Evaluation of a Didactic Method for the Active Learning of Greedy Algorithms

Abstract

An evaluation of the educational effectiveness of a didactic method for the active learning of greedy algorithms is presented. The didactic method sets students structured-inquiry challenges to be addressed with a specific experimental method, supported by the interactive system GreedEx. This didactic method has been refined over several years of use. Additional elements are lecture contents and the scheduling of classes and lab sessions. Learning gain in the topic of greedy algorithms was measured in the short term for two groups of students: an experimental group taught with the new didactic method, and a control group taught with a traditional approach. The results show a significant learning gain improvement in the experimental group, while the students taught with a traditional method had little learning gain. In addition, the levels of Bloom's taxonomy at which improvements occurred were identified. In the control group, improvements were found at the knowledge level. In the experimental group, however, improvements were found at the knowledge and the comprehension levels, although not at the analysis level. These results are encouraging and indicate directions for future research, as analysis skills are important in algorithm courses.

A pedagogical case on active learning regarding to Kirchhoff's circuit laws

“…Figure 4(a) shows that the stimulated current is injected at node (3,3) with resistors of R x1 ¼ R x2 ¼ 4:7 X and R y1 ¼ R y2 ¼ 4:7 X. In Figure 4(b), there are two stimulated points at (2,2) and (4,4) with the same resistors as in Figure 4(a). But in Figure 4(c), there is still one stimulated node at (3,3) with different resistors of…”

“…Recently, more and more teaching cases have proved that students will benefit more from an active learning method. 4,5 It can, to a certain extent, increase their interests and motivation for learning, enhance their ability to solve problems, deepen their understanding of knowledge, and improve their overall learning effect. Based on the idea of active learning, this paper designs a teaching paradigm based on multi-channel resistance network to learn and understand Kirchhoff's law.…”

GreedExCol, A CSCL tool for experimenting with greedy algorithms

“…It has also been evaluated for educational effectiveness, with statistically significant positive results [9,10]. Finally, it has been used as a tool to identify and correct students' misconceptions on optimization problems [11].…”

“…We developed several years ago the GreedEx system to support the learning of greedy algorithms by means of interactive experimentation [9]. GreedEx was successful in fostering more active learning [10], but we noticed that students could benefit even more if individual experimentation was followed by collaborative discussion of proposals within groups of students. As a consequence, we developed an extension of GreedEx, called GreedExCol that supports such collaborative discussions.…”

“…GreedEx has constantly been evaluated for usability in order to remove deficiencies and incorporate new functionalities [9]. It has also been evaluated for educational effectiveness, with statistically significant positive results [9,10]. Finally, it has been used as a tool to identify and correct students' misconceptions on optimization problems [11].…”