Section: New Results

Teaching Algorithms using Problem and Challenge Based Learning

Participant : Florent Bouchez - Tichadou.

Teaching algorithms is always a challenge at any level of the CS curriculum, as it is often viewed as a theoretical field. While many exercises revolve around classical examples that illustrate interesting algorithmic points, they are often disconnected from reality, which is a major drawback for students trying to learn. During the last four years, we have been trying to reconnect the teaching of algorithms with their applicability in the real world to M1 and L2 students, by giving them actual problems that could arise in their life of future software engineers, challenging enough to force them to use particular algorithmic techniques or data structures—e.g., linked lists, binary trees, dynamic programming or approximation algorithms.

By assigning students in groups of 5 to 6 members, we wanted to create an environment where they function as a team trying to work together to solve a problem. This allowed them to help each other in their respective comprehension, and made them more autonomous in their learning. The effective materials was provided as online pdf files so they had to read and learn from them by themselves, while the class sessions with a tutor (teacher) where used for the problem-solving part, with guidance from the tutor (who is there to make sure the learning takes place).

After four years of experimentation with M1 students, we found that the student's grades were stable, in particular there was no decrease in exams' performances compared to the classical course that was taught in the previous years. However, the students progressed in trans-disciplinary skills such a communication and the writing of essays. More importantly, students show a strong adhesion to the teaching method, 50% of them rating it as “excellent” (6) and 25% as “good” (resp. 6 and 5 on a scale from 1 (terrible) to 6 (excellent)). No student rated the course below average.

This work is still ongoing, and our plan now is to use our knowledge of the internals of compilers and run-time systems to: First, extract real-life algorithmic problems that have concrete applications; Second, create a tool that exposes the working mechanics of a running program, hence helping students to better understand how algorithms work.