The gamification of education can enhance levels of students' engagement similar to what games can do, to improve their particular skills and optimize their learning. On the other hand, scientific studies have shown adverse outcomes based on the user's preferences. The link among the user's characteristics, executed actions, and the game elements is still an open question. Aiming to find some insights for this issue, we have investigated the effects of gamification on students' learning, behavior, and engagement based on their personality traits in a web-based programming learning environment. We have conducted an experiment for four months with 40 undergraduate students of first-year courses on programming. Students were randomly assigned to one of the two versions of the programming learning environment: a gamified version composed of ranking, points, and badges and the original non-gamified version. We have found evidence that gamification affected users in distinct ways based on their personality traits. Our results indicate that the effect of gamification depends on the specific characteristics of users. First part title: Studying the impact of gamification on learning and engagement based on the personality traits of students

Efficient distributed numerical word representation models (word embeddings) combined with modern machine learning algorithms have recently yielded considerable improvement on automatic document classification tasks. However, the effectiveness of such techniques has not been assessed for the hierarchical text classification (HTC) yet. This study investigates application of those models and algorithms on this specific problem by means of experimentation and analysis. We trained classification models with prominent machine learning algorithm implementations-fastText, XGBoost, SVM, and Keras' CNN-and noticeable word embeddings generation methods-GloVe, word2vec, and fastText-with publicly available data and evaluated them with measures specifically appropriate for the hierarchical context. FastText achieved an lcaF 1 of 0.893 on a single-labeled version of the RCV1 dataset. An analysis indicates that using word embeddings and its flavors is a very promising approach for HTC.

The study demonstrated that a home exercise programme had positive effects in adult patients with CF, including gain in muscle strength in UULL. No increase in tolerance to exercise was shown and improvement in the quality of life of the patients who received intervention.

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