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This week, I found myself challenged to create a new Instructional Design (ID) Model for addressing online course upgrades. After struggling with the task and coming up with more content for the trash than the plate, and in keeping with last week's cooking analogy, I decided to compare my exploits this week with baking.
Just like ID, baking is one of those things I love to do. It involves a lot of creativity, but requires a solid foundation. You won't get anything close to cake if you don't have the right ingredients in the correct ratios. It's an art and a science.
ID is the same. It requires a great deal of creativity to come up with engaging activities. But if it isn't grounded on a strong foundation of sound learning strategies and analyses, the result isn't guaranteed to be a learning experience. So, let's consider what it takes to bake a great ID model.
Just like baking a show-stopping cake, the first step in creating an ID model is gathering the ingredients. When considering how to upgrade an existing online course, the ingredients are data. Online courses produce a lot of data. There are course gradebooks that provide student performance data. There are pre- and post-course surveys that provide qualitative feedback of student and instructor experiences. And there are course data reports that provide student engagement metrics.
In baking, once you've got your ingredients, it’s time to mix the batter. In the ID process, this is where we mix all the data in measured amounts to create a story of what is and isn't working in the course. This step is where your analytical skills come into play. Mixing data can be as tricky as getting your cake batter smooth without overmixing. You're looking for patterns, trends, and insights, while simultaneously trying not to read into any of it. The data only provides part of the story, so we must be careful to keep our assumptions in check.
For example, students might provide feedback about a major course assignment, such as "the final test was too hard." If we stop there, we might consider simplifying some of the questions. But if we look further at the questions most missed and find they are all from the same learning module, we might consider adding more training content or practice activities for that module. If we keep digging and discover the average time spent on the existing training materials is 95% less than the expected time to complete, we might consider encouraging students to spend more time on the activity. We might add incentives, like gamification. Or we might check if the activity is advanced enough to enforce the objectives we're trying to teach. In other words, the data can be combined in countless ways. And just like adding more flour won't make all batters better, some data interpretations will not add benefit to the course.
Once we've examined our data batter and combined the right pieces in the right amounts, we have to put it in a pan. In this metaphor, the pan is structure for our learning experience. The structure is determined by the needs of the audience and the goals of the course. Just like you wouldn't bake a wedding cake in a pie plate, it wouldn't make sense to use the same model for a doctoral dissertation course on a first-year introductory course. The audiences for both courses have different needs, skills, and experiences.
Once the course data is poured into the right mold, it's time for the oven. This is where you implement your course. If you misinterpret your data and added too little content, your strategy is undercooked. If you added too much, you risk burning out your learners. This stage requires careful monitoring, just like peeking through the oven door, making sure everything is rising as expected.
Finally, the most nerve-wracking yet satisfying part comes: tasting. In ID, this translates to evaluating the effectiveness of the course. Were your learners engaged? Did they retain information? It’s like biting into that first slice of cake. Is it moist? Flavorful? Do you go back for a second slice, or does it need some tweaking? Just like a great baker, a great ID continues to edit their recipe until they've mastered the perfect blend of ingredients and process to produce an exceptional dessert.
The first try probably won't be perfect. Neither might the second, nor the tenth. Maybe your course needs more interactivity, less passive reading assignments. Maybe it needs more authentic assessments over smaller chunks of information. Maybe the questions on the final are too hard. Maybe they're too easy.
Creating a data-driven ID model is a lot like baking – it requires the right ingredients in the form of data, careful mixing through analysis, the perfect pan to form the overall structure, precise baking through the implementation strategies, and of course, the taste test where success is finally measured through evaluation. And just like baking, it's a process filled with trial, error, and hopefully, a lot of fun and delicious experiences.
All images contained within this post are courtesy of Media from Wix, unless otherwise noted.
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