A Comprehensive Methodology to Analyse Topic Difficulties in Educational Programmes

We propose a comprehensive Learning Analytics methodology to investigate the level of understanding students achieve in the learning process. The goals of such methodology are

1) To identify topics in which students experience difficulties on;

2) To assess whether these difficulties are recurrent along semesters;

3) To decide if there are conceptual associations between topics in which students experience

difficulties on;

and, more generally,

4) To discover statistically significant groups of topics in which students show similar performance.

The proposed methodology uses statistics and data visualization techniques to address the first and the second goals, frequent itemset mining to tackle the third goal, and biclustering is proposed to find relationships within educational data, revealing meaningful and statistically significant patterns of students’ performance.

We illustrate the application of the methodology to a Computer Science course.