A content analysis method to measure critical thinking in face-to-face and computer supported group learning

This paper gives a detailed account of the content analysis method developed at Queen's University Belfast to measure critical thinking during group learning, as used in our controlled comparisons between learning in face-to-face and computer conference seminars. From Garrison's 5 stages of critical thinking, and Henri's cognitive skills needed in CMC, we have developed two research instruments: a student questionnaire and this content analysis method. The content analysis relies on identifying, within transcripts, examples of indicators of obviously critical and obviously uncritical thinking, from which several critical thinking ratios can be calculated.

Robust face recognition using posterior union model based neural networks

Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the reliable local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN model has been evaluated on three face image databases (XM2VTS, AT&T and AR) using testing images subjected to various types of simulated and realistic partial distortion and occlusion. The new system has been compared to other approaches and has demonstrated improved performance.

Shyness and face scanning in children

Contrary to popular beliefs, a recent empirical study using eye tracking has shown that a non-clinical sample of socially anxious adults did not avoid the eyes during face scanning. Using eye-tracking measures, we sought to extend these findings by examining the relation between stable shyness and face scanning patterns in a non-clinical sample of 11-year-old children. We found that shyness was associated with longer dwell time to the eye region than the mouth, suggesting that some shy children were not avoiding the eyes. Shyness was also correlated with fewer first fixations to the nose, which is thought to reflect the typical global strategy of face processing. Present results replicate and extend recent work on social anxiety and face scanning in adults to shyness in children. These preliminary findings also provide support for the notion that some shy children may be hypersensitive to detecting social cues and intentions in others conveyed by the eyes. Theoretical and practical implications for understanding the social cognitive correlates and treatment of shyness are discussed. (C) 2009 Elsevier Ltd. All rights reserved.

When some is actually all: Scalar inferences in face-threatening contexts

Accounts of the scalar inference from 'some X-ed' to 'not all X-ed' are central to the debate between contemporary theories of conversational pragmatics. An important contribution to this debate is to identify contexts that decrease the endorsement rate of the inference. We suggest that the inference is endorsed less often in face-threatening contexts, i.e., when X implies a loss of face for the listener. This claim is successfully tested in Experiment 1. Experiment 2 rules out a possible confound between face-threatening contexts and lower-bound contexts. Experiment 3 shows that whilst saying 'some X-ed' when one knew for a fact that all X-ed is always perceived as an underinformative utterance, it is also seen as a nice and polite thing to do when X threatens the face of the listener. These findings are considered from the perspective of Relevance Theory as well as that of the Generalized Conversational Inference approach. (C) 2009 Elsevier B.V. All rights reserved.