Treffer: International Conference on Engineering Education – ICEE 2007 Meaningful Learning through Identifying Differences and Similarities between Certain Problems and Algorithms

Some typical optimization problems like knapsack problem, activity selection problem, coin change problem and subset sum problem are taught in different courses of theoretical computer science, or in the different modules of the same course. A few of these problems can be solved in polynomial time but the rest are NP-complete problems. Even though, every problem has its own nature and description; however after changing certain constraints, they become equivalent to each other. These problems are discussed independently in most textbooks which makes it harder for the students to interlink them. Students get confused very easily while transitioning between different problem domains even if one problem is described in the language of another problem. In this paper, we have carefully analyzed and investigated links between the aforementioned problems and have also diagnosed the major causes of failure in understanding. One of the primary reasons students face difficulty is that they have a tendency to rote learn. We suggest that these problems and their algorithms should be taught simultaneously in a generic language using super-ordinate learning, so that they may be able to maintain the currently missing links. We assert that this effort will empower the notion of meaningful learning.