Abstract:

In today’s technology-intensive “instant” world, attention spans are getting lower. Many students are working, and often at multiple jobs. A lot of interests and issues are claiming their attention. This results in a need for creating “bite-sized” modules through which information is conveyed.

This becomes increasingly challenging as students progress up the educational pipeline, since the concepts become more difficult and more interrelated, making it difficult to create smaller modules. Larger modules are one way to go, but increasingly, we find that fewer students are capable of coping with these, and the “instant” world does not assist students in this adaptation. At the same time, in a field like computing, the rapidly changing technologies require that practitioners have a more abstract understanding of concepts in order take the changes in stride.

We need modules that students can complete in a short time, but also need to ensure that “larger learning goals” are also met i.e., students don’t miss the wood for the trees.

The challenges associated with this are:
1. Identify the larger learning goals appropriate for the course. (the notion of threshold concepts has been put forward, as have been the best practices. Not everything difficult is a threshold concept and no clearly defined process to recognize them.)
2. Identify a sequence of sub-goals with associated activities/assignments that lead to attainment of larger learning goal. Should be appropriate to level of course, build on and connect with previous items in course, keep students comfortable with their progress, while understanding that they are in “liminal space.”

Two examples:
1. In a lower-level course (CS 1) helping students grasp the concept of program runtime complexity. There is a need to provide many examples, so we started with experimental examples and added analytical approaches and then tied everything together. Reflection at the end. It is relatively easy to find the examples, and tying the examples to the theory is fairly intuitive.
2. In a higher-level course, helping students understand how to choose the best model to apply to design and implement a computer system for a given situation. A challenge here is to find examples that become progressively more difficult.

In this paper, we will describe our experiences with these courses and share some of the results we obtained. We also discuss associated best practices, and ideas that were helpful in the course of our experience.

Keywords:

Algorithm analysis, modeling, computer science, thresholds.