Speech disorders are among the salient characteristics of negative symptoms of schizophrenia. Such impairments are often exhibited through disorganized speech, inappropriate affective prosody, and poverty of speech. The current method of detecting such symptoms requires the expertise of a trained clinician, which may be prohibitive due to cost, stigma or high patient-to-clinician ratio. An objective method to extract nonverbal and verbal speech-related cues can help to automate and simplify the assessment method of severity of speechrelated symptoms of schizophrenia. In this paper, a novel automated method is presented which uses speech content from schizophrenic patients to predict the clinician-assigned subjective ratings of their negative symptoms. Specifically, the interviews of 50 schizophrenia patients were recorded and features related to acoustics, linguistics and non-verbal conversation were extracted. The subjective ratings can be accurately predicted from the objective features with an accuracy of 64-82% using machine learning algorithms with leave-one-out cross-validation. Our findings support the utility of automated speech analysis to aid clinician diagnosis, monitoring and understanding of schizophrenia.
The motivation for this paper is derived from the fact that there has been increasing interest among researchers and practitioners in developing technologies that capture, model and analyze learning and teaching experiences that take place beyond computer-based learning environments. In this paper, we review case studies of tools and technologies developed to collect and analyze data in educational settings, quantify learning and teaching processes and support assessment of learning and teaching in an automated fashion. We focus on pipelines that leverage information and data harnessed from physical spaces and/or integrates collected data across physical and digital spaces. Our review reveals a promising field of physical classroom analysis. We describe some trends and suggest potential future directions. Specifically, more research should be geared towards a) deployable and sustainable data collection set-ups in physical learning environments, b) teacher assessment, c) developing feedback and visualization systems and d) promoting inclusivity and generalizability of models across populations. CCS CONCEPTS• Human-centred computing~Visualization design and evaluation methods •Information systems~Data analytics KEYWORDSFace-to-face classroom analysis, co-located learning, physical learning analytics, educational data mining, educational technologies We utilized a three-pronged approach to select studies for review. First, relevant journals and conference proceedings, such as journals and proceedings of known conferences targeted at showcasing advancements and innovation in the field of learning sciences and learning analytics, were shortlisted and searched using a targeted search strategy. We include the following journals:
Human-robot interaction in corporate workplaces is a research area which remains unexplored. In this paper, we present the results and analysis of a social experiment we conducted by introducing a humanoid robot (Nadine) into a collaborative social workplace. The humanoid's primary task was to function as a receptionist and provide general assistance to the customers. Moreover, the employees who interacted with Nadine were given over a month to get used to her capabilities, after which, the feedback was collected from the staff on the grounds of influence on productivity, affect experienced during interaction and their views on social robots assisting with regular tasks. Our results show that the usage of social robots for assisting with normal day-today tasks is taken quite positively by the co-workers and that in the near future, more capable humanoid social robots can be used in workplaces for assisting with menial tasks. Finally, we posit that surveys such as ours could result in constructive opinions based on technological awareness, rather than opinions from media-driven fears about the threats of technology.
While in training, teachers are often given feedback about their teaching style by experts who observe the classroom. Trained observer coding of classroom such as the Classroom Assessment Scoring System (CLASS) provides valuable feedback to teachers, but the turnover time for observing and coding makes it hard to generate instant feedback. We aim to design technological platforms that analyze real-life data in learning environments, and generate automatic objective assessments in real-time.To this end, we adopted state-of-the-art speech processing technologies and conducted trials in real-life teaching environments. Although much attention has been devoted to speech processing for numerous applications, few researchers have attempted to apply speech processing for analyzing activities in classrooms. To address this shortcoming, we developed speech processing algorithms that detect speakers and social behavior from audio recordings in classrooms. Specifically, we aim to infer the climate in the classroom from non-verbal speech cues. We extract non-verbal speech cues and lowlevel audio features from speech segments and we train classifiers based on those cues. We were able to distinguish between positive and negative CLASS climate scores with 70-80% accuracy (estimated by leave-one-out crossvalidation). The results indicate the potential of predicting classroom climate automatically from audio recordings.
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