Learn More
Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development(More)
SUMMARY This study evaluated seasonal differences (spring and summer) in the processing of Typhu ungustifolia leaves in a Mediterranean river. The experiment extended over 120 days, corresponding to six sampling periods for each experiment. Leaves (5 g) were placed in 0.5 cm mesh-size bags. After recovering the sample, macroinvertebrates inside the leaf(More)
In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including(More)
In this paper we investigate the usefulness of eye tracking data for predicting emotions relevant to learning, specifically boredom and curiosity. The data was collected during a study with MetaTutor, an intelligent tutoring system (ITS) designed to promote the use of self-regulated learning strategies. We used a variety of machine learning and feature(More)
Germination of Agrostis castellana caryopses is not affected by arsenate. The viability of caryopses of tolerant Agrostis castellana is lower than that of sensitive populations. The possible relation with ‘cost’ of tolerance is discussed. Tolerant populations of Agrostis castellana and Agrostis delicatula are clearly distinguished by their maximum root(More)
What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980(More)
We apply a recently proposed technique – Multi-task Multi-Kernel Learning (MTMKL) – to the problem of modeling students' wellbeing. Because wellbe-ing is a complex internal state consisting of several related dimensions, Multi-task learning can be used to classify them simultaneously. Multiple Kernel Learning is used to efficiently combine data from(More)
To filter noise or detect features within physiological signals, it is often effective to encode expert knowledge into a model such as a machine learning classifier. However, training such a model can require much effort on the part of the researcher; this often takes the form of manually labeling portions of signal needed to represent the concept being(More)
This paper presents a user study that investigates the factors affecting student attention to user-adaptive hints during interaction with an educational computer game. The study focuses on Prime Climb, an educational game designed to provide individualized support for learning number factorization skills in the form of textual hints based on a model of(More)
This paper presents a system prototype designed to capture naturally occurring instances of positive emotion during the course of normal interaction with a computer. A facial expression recognition algorithm is applied to images captured with the user's webcam. When the user smiles, both a photo and a screenshot are recorded and saved to the user's profile(More)