One new usage of Learning Analytics in Intelligent Tutoring Systems (ITS) is sequencing based on performance prediction, which informs sequencers whether a student mastered or not a specific set of skills. Matrix Factorization (MF) performance prediction is particularly appealing because it does not require tagging involved skills in tasks. However, MF’s difficult interpretability does not allow to show the student’s state evolution, i.e. his/her progress over time. In this paper we present a novel progress modeling technique integrating the most famous control theory state modeler, the Kalman Filter, and Matrix Factorization. Our method, the Skill Deficiency aware Kalman State Estimation for Matrix Factorization, (1) updates at each interaction the student’s state outperforming the baseline both in prediction error and in computational requirements allowing faster online interactions; (2) models the individualized progress of the students over time that could be later used to develop novel sequencing policies. Our results are tested on data of a commercial ITS where other state of the art methods were not applicable.

Correct evaluation of Machine Learning based sequencers require large data availability, large scale experiments and consideration of different evaluation measures. Such constraints make the construction of ad-hoc Intelligent Tutoring Systems (ITS) unfeasible and impose early integration in already existing ITS, which possesses a large amount of tasks to be sequenced. However, such systems were not designed to be combined with Machine Learning methods and require several adjustments. As a consequence more than a half of the components based on recommender technology are never evaluated with an online experiment. In this paper we show how we adapted a Matrix Factorization based performance predictor and a score based policy for task sequencing to be integrated in a commercial ITS with over 2000 tasks on 20 topics. We evaluated the experiment under different perspectives in comparison with the ITS sequencer designed by experts over the years. As a result we achieve same post-test results and outperform the current sequencer in the perceived experience questionnaire with almost no curriculum authoring effort. We also showed that the sequencer possess a better user modeling, better adapting to the knowledge acquisition rate of the students.

Currently, a lot of research in the field of intelligent tutoring systems is concerned with recognising student’s emotions and affects. The recognition is done by extracting features from information sources like speech, typing and mouse clicking behaviour or physiological sensors. According to the state-of-the-art support vector machines are the best performing classification models for those kinds of features. However, single classification models often do not deliver the best possible performance. Hence, we propose an approach for further improving the affect recognition performance, which is based on ideas from ensemble approaches and feature selection methods. The approach is proven by experiments on low-level speech features extracted from data which was collected in a study with German students solving mathematical tasks. In these experiments the proposed approach reached on average an affect recognition performance improvement of about 59 % in comparison to a single SVM.

In this paper we show that a performance prediction method can be used to sequence contents ameliorating sequencing over common domain informed strategies. Our developed sequencer is able to sequence content without knowledge on the domain, i.e. without knowing the number of skills involved in a content and the relative difficulties. Moreover, we discuss if a synthetic learning process can be modeled in a plausible way in order to facilitate testing with sequencing, which is generally difficult to evaluate on real students. In conclusion we show that Matrix Factorization is able to deal with all the important actual problems of Intelligent Tutoring Systems: personalization, multiple skill content modeling, and difficulty.

A common problem when trying to apply data mining techniques to improve educational systems is the disconnection between those who have the expertise (e.g. Universities) and those who have access to the data (e.g. Small companies). Bringing expertise into educational in-production systems is complicated because companies are reluctant to invest a lot of effort into integrating new technology that they do not fully trust, while the technology cannot prove its worth without access to real, valid data. In this paper we explore the requirements that machine learning systems have to be applied to specific learning problems (sequencing and performance prediction), and then propose a minimally invasive protocol for sequencing (based on web services) to easily integrate Learning Analytics Services into e-learning systems.

Usually, in intelligent tutoring systems the task sequencing is done by means of expert and domain knowledge. In a former work we presented a new efficient task sequencer without using the expensive expert and domain knowledge. This task sequencer only uses former performances and decides about the next task according to Vygotsky's Zone of Proximal Development, that is to neither bore nor frustrate the student. We aim to support this task sequencer by a further automatically to gain information, namely students affect recognized from his speech input. However, the collection of the data from children needed for training an affect recognizer in this field is challenging as it is costly and complex and one has to consider privacy issues carefully. These problems lead to small data sets and limited performances of classification methods. Hence, in this work we propose an approach for improving the affect recognition in intelligent tutoring systems, which uses a special structure of several support vector machines with different input feature vectors. Furthermore, we propose a new kind of features for this problem. Different experiments with two real data sets show, that our approach is able to improve the classification performance on average by 49% in comparison to using a single classifier.

Most of the artificial intelligence and machine learning researches deal with big data today. However, there are still a lot of real world problems for which only small and noisy data sets exist. Hence, in this paper we focus on those small data sets of noisy images. Applying learning models to such data may not lead to the best possible results because of few and noisy training examples. We propose a hybrid neural network plait for improving the classification performance of state-of-the-art learning models applied to the images of such data sets. The improvement is reached by (1) using additionally to the images different further side information delivering different feature sets and requiring different learning models, (2) retraining all different learning models interactively within one common structure. The proposed hybrid neural network plait architecture reached in the experiments with 2 different data sets on average a classification performance improvement of 40% and 52% compared to a single convolutional neural network and 13% and 17% compared to a stacking ensemble method.

Passive brain-computer interaction (BCI) can provide useful information to understand a user's state and anticipate intentions, which is needed to support adaptivity and personalization. Given the huge variety of audiences, a game's capability of adapting to different user profiles-in particular to keep the player in flow-is crucial to make it ever more enjoyable and satisfying. We have performed a user experiment exploiting a four-electrode electroencephalogram (EEG) tool similar to the ones that are soon likely to appear in the market for game control. We have performed a spectral characterization of the video-gaming experience, also in comparison with other tasks. Results show that the most informative frequency bands for discriminating among gaming conditions are around low beta. Simple signals from the peripheral nervous system add marginal information. Classification of three levels of user states is possible, with good accuracy, using a support vector machine (SVM) classifier. A user-independent classification performs worse than a user-dependent approach (50.1% versus 66.4% rate). Personalized SVM training and validation time is reasonable (7-8 min). Thus, we argue that a personalized system could be implemented in a consumer context and research should aim at improving classifiers that can be trained online by end users.

Ability of adapting to different users is considered key to maximize the impact and effectiveness of Serious Games (SGs). Advances in neurosciences are making it possible to continuously monitor the player status. This paper reports the research work on the monitoring of the player flow status with a simple (4 electrode) commercial electroencephalogram (EEG). In particular, we focus on three consequential research questions: is it possible to statistically distinguish a flow from a boredom condition? For which wavelengths? Can different levels of boredom and flow be identified? Results - even if limited because of the small size of the test, are promising and enable further research. Statistically significant differences could be observed for brainwaves under various conditions. A machine learning classifier (SVM) was successful in the 2-level distinction case, in particular with personalized training.