Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task is a phase-shifted periodic time series. In particular, we develop a novel Bayesian nonparametric model capturing a mixture of Gaussian processes where each task is a sum of a group-specific function and a component capturing individual variation, in addition to each task being phase shifted. We develop an efficient \textsc{em} algorithm to learn the parameters of the model. As a special case we obtain the Gaussian mixture model and \textsc{em} algorithm for phased-shifted periodic time series. Furthermore, we extend the proposed model by using a Dirichlet Process prior and thereby leading to an infinite mixture model that is capable of doing automatic model selection. A Variational Bayesian approach is developed for inference in this model. Experiments in regression, classification and class discovery demonstrate the performance of the proposed models using both synthetic data and real-world time series data from astrophysics. Our methods are particularly useful when the time series are sparsely and non-synchronously sampled.

Here we describe the SHARE system, a web service based framework for distributed querying and reasoning on the semantic web. The main innovations of SHARE are: (1) the extension of a SPARQL query engine to perform on-demand data retrieval from web services, and (2) the extension of an OWL reasoner to test property restrictions by means of web service invocations. In addition to enabling queries across distributed datasets, the system allows for a target dataset that is significantly larger than is possible under current, centralized approaches. Although the architecture is equally applicable to all types of data, the SHARE system targets bioinformatics, due to the large number of interoperable web services that are already available in this area. SHARE is built entirely on semantic web standards, and is the successor of the BioMOBY project.

Educators who wish to engage their students in multi-agent experimentation and learning need an inexpensive multi-robot system that leverages existing equipment and open-source software. This paper proposes Overwatch as an inexpensive educational tool for teaching and experimenting in multi-robot systems. The interaction of multiple agents within a single environment is an important area of study. It is vital that agents within the environment perceive other agents as intelligent, acting within the environment as cooperative teammates or as competitive members of another team. To do so, the system must meet three goals: first, to allow multiple robots to communicate and coordinate; second, to localize within a shared global coordinate system; third, to recognize their teammates and other teams. The cost and scale of such experimental platforms places them outside the reach of many educational institutions or limits the number of agents that are interacting within the system \cite{Liu201160}. The goal of Overwatch is to create an experimental platform for multi-agent systems that is comprised of much smaller, albeit less capable, robots, many of which are prevalent in academic institutions already. Making use of available open-source libraries and utilizing lower cost robots, such as Scribblers, allows for experiments with many agents. This enables Overwatch to fit into the budget limitations of an academic setting. The Overwatch platform provides the Scribblers with global localization capabilities. This paper presents the system in detail and includes experiments to show its ability to localize, interact with other agents, and coordinate behaviors with these other agents. Additionally, the details to setup this system are also included.

This study expands upon an existing model of students’ reading comprehension ability within an intelligent tutoring system. The current system evaluates students’ natural language input using a local student model. We examine the potential to expand this model by assessing the linguistic features of self-explanations aggregated across entire passages. We assessed the relationship between 126 students’ reading comprehension ability and the cohesion of their aggregated self-explanations with three linguistic features. Results indicated that the three cohesion indices accounted for variance in reading ability over and above the features used in the current algorithm. These results demonstrate that broadening the window of NLP analyses can strengthen student models within ITSs.

Recent research has extended tutor strategies to model not just interventions to offer information and activities, but also interventions to support learners’ wills and motivation. It is important to investigate new ways, intertwined with learners’ performance (successful completion of tasks) and judgement (self-report questionnaires), for evaluating tutor intervention strategies. One promising way is the use of physiological sensors. Within this paper, we study some motivational strategies that were implemented in a serious game called HeapMotiv to support learners’ performance and motivation. We build several hidden Markov models which use Keller’s ARCS model of motivation and electrophysiological data (heart rate HR, skin conductance SC and EEG) and are able to identify physiological patterns correlated with different motivational strategies.

In student modeling, the concept of "mastery learning" i.e. that a student continues to learn a skill till mastery is attained is important. Usually, mastery is defined in terms of most recent student performance. This is also the case with models such as Knowledge Tracing which estimate knowledge solely based on patterns of questions a student gets correct and the task usually is to predict immediate next action of the student. In retrospect however, it is not clear if this is a good definition of mastery since it is perhaps more useful to focus more on student retention over a longer period of time. This paper improves a recently introduced model by Wang and Beck that predicts long term student performance by clustering the students and generating multiple predictions by using a recently developed ensemble technique. Another contribution is that we introduce a novel clustering algorithm we call "Regularity Clustering" and show that it is superior in the task of predicting student retention over more popular techniques such as k-means and Spectral Clustering.

Research on individual differences indicates that students vary in how they interact with and perform while using intelligent tutoring systems (ITSs). However, less research has investigated how individual differences affect students’ interactions with game-based features. This study examines how learning outcomes and interactions with specific game-based features (off-task personalization vs. on-task mini games) within a game-based ITS, iSTART-ME, vary as a function of students’ performance orientation. The current study (n=40) is part of a larger study (n=126) conducted with high school students. The analyses in this study focus on those students assigned to iSTART-ME. Results indicate that students with higher levels of performance orientation perform better during training, progress further within the system, and interact less frequently with off-task game-based features. These results provide further evidence that individual differences play an important role in influencing students’ interactions and achievement within learning environments.

Many intelligent tutoring system (ITS) researchers are looking at ways to detect and to respond to student emotional states (for instance animated pedagogical agents that mirror student emotion). Such interventions are complicated to build, and do not take advantage of the potential for teachers to be part of the process. We present two studies that intervene when a student is having trouble by presenting the student with a YouTube video that is recorded by their own teacher and that delivers a motivational message to help them to persist with the learning session. We experimentally compared two different motivational interventions, which are both grounded in the literature on student affect and motivation. We also had a control condition that had no video. We found that when looking at students’ self-reports on the value of mathematics, we found a main effect of condition for the value-video. In Study 2 we examined whether these 60-second videos could impact homework completion rates and found that in fact homework completion rates were higher for students in the value-video condition. The present research is suggestive of a somewhat novel use of teacher-generated content that could easily be incorporated into other ITSs.

Introduction to the papers presented at the 2013 FLAIRS Conference Special Track on Intelligent Tutoring Systems

Finding useful patterns in stock market data requires tremendous analytical skills and effort. To help investors manage their portfolios, we developed a tool for clustering and visualizing stock market data using an unsupervised learning algorithm called Self-Organizing Map. Our tool is intended to assist users in identifying groups of stocks that have similar price movement patterns over a period of time. We performed a visual analysis by comparing the resulting visualization with Yahoo Finance charts. Overall, we found that the Self-Organizing Map algorithm can analyze and cluster the stock market data reasonably.