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Chronic neurological disorders in their advanced phase are characterized by a progressive loss of mobility (use of upper and lower limbs), speech and social life. Some of these pathologies, such as amyotrophic lateral sclerosis and multiple sclerosis, are paradigmatic of these deficits. High technology communication instruments, such as eye tracking, can be an extremely important possibility to reintroduce these patients in their family and social life, in particular when they suffer severe disability. This paper reports and describes the results of an ongoing experimentation about Eye Tracking impact on the quality of life of amyotrophic lateral sclerosis patients. The aim of the experimentation is to evaluate if and when eye tracking technologies have a positive impact on patients' lives.

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The Learning Analytics community has recently paid particular attention to early predict learners’ performance. An established approach entails training classification models from past learner-related data in order to predict the exam success rate of a student well before the end of the course. Early predictions allow teachers to put in place targeted actions, e.g., supporting at-risk students to avoid exam failures or course dropouts. Although several machine learning and data mining solutions have been proposed to learn accurate predictors from past data, the interpretability and explainability of the best performing models is often limited. Therefore, in most cases, the reasons behind classifiers’ decisions remain unclear. This paper proposes an Explainable Learning Analytics solution to analyze learner-generated data acquired by our technical university, which relies on a blended learning model. It adopts classification techniques to early predict the success rate of about 5000 students who were enrolled in the first year courses of our university. It proposes to apply associative classifiers at different time points and to explore the characteristics of the models that led to assign pass or fail success rates. Thanks to their inherent interpretability, associative models can be manually explored by domain experts with the twofold aim at validating classifier outcomes through local rule-based explanations and identifying at-risk/successful student profiles by interpreting the global rule-based model. The results of an in-depth empirical evaluation demonstrate that associative models (i) perform as good as the best performing classification models, and (ii) give relevant insights into the per-student success rate assignments.

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