I’ve noticed something pretty interesting in ed tech. I’m wondering if you’ve noticed it too. There are people out there in ed tech who think it’s meaningful to recommend a product according to whether it is liked by their own kids or students. And I think that’s so….well…strange.
Don’t get me wrong. I think that learning can definitely be fun and that we, as instructional designers, educators and teachers, can do an awful lot to create materials that kids are likely to enjoy. I just think it’s meaningless for a product reviewer to tell me that my kids are going to like a product just because their kids (or students) did. Here’s why:
First, kids are idiosyncratic. We all are. We like different things. Books I enjoy reading, you might not. Topics that are interesting for you might be boring for me. That part, I think, is fairly obvious.
But here’s the more important part. There is plenty of research that shows that there is a relation between how much learners report enjoying educational experiences and how successful they are during those experiences (see Bandura, 1986; Gottfried, 1985; Harrison et al., 1987). So learners report being more motivated and happy to do academic work when they are attaining achievement with that work and feel competent. And this isn’t just for academic tasks…the same holds true for leisure and activities (Scanlan & Lewthwaite, 1986).
So what it means for ed tech products is this: the entry skill level of the learner matters.
And I don’t think I have to tell you that kids have differing skill levels.
But don’t worry. Because even though I’m telling you that some other kids liking educational apps doesn’t really mean anything about whether or not your kids will like those apps, I have a better solution for you. That’s right, a much better predictor of whether or not your kids will like an educational app. And that predictor is: the app’s instructional design.
Instructional designers have known about how to create programs that build motivation and preference for a long time (see Keller, 1983). So look for apps and products with good learner feedback built in (e.g., Azevedo & Bernard, 1995) and adaptive instruction (e.g., Tsai et al., 2013). In other words, look for instructional design that adjusts to the level of your particular learner…allowing your child or student to achieve with the skills they bring to the table. Apps that provide the infrastructure to support achievement are the ones that your particular learner is most likely to enjoy. Because we all prefer to do things that make us feel confident and competent.
Reports of other kids liking apps and wanting to use them simply isn’t enough.
Azevedo, R. & Bernardo, R.M. (1995). A meta-analysis of the effects of feedback in computer-based instruction. Journal of Educational Computing Research, 13(2), 111-127.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice- Hall, Inc.
Gottfried, A.E. (1985). Academic instrinsic motivation in elementary and junior high school students. Journal of Educational Psychology, 77(6), 631-645.
Harrison, A.W., Rainer, R.K., Hochwarter, W.A. & Thompson, K.R. (1997). Testing the Self-efficacy performance linkage of social-cognitive theory. The Journal of Social Psychology, 137(1), 79-87)
Keller, J. M. (1983). Motivational design of instruction. Instructional design theories and models: An overview of their current status, 1, 383-434.
Scanlan, T. K., & Lewthwaite, R. (1986). Social psychological aspects of competition for male youth sport participants: IV. Predictors of enjoyment.Journal of sport psychology, 8(1), 25-35.
Tsai, F., Kinzer, C., Hung, K., Chen, C., & Hsu, I. (2013). The importance and use of targeted content knowledge with scaffolding aid in educational simulation games. Interactive Learning Environments, 21(2), 116-128
- My 100th Post: Balefire Labs and #EdApps (karenmahon.com)