Prideax’s (2003) description of three levels (the planned, the delivered, and the experienced) for examining curriculum is helpful for thinking about what kind of questions get asked about curricula. With a focus on the delivered curriculum, faculty members engage in curriculum mapping to see, over the course of a program, what gets taught, when it gets taught and how it gets assessed. The mapping described below occupies Prideax’s “delivered curriculum” level with faculty members’ experiences in their individual classrooms a key component of analysis.
Two key assumptions here: the data is meant to be examined in aggregate—that is, the unit of assessment is at the program level, not at the level individual courses ((This is largely an act of “bracketing” as individual courses can be seen in the data. The intent is not to use the data to say “Professor X is not doing Y in class Z”.)) ; and that the data does not drive decisions, rather the data drives discussions amongst faculty members which, in turn, drives decision making.
A challenge with curriculum mapping can be the logistics of it — done by hand and following a collaborative model, there are sticky notes to transport and transcribe, not mentioning the additional challenge of getting faculty members together in one room at one time.
At Western University we’re in the midst of developing a web-based curriculum visualization tool that will create a series of curriculum visualizations. In the meantime, we felt we could improve our analog process by taking a small step into the digital world. Enter: Google Sheets.
Before I go into any sort of detail on what we’ve done, I’m just going to go ahead and share an example map with dummy data entered ((And it should be noted that the template is licensed under the Creative Commons under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License)).
The data collected produces two visualizations: the first is a visualization of a progression of learning through the program (what we sometimes call and IRM chart, where I stands for Introduce, R stands for Reinforce and M stands for Master). The approach of asking instructor to weight the complexity of an outcomes was inspired, in part, by Veltri, Webb, Matveev & Zapatero (2011). The second asks instructors whether an outcome is taught and / or assessed in their course (what we’ll often call an T/A chart).
Sandwiched between these two visualizations is an opportunity for faculty to enter the assessment methods and instructional methods used to assess and teach the particular program-level learning outcome in their course.
What’s elegant about this stop-gap is the fact that as instructors enter data, they create the visualization. There is no additional transcription or translation — my colleague, Dr. Beth Hundey, and I set up the Google Sheet to automatically update the colour of the cell, for example, in the IRM chart. At a glance, there’s the opportunity to see how a particular program-level learning outcome progresses through a program curriculum. The data can become “useful” the moment that faculty are done entering data.
Less elegant is the interpretation of the assessment and instruction data. We provide a list of methods linked to numbers, asking instructors to enter the list numbers that match the methods used in the course. There isn’t, however, an easy way to visualize the data entered — it requires extra step(s) of downloading and manipulating the data in a program like Excel. It should be noted that Google has added an “Explore” option that interprets the data entered and creates automatic visualizations of the data. My cursory look at the graphs created doesn’t make me want to suggest that this will be a viable option for creating useful visualizations.
Regardless, as we work with programs undergoing curriculum review, our collaborative sheet allows for the quick collection and interpretation of data. There’s certainly some work that’s required to set the sheet up as well as introducing the task to individual instructors. A curriculum visualization process set up in Google Sheets can work in very specific situations to simplify the task of collecting curriculum data for both faculty members and curriculum developers.
References
Prideaux, D. (2003). ABC Of Learning And Teaching In Medicine: Curriculum Design. British Medical Journal, 326(7383), 268–270. Retrieved from http://www.jstor.org/stable/25453551.
Veltri, N. F., Webb, H. W., Matveev, A. G., & Zapatero, E. G. (2011). Curriculum mapping as a tool for continuous improvement of IS curriculum. Journal of Information Systems Education, 22(1), 31.