“It’s Just Nice Having a Real Teacher”: Student Perceptions of Online versus Face-to-Face Instruction

With recent increases in online enrollment, undergraduate students are far more likely to experience an online learning environment than they were in the past. While existing literature provides general insight into reasons why students may or may not prefer online instruction, it is unclear whether these preferences are shaped by student’s perceptions of online learning or actual experience with online courses. To address this gap, undergraduate students enrolled in either online (n=370) or face-to-face (n=360) courses were surveyed about their course format preference. A content analysis of the responses was performed with the findings suggesting that 1) student perceptions may be based on old typologies of distance education akin to correspondence courses, regardless of actual experience with online courses, and 2) course preferences are related to issues involving teaching presence and self-regulated learning. The implications of this research for developing more effective online pedagogy are discussed

Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer's disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification

An Introduction to EEG Source Analysis with an illustration of a study on Error-Related Potentials

International audienceOver the last twenty years blind source separation (BSS) has become a fundamental signal processing tool in the study of human electroencephalography (EEG), other biological data, as well as in many other signal processing domains such as speech, images, geophysics and wireless communication (Comon and Jutten, 2010). Without relying on head modeling BSS aims at estimating both the waveform and the scalp spatial pattern of the intracranial dipolar current responsible of the observed EEG, increasing the sensitivity and specificity of the signal received from the electrodes on the scalp. This chapter begins with a short review of brain volume conduction theory, demonstrating that BSS modeling is grounded on current physiological knowledge. We then illustrate a general BSS scheme requiring the estimation of second-order statistics (SOS) only. A simple and efficient implementation based on the approximate joint diagonalization of covariance matrices (AJDC) is described. The method operates in the same way in the time or frequency domain (or both at the same time) and is capable of modeling explicitly physiological and experimental source of variations with remarkable flexibility. Finally, we provide a specific example illustrating the analysis of a new experimental study on error-related potentials

Extension of EFICA algorithm for blind separation of piecewise stationary non Gaussian sources

International audienc

A Treatment of EEG Data by Underdetermined Blind Source Separation for Motor Imagery Classification

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201