Abstract:

In order to improve the quality of education, it is fundamental to focus the efforts of teaching teams on the aspects of each subject that are more problematic for students.
This requires identifying these elements of greater difficulty based on data, since the perception of the teachers may differ from that of the students. This analysis is particularly relevant in the case of distance education, where teachers have a more indirect perception of the difficulties that students face.

Sometimes the difficulty may be due to previous knowledge required, particular skills, etc. Our hypothesis is that, based on the data collected in previous courses, we can perform analysis that indicate the difficulty of the different topics included in the Computer Science subject considered. This subject is taught in the second year of a Computer Science degree in the National University of Distance Education of Spain (UNED). This subject has an advanced level and requires prior knowledge of mathematics and programming. Specifically, it refers to advanced data structures of computer science, and to algorithmic schemes, which represent general principles to address a problem with.

Among the data structures studied are hash tables, structures that associates keys with values, supporting searches efficiently. Graphs are also studied, which allow to represent the data and their connections for many problems, and heaps, which are used to represent priority queues in which an element with higher priority is extracted before one of lower priority. If two elements have the same priority, they are extracted following the usual order of waiting queues.

Among the schemes studied are the greedy one, which is applicable to problems in which there is a criterion that allows to build the solutions directly, without having to undo decisions already taken. However, this approach is not applicable for many problems. Another scheme studied in the subject is divide and conquer, in which the problems are divided in other simpler ones, and the backtracking scheme in which all potential solutions are explored, while it is not proven that they can not be a valid solution.

For teaching each topic the general case is presented and exemplified in a particular problem. Then, other classic problems of application of the structure or the scheme are shown.

The first question studied has been whether the differences in the results of the students over the years for the different topics are stable, or if they are punctual and disappear when the data are aggregated over the years. Once this aspect has been verified, we have analyzed which topics of the subject tend to be more difficult and we have tried to identify the characteristics of those topics based on the data.

Our hypothesis is also that improving materials by the teaching team in these hard aspects will optimize the results obtained by the students. We also believe that as a result of this process, the students will be able to select the most relevant materials for them, allowing a personalized learning plan. Finally, we consider that it is possible to define a methodology based on the analysis of the data that is exportable to other subjects and areas, thus advancing in the improvement of the quality of
teaching in general.

Keywords:

Topic difficulty, computer science, data structures and algorithms, data analysis, distance learning.