Integrating social media into academia is not a novel idea. And since you are reading this, chances are you probably have been utilizing some feature of social media in the classroom for years. What is more interesting is asking why academia should exploit social media and, more specifically, Twitter.
Some learners are effective at self-regulation; that is, they guide their learning through metacognition – thinking about their own learning – and through strategic action and evaluation of their own progress. Research has shown that such learners will outperform inadequate self-regulators in nearly every aspect of learning. So, as educators, our goal is to infuse our learners with knowledge, but do so in a way that also improves their self-regulatory practices for future learning.
Increasing metacognitive awareness (knowing what you know, and what you don’t know) is critical for better self-regulation. Researchers have also pointed out that most learners are deficient in performing basic metacognitive skills and will not actively pursue metacognitive activities on their own. For examples of this research, see the References listed at the end of this article by Claire E. Weinstein, Jenefer Husman, and Douglas R. Dierking, and by Philip Winne. Twitter is a convenient platform to provide this type of metacognitive support.
What is a metacognitive support device?
In a paper written in 2000, Ward Cates delineates two types of metacognitive support: static/directive support and dynamic/interactive support. As you can imagine, Twitter is not static. Rather, it is an interactive tool under learner control. Further, Maria Bannert refers to a metacognitive support device that “focuses students’ attention on their own thoughts and on understanding the activities they are engaged in during the course of learning.” When used explicitly as a tool to improve self-regulation through metacognitive support, Twitter becomes very effective.
Four types of tweets
Over the course of the 2012 spring semester, I collected and analyzed 547 tweets from my English students. Using Paul Pintrich’s framework for the foci of self-regulation, I then categorized each tweet into one of the four areas. The most commonly generated tweet was behavioral (38 percent), followed by context and motivation (17 percent each) and cognition (10 percent). About 19 percent of all tweets were just plain irrelevant. (Figure 1)
Figure 1: Analysis and examples of tweets collected from students
What does this mean?
Students mainly used the class Twitter list to remind each other of due dates, seek help and feedback, and vent their frustrations. From an instructor’s perspective, Twitter offers the ability to prompt students throughout the learning process by asking them to reflect on learning strategies and time management, which ultimately raises metacognitive awareness.
While the use of Twitter only shows a slight increase in grades (about .5 percent), it substantially improves engagement and motivation, as Reynol Junco and his co-authors, and as I and my colleagues Denise Houchen-Clagett and J. Burton Browning, have found. More, Twitter enhances the social presence outside of the classroom, which leads to overall course satisfaction.
So is Twitter the magic bullet for improving grades? No. But, if student satisfaction, engagement, and metacognitive awareness are all part of your definition of a successful course, then introducing Twitter in your classroom may be an option for you.
(Editor’s Note: Readers who are eLearning Guild Members, Members Plus, or Premium Members may also wish to refer to the Guild Research Perspectives Report Smart Companies Support Informal Learning, published August 16, 2012.)
Bannert, Maria, M. Hildebrand, and C. Mengelkamp. (2009). Effects of a metacognitive support device in learning environments. Computers in Human Behavior, 25(4), 829-835. doi:10.1016/j.chb.2008.07.002
Cates, W. (2000). Supporting and evaluating metacognition in hypermedia/multimedia learning products. Retrieved from http://www.lehigh.edu/~wmc0/MetacognitionInHypermedia
Junco, R., G. Heiberger, and E. Loken. (2011). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27(2), 119-132. doi: 10.1111/j.1365-2729.2010.00387.x
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, and M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). San Diego, CA: Academic Press.
Reid, A., D. Houchen-Clagett, and J.B. Browning. (2012). Twitter: Integration into developmental English and technology. In Cheal, C., Coughlin, J., & Moore, S. (Eds.), Transformation in Teaching: Social Media Strategies in Higher Education (391-412). Santa Rosa, CA: Informing Science Institute.
Weinstein, C.E., J. Husman, and D.R. Dierking. (2000). Self-regulation interventions with a focus on learning strategies. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 727-747). San Diego, CA: Academic Press. doi: 10.1016/B978-012109890-2/50051-2
Winne, P. (2005). A perspective on state-of-the-art research on self-regulated learning. Instructional Science, 33, 559-565. doi: 10.1007/s11251-005-1280-9