Personalized Learning (Theories)

This week in CEP 811 I focused on Learning Theories and how I can “use technology to do entirely new things that simply were not possible before” (Culatta, 2013). More specifically, I focused on personalized learning and how it can be supportive of both student learning and Maker Education. I chose to focus my research on personalized learning because my Interactive Cell Model prototype from last week provides students with choices as they design and build their model. Scaffolding can also be varied depending on each student’s needs.

Edify iLMS (http://www.kickstandsystems.com/)

In addition, personalized learning has been a major interest of mine for the past year and a half. After visiting several Detroit schools in the spring of 2014, I have been focusing on how I can create a learning environment that benefits everyone in my class – not just the middle 70%. Part of this focus has included using the individualized learning management system called Edify to generate performance data.

After watching Richard Culatta’s TedTalk on Reimagining Learning and after brushing up on learning theories by reviewing Bransford, Brown & Cocking’s (2000) book “How People Learn” and Angela O’Donnell’s (2012) chapter on Constructivism, I was tasked with using MSU’s electronic library to find articles relating to personalized learning. While I ended up reading several articles, I will summarize the key ideas from two of them and show how all of the resources this week relate to Maker Education and using technology to improve student understanding through personalized learning.

The Research:

In their 2009 article, Graf, Liu, Kinshuk, Chen, and Yang focused on how learning styles and cognitive traits can help to build adaptive web-based educational systems. Graf et al. argue that if both these areas can be monitored, more student data will be generated and the feedback will be much more useful. According to Graf et al., “Different learners have different knowledge about the domain, aim at different goals, have different learning styles, and also have different cognitive abilities. In traditional education, teaching in a way that the needs of all students are met is difficult, especially in classes with a high number of students” (n. pag.). Technology can be used to create a personalized learning environment by automatically capturing data based on “the actions of the learners when they are using the learning system” (n. pag.). The more information that can be collected about a learner, the more reliable the information is and the more personalized instruction will become.

Sahabudin and Ali (2013) conducted a study to identify learning styles based on Kolb’s learning style model and then to determine the desired format of learning materials for each of these styles. Sahabudin and Ali say that some “advantages of personalized learning is to optimize students’ learning process as well as help students acquire knowledge more efficiently and effectively” (p. 711). They argue that in order for teachers to meet the needs of all their students, they must understand the specific learning styles of each student (p. 712). Sahabudin and Ali agree with Graf et al. (2009) when they state that “this is difficult to do if the process of learning occurs in the traditional manner (face to face) in the classroom” (p. 712). The remainder of their paper discusses their study on how they identified the learning style of 39 students and then linked the learning style to preferred learning materials. Interestingly, their conclusions differed from a similar study by Yang and Wu in 2009.

Tying it Together:

“Schools and classrooms must be learner centered” (Bransford, Brown & Cocking, 2000, p.23). This means that teachers must strive to know their students. How does Fred learn best? What background knowledge does Susan have regarding cell structure? How much support does Greg need as he begins a project? Graf et al. (2009) state that “a requirement for providing adaptivity in web-based educational systems is to know the characteristics of learners” (n. pag.). In addition, learner centered means that the teacher’s role is to guide students as they learn. According to O’Donnell (2012) “constructivism, in particular, is an approach to understanding learning that begins with the notion that meaning is constructed by the learner” (p. 61). Therefore, the teacher must help to support and scaffold as the learner engages in “authentic tasks and experiences” (p. 67). What might be authentic and interesting to one student might feel forced and irrelevant to another student. Culatta (2013) states that technology can help to provide a personalized learning environment that allows students to move at their own pace, have choice in the resources they pursue, and become creators. In addition algorithms can be used to collect and analyze thousands of data points on each student. As more information is collected, a more personalized plan can be generated for each student. This is a long cry from the one size fits all mentality of traditional education. By creating a personalized learning environment, we allow “students to interact with the learning material that suits with their needs” (Sahabudin & Ali, 2013, p. 711).

So What Now?

Personalized Learning might be a great idea but how do we start? As teachers, we need to begin by paying closer attention to our students and how they learn. In addition, many web-based systems are beginning to use algorithms to provide automated feedback. As more and more data is collected and analyzed, these algorithms will become much better at alerting teachers to the needs of their students. In addition, many aspects of Maker Education can be seen throughout this week’s resources. Maker Education Initiative states that one of their main values is that they “believe that the diversity inherent in maker education approaches and applications is crucial in creating inclusive learning environments that enable multiple entry points to making.” Designing and creating produces a constructivist personalized learning environment as students “integrate new knowledge with prior knowledge and actively interact with their environment” (O’Donnell, 2012, p. 65). So after all of this, Culatta (2013) asks “does technology make a difference for learning?” The answer of course is “YES!” but only if the technology is used intentionally.

References:

Bransford, J.D., Brown, A.L., & Cocking, R.R. (2000). How people learn: Brain, mind, experience and school. National Academies Press. Retrieved from http://www.nap.edu/openbook.php?isbn=0309070368.

Culatta, R. (2013, January). Reimagining Learning: Richard Culatta at TEDxBeaconStreet [Video File]. Retrieved from https://www.youtube.com/watch?v=Z0uAuonMXrg

Graf, S., Liu, T. C., Kinshuk, Chen, N. S., & Yang, S. J. H. (2009). Learning styles and cognitive traits – their relationship and its benefits in web-based educational systems. Computers in Human Behavior, 25, 1280-1289.

Maker Education Initiative. (2015). Who We Are. Retrieved from http://makered.org/about-us/who-we-are/

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.

Sahabudin, N.A., & Ali, M.B. (2013). Personalized learning and learning style among upper secondary school students. Procedia – Social and Behavioral Sciences, 103, 710-716.