Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification

A Comparative Study of Predicting Student’s Performance by use of Data Mining Techniques

Educational systems need innovative ways to improve quality of education to achieve the best results and decrease the failure rate.  Educational Data Mining (EDM) has boomed in the educational systems recently as it enables to analyze and predict student performance so that measures can be taken in advance. Due to lack of prediction accuracy, improper attribute analysis, and insufficient datasets, the educational systems are facing difficulties and challenges exist to effectively benefit from EDM. In order to improve the prediction process, a thorough study of literature and selection of the best prediction technique is very important. The main objective of this paper is to present a comparative study of various recently used data mining techniques, classification algorithms, their impact on datasets as well as the prediction attribute’s result in a clear and concise way. The paper also identifies the best attributes that will help in predicting the student performance in an efficient way

Sensitivity to Social Reward in Music Behavior Changes After Music Training in Preadolescence

During the last decades, a growing body of research on musical pleasure has shed light on individual differences and mechanisms underlying music reward sensitivity. Music training has been identified as a factor able to affect the rewarding experience associated with music, although in the existing literature, evidence on children is scarce. The current study focused on the effects of music training and individual musical engagement on sensitivity to music reward in preadolescence. One hundred and forty-two students (aged 10-14 years) at three different Italian music middle schools were tested three times over a period of one year and a half. Eighty two children belonged to a music curriculum within the school and 60 belonged to a standard curriculum. The Barcelona Music Reward Questionnaire (BMRQ), a multi-dimensional assessment tool to measure music reward sensitivity, was used, and pre-existing differences in music sophistication were controlled for. Moreover, in addition to the between-group comparison, highlighting the formal music training variable, the actual amount of musical activities and engagement both in and out of school was also taken into account. Several positive effects in terms of music social reward were found for students with a high level of musical engagement. Also, results showed a main effect of gender, with girls showing higher scores than boys in total BMRQ score and in several subdomains. Taken together, these data provide new evidence for the special role played by collective musical activities and suggest that music training may be able to promote social connection in preadolescence

Detecting and Modelling Stress Levels in E-Learning Environment Users

A modern Intelligent Tutoring System (ITS) should be sentient of a learner's cognitive and affective states, as a learner’s performance could be affected by motivational and emotional factors. It is important to design a method that supports low-cost, task-independent and unobtrusive sensing of a learner’s cognitive and affective states, to improve a learner's experience in e-learning, as well as to enable personalized learning. Although tremendous related affective computing research were done in this area, there is a lack of empirical research that can automatically measure a learner's stress using objective methods. This research is set to examine how an objective stress measurement model can be developed, to compute a learner’s cognitive and emotional stress automatically using mouse and keystroke dynamics. To ensure the measurement is not affected even if the user switches between tasks, three preliminary research experiments were carried out based on three common tasks during e-learning − search, assessment and typing. A stress measurement model was then built using the datasets collected from the experiments. Three stress classifiers were tested, namely certainty factors, feedforward back-propagation neural network and adaptive neuro-fuzzy inference system. The best classifier was then integrated into the ITS stress inference engine, which is designed to decide necessary adaptation, and to provide analytical information of learners' performances, which include stress levels and learners’ behaviours when answering questions

The Artisan Teacher: A Field Guide to Skillful Teaching

A capstone submitted in partial fulfillment of the requirements for the degree of Doctor of Education in the College of Education at Morehead State University by Michael A. Rutherford on March 26, 2013