Exercise Difficulty Prediction in Online Education Systems In Online Education System (OES), it is popular to assess examinee by providing exercises with different difficulty. So Exercise Difficulty Prediction (EDP) is a fundamental task of OES, such as medicine examination and school assignment. In view of the rich semantic information embedded in exercise which contains texts and images, it is hard to represent exercise and predict its difficulty. In this paper, we propose a novel Bayesian inferencebased Exercise Difficulty Prediction (BEDP) framework to predict difficulty of visualtextual exercise. In BEDP, given exercises with texts, images and difficulty label, we first apply a pretrained Deep Multimodal Embedding model to obtain the unified multimodal representations of each exercise. Then we propose a Bayesian inferencebased Softmax Regression classifier to predict the difficulty of exercise. Variational Inference (VI) is applied for approximately solve probabilistic model with a joint distribution of observations and latent variables. By experimenting on two selfcollected dataset, we demonstrate the effectiveness of our proposed framework on predicting the difficulty of exercise, and conduct convergence diagnostics for Bayesian inference.

On the use of ECG and EMG Signals for Question Difficulty Level Prediction in the Context of Intelligent Tutoring Systems A fundamental drawback of traditional Intelligent Tutoring Systems (ITS) is that, unlike human tutors, they are not able to understand the emotional state of their users and adapt the learning process accordingly. This work explores the potential use of affective computing techniques for providing an affect detection mechanism for ITS. Electrocardiography (ECG) and electromyography (EMG) signals were recorded from 45 individuals that undertook a computerised English language test and provided feedback on the difficulty of the testu0027s questions. Features extracted from the ECG and EMG signals were then used in order to train machine learning models for the task of predicting the selfperceived difficulty level of the questions. The conducted supervised classification experiments provided promising results for the suitability of this approach for enhancing ITS with information relating to the affective state of the learners, reaching an average classification F1score of 75.49% for the personalised singleparticipant models and a classification F1score of 64.10% for the global models.