impractical to combine?
To answer these questions consider The Role of Affective and Motivational Factors in Designing Personalized Learning Environments by ChanMin Kim and Personal Competencies in Personalized Learning by Sam Redding. Kim’s research presents guidelines for the design of “virtual change agents” which address affective and motivational factors in students in order to promote personalized learning and provide meaningful feedback in online remedial mathematics courses” (2012). In Personal Competencies in Personalized Learning, Redding proposes a Personal Competency Framework to support student growth in a personal learning environment. Redding lists personal competencies as cognitive competency, metacognitive competency, motivational competency, and social/emotional competency (2014). Both papers dovetail with Culatta’s vision of personalized learning because they compliment the real time feedback, adjusted pacing, and agency his personalized learning is built upon. How specifically does Kim and Redding’s work dovetail with Culatta’s description of personalized learning?
Kim and Redding address similar dimensions of personalized education by focusing on the personal and emotional aspects of student learning. Kim’s work specifically seeks to address negative emotional responses that are a result of students experiencing academic difficulty. Kim notes that research demonstrates that negative emotions related to academic performance can have a deep impact on future outcomes for students. Thus the central theme in Kim’s work is to provide guidelines for “individual and personalized support for positive emotional experiences in designing instruction” within the context of personalized learning (2012). Redding observes that, “psychological characteristics of individual students and their immediate psychological environments most directly influence educational outcomes” which parallels Kim’s addressing issues related to the impact that negative academic emotions can have on student growth (2014). So how does Culatta’s personalized learning dovetail neatly with Kim’s addressing affective and motivational factors and Redding’s personal competencies?
Culatta touts the potential of real time feedback. Real time feedback can empower students to more closely monitor their own learning. By monitoring their own learning, students have a greater chance of experiencing positive academic emotions and by monitoring their own learning every step of the way bolster bolster metacognition. Personalized learning adjusts to a student’s pace and level of progress and as a consequence can result in positive academic emotions and experiencing increased motivational, and social/emotional competency. Finally, Cullata describes personalized learning as providing students with agency because in personalized learning, students have choice in what tasks they will perform to gain mastery of content and skills. All of these factors lead to student empowerment, which in turn fosters motivation for further learning.
Is personalized learning and maker education compatible? It can be argued that maker education is a form of personalized learning. Maker education results in many of the same outcomes identified by Kim and Redding: positive academic emotions, metacognitive competency, and motivational/emotional competency. Additionally, maker education is embedded with the same dynamics as Culatta’s personalized learning: real time feedback, adjusted pace, and agency.
References
Kim, C. (2012). The Role of Affective and Motivational Factors in Designing Personalized Learning Environments. Educational Technology Research and Development, 60(4), 563–584.
Redding, S. (2014). Personal Competencies in Personalized Learning. Center on Innovations in Learning, Temple University. Retrieved from http://eric.ed.gov.proxy2.cl.msu.edu/?id=ED558063
“Reimagining Learning: Richard Culatta at TEDxBeaconStreet” Video at TEDxTalks. (n.d.). Retrieved July 24, 2016, from http://tedxtalks.ted.com/video.mason/Reimagining-Learning-Richard-Cu?
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