Estimating the difficulty of a learning activity from the training cost for a machine learning algorithm

Estimating the difficulty of a learning activity is crucial for smart learning systems to provide learners with most suitable activities for their abilities. Generally, difficulty is estimated by teachers according to their experience and based on students' results. For newly designed activities, this estimation is often inaccurate or complicated to obtain. Moreover, machine learning methods seem impossible to use on absence of data. This work proposes an innovative way to use machine learning in this scenario. We start hypothesizing that the effort a student has to make to perform an activity is correlated with the effort that a machine learning algorithm would require. We define the concept of difficulty in a formal way, implement two Neuroevolution algorithms that solve a specific type of activity, and then calculate and compare efforts for students and implemented algorithms. Results show that the correlation exists for the selected activity. Therefore, the effort of the algorithm can be used to estimate the difficulty of this learning activity.