Abstract-Students' real-time feedback has numerous advantages in education, however, analysing feedback while teaching is both stressful and time consuming. To address this problem, we propose to analyse feedback automatically using sentiment analysis. Sentiment analysis is domain dependent and although it has been applied to the educational domain before, it has not been previously used for real-time feedback. To find the best model for automatic analysis we look at four aspects: preprocessing, features, machine learning techniques and the use of the neutral class. We found that the highest result for the four aspects is Support Vector Machines (SVM) with the highest level of preprocessing, unigrams and no neutral class, which gave a 95 percent accuracy.

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Abstract. Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded.

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Abstract-Students' real-time feedback is acknowledged as an important source of information for teachers/lecturers to improve their teaching and address issues students may have, such as going deeper in some of the materials covered or providing more examples to understand an abstract concept. Previous applications collecting real-time feedback from students through clickers and mobiles typically collect limited information with pre-defined questions, while more recent applications using social media collect such a large volume of information that a lecturer cannot manually process it in real time. We developed the SA-E system for analysing students' real-time feedback provided via social media, and, in this paper, we present the evaluation of this system in real settings with lecturers and students. The results show that lecturers are highly satisfied with the proposed system. In contrast, although the participation of students in providing feedback was high, the students' opinions of the system were between neutral and dislike.