The implementation of chatbot-mediated immediacy for synchronous communication in an online chemistry course.

Low student engagement and motivation in online classes are well-known issues many universities face, especially with distance education during the COVID-19 pandemic. The online environment makes it even harder for teachers to connect with their students through traditional verbal and nonverbal behaviours, further decreasing engagement. Yet, addressing such problems with 24/7 synchronous communication is overly demanding for faculty. This paper details an automated Question-Answering chatbot system trained in synchronous communication and instructor immediacy techniques to determine its suitability and effectiveness in attending to students undergoing an online Chemistry course. The chatbot is part of a new wave of affective focused chatbots that can benefit students' learning process by connecting with them on a relatively more humanlike level. As part of the pilot study in the development of this chatbot, qualitative interviews and self-report data capturing student-chatbot interactions, experiences and opinions have been collected from 12 students in a Singaporean university. Thematic analysis was then employed to consolidate these findings. The results support the chatbot's ability to display several communication immediacy techniques well, on top of responding to students at any time of the day. Having a private conversation with the chatbot also meant that the students could fully focus their attention and ask more questions to aid their learning. Improvements were suggested, in relation to the chatbot's word detection and accuracy, accompanied by a framework to develop communication immediacy mechanics in future chatbots. Our findings support the potential of this chatbot, once modified, to be used in a similar online setting. Supplementary Information: The online version contains supplementary material available at 10.1007/s10639-023-11602-1.

3D object retrieval using salient views.

This paper presents a method for selecting salient 2D views to describe 3D objects for the purpose of retrieval. The views are obtained by first identifying salient points via a learning approach that uses shape characteristics of the 3D points (Atmosukarto and Shapiro in International workshop on structural, syntactic, and statistical pattern recognition, 2008; Atmosukarto and Shapiro in ACM multimedia information retrieval, 2008). The salient views are selected by choosing views with multiple salient points on the silhouette of the object. Silhouette-based similarity measures from Chen et al. (Comput Graph Forum 22(3):223-232, 2003) are then used to calculate the similarity between two 3D objects. Retrieval experiments were performed on three datasets: the Heads dataset, the SHREC2008 dataset, and the Princeton dataset. Experimental results show that the retrieval results using the salient views are comparable to the existing light field descriptor method (Chen et al. in Comput Graph Forum 22(3):223-232, 2003), and our method achieves a 15-fold speedup in the feature extraction computation time.

Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models.

Craniosynostosis is the premature fusion of the bones of the calvaria resulting in abnormal skull shapes that can be associated with increased intracranial pressure. While craniosynostoses of multiple different types can be easily diagnosed, quantifying the severity of the abnormality is much more subjective and not a standard part of clinical practice. For this purpose we have developed a severity-based retrieval system that uses a logistic regression approach to quantify the severity of the abnormality of each of three types of craniosynostoses. We compare several different sparse feature selection techniques: L1 regularized logistic regression, fused lasso, and clustering lasso (cLasso). We evaluate our methodology in three ways: 1) for classification of normal vs. abnormal skulls, 2) for comparing pre-operative to post-operative skulls, and 3) for retrieving skulls in order of abnormality severity as compared with the ordering of a craniofacial expert.

Longitudinal, three-dimensional analysis of head shape in children with and without deformational plagiocephaly or brachycephaly.

OBJECTIVES: To assess 3-dimensional (3D) changes in head shape in infancy and at age 18 months in children with and without plagiocephaly or brachycephaly. STUDY DESIGN: Using a longitudinal design, we evaluated head shape using 3D surface imaging. We compared the head shapes of children with (1) diagnosed deformational plagiocephaly or brachycephaly (cases; n=233); (2) unaffected controls, with no evidence of dysmorphology (n=167); and (3) affected controls, who despite having no previous diagnosis demonstrated skull dysmorphology on 3D surface imaging (n=70). RESULTS: Cases had greater skull flattening and asymmetry than unaffected controls at both time points, as did controls with skull dysmorphology. In all groups, head shapes became less flat and more symmetric over time. Among cases, symmetry improved slightly more for those who received orthotic treatment. CONCLUSIONS: Although head shape improves over time for children with deformational plagiocephaly or brachycephaly, skull dysmorphology persists relative to unaffected controls. Further research is needed to clarify the extent to which these differences are detectable to clinicians and lay observers.

The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications.

Craniofacial disorders commonly result in various head shape dysmorphologies. The goal of this work is to quantify the various 3D shape variations that manifest in the different facial abnormalities in individuals with a craniofacial disorder called 22q11.2 Deletion Syndrome. Genetic programming (GP) is used to learn the different 3D shape quantifications. Experimental results show that the GP method achieves a higher classification rate than those of human experts and existing computer algorithms [1], [2].

Shape-Based Classification of 3D Head Data.

Craniofacial disorders are one of the most common category of birth defects worldwide, and are an important topic of biomedical research. In order to better understand these disorders and correlate them with genetic patterns and life outcomes, researchers need to quantify the craniofacial anatomy. In this paper we introduce several different craniofacial descriptors that are being used in research studies for two craniofacial disorders: the 22q11.2 deletion syndrome (a genetic disorder) and plagiocephaly/brachycephaly, disorders caused by pressure on the head. Experimental results show that our descriptors show promise for quantifying craniofacial shape.

Automatic 3D Shape Severity Quantification and Localization for Deformational Plagiocephaly.

Recent studies have shown an increase in the occurrence of deformational plagiocephaly and brachycephaly in children. This increase has coincided with the "Back to Sleep" campaign that was introduced to reduce the risk of Sudden Infant Death Syndrome (SIDS). However, there has yet to be an objective quantification of the degree of severity for these two conditions. Most diagnoses are done on subjective factors such as patient history and physician examination. The existence of an objective quantification would help research in areas of diagnosis and intervention measures, as well as provide a tool for finding correlation between the shape severity and cognitive outcome. This paper describes a new shape severity quantification and localization method for deformational plagiocephaly and brachycephaly. Our results show that there is a positive correlation between the new shape severity measure and the scores entered by a human expert.