I love online learning. I love it so much that I decided to get an online degree in it. Working in a physical brick-and-mortar school is a pleasure, for sure, but I’ve long been interested in bringing the best of online learning into the face-to-face classroom. This is not because I think these tools are cool, or because I wish to replace human interaction with a dystopian educational system in which every child receives personalized training from a computer. It’s because I think that there are facets of online learning that can enhance proven instructional methods.
As an example, let’s take the ubiquitous face-to-face practice of “raise your hand and share.” We know that asking a lot of questions and eliciting the responses of all students is an effective teaching method due to years of research on the best teachers (Rosenshine, 2012). But by asking questions in the default face-to-face way, teachers are unlikely to get to every student, and, unless you use popsicle sticks or some other randomizer, teachers’ calling methods will probably always be biased. Teachers naturally wish for confirmation that their lessons are going well, so we tend to call on students that always raise their hands, the very students who probably already know the content before the lesson is taught. By using online tools, such as those mentioned in this older post, teachers can enhance how they might typically ask questions by posting a discussion question that requires every student to answer at once, with the added (and significant) advantage that teachers now have access to a mine of assessment information and can provide detailed feedback outside of student contact hours.
A few weeks ago, millions of students in areas affected by the Coronavirus outbreak were forced to make the shift towards online learning. As a teacher at an international school in China, this has meant that we have had to rapidly expand our capacity to improve learning outcomes from a distance. I’ve learned a lot from the experience, but if I had to whittle it down to just one take-away, it is that I am now much more certain that every teacher is capable of teaching with online tools.
Knowledge, motivation, attitude, or philosophy?
I used to think that knowledge held the answer to why teachers did not move to adopt the online tools that I found to be quite effective in my teaching. I assumed that if all non-adopting teachers received training on how to use online tools, they would know more about the tools, and then go and use them. Diffusion of innovations theory supports this assumption by identifying knowledge as the first stage in the adoption process (Pashaeypoor et al., 2017; Rogers, 2003; Sahin, 2006). Indeed, the user’s perception that technology is easy to use and might be useful – aka knowledge – predicts higher levels of adoption (Scherer et al., 2019; Viswanath & Davis, 2000).
What I’ve seen from teachers during this online learning period, however, has challenged this knowledge-first assumption. Due to the sudden nature of the Coronavirus, schools have not had the time to provide robust LMS or online tools training that would fill all of the necessary gaps in knowledge. Teachers, parents, and students have just had to roll with it, and roll with it they have! Despite the lack of training around online tools, teachers around the world are teaching with online tools, and, from my viewpoint, using them well. The truth behind why teachers do not adopt technology normally is probably closer to what we all suspected deep-down all along: Teachers will use a technology – and probably use it well – if they have no other choice but to use it. In short, online tool adoption in education is likely more motivational, attitudinal, and philosophical, rather than solely due to (lack of) training.
What, then, can instructional leaders do to ensure that the best teaching methods are enhanced by online tools once we return to face-to-face learning after this Coronavirus clears up? We can first identify what those “best teaching methods” are, and provide ample support for their use from the research base, as well as explanations that are consistent with the science of learning. For example, most schools will likely identify “eliciting performance through spaced and interleaved practice” as part of their instructional design. Once the effective teaching methods are identified, schools should select online tools that fit the methods. To continue with the spaced practice example, we know that certain online tools can enhance practice because humans are terrible at adhering to practice schedules and computers are great at managing them. Once the methods-based online tools are identified, teachers should be required to use them alongside viable non-digital alternatives. And since we now know that teachers are more capable with tech than we assumed, there shouldn’t be any issues moving forward, right?
– Zach Groshell @mrzachg
Pashaeypoor, S., Ashktorab, T., Rassouli, M., & Alavi Majd, H. (2017). Experiences of nursing students of Evidence-Based Practice Education according to Rogers’ Diffusion of Innovation Model: A Directed Content Analysis. Journal of Advances in Medical Education & Professionalism, 5(4), 203–209. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/28979915http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5611430
Rogers, E.M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.
Rosenshine, B. (2012). Principles of Instruction: Research-based strategies that all teachers should know. American Educator, 12–20. https://doi.org/10.1111/j.1467-8535.2005.00507.x
Sahin, I. (2006). Detailed review of Roger’s Diffusion of innovations theory and educational technology. The Turkish Online Journal of Educational Technology, 5(2), 14–23.
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers and Education, 128(0317), 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Viswanath, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1016/0014-4800(87)90073-6