Ricerc@Sapienza

One standing problem in the area of web-based e-learning is how to support instructional
designers to effectively and efficiently retrieve learning materials, appropriate for their
educational purposes. Learning materials can be retrieved from structured repositories,
such as repositories of Learning Objects and Massive Open Online Courses; they could also
come from unstructured sources, such as web hypertext pages. Platforms for distance education
often implement algorithms for recommending specific educational resources and
personalized learning paths to students. But choosing and sequencing the adequate learning
materials to build adaptive courses may reveal to be quite a challenging task.
In particular, establishing the prerequisite relationships among learning objects, in terms
of prior requirements needed to understand and complete before making use of the subsequent
contents, is a crucial step for faculty, instructional designers or automated systems
whose goal is to adapt existing learning objects to delivery in new distance courses.
Nevertheless, this information is often missing. In this paper, an innovative machine
learning-based approach for the identification of prerequisites between text-based
resources is proposed. A feature selection methodology allows us to consider the attributes
that are most relevant to the predictive modeling problem. These features are extracted
from both the input material and weak-taxonomies available on the web. Input data
undergoes a Natural language process that makes finding patterns of interest more easy
for the applied automated analysis. Finally, the prerequisite identification is cast to a binary
statistical classification task. The accuracy of the approach is validated by means of experimental
evaluations on real online coursers covering different subjects.