Providing university teachers with high-quality opportunities for professional development cannot happen without data about the classroom environment. Currently, the most effective mechanism is for an expert to observe one or more lectures and provide personalized formative feedback to the instructor. Of course, this is expensive and unscalable, and perhaps most critically, precludes a continuous learning feedback loop for the instructor. In this paper, we present the culmination of two years of research and development on EduSense, a comprehensive sensing system that produces a plethora of theoretically-motivated visual and audio features correlated with effective instruction, which could feed professional development tools in much the same way as a Fitbit sensor reports step count to an end user app. Although previous systems have demonstrated some of our features in isolation, EduSense is the first to unify them into a cohesive, real-time, in-the-wild evaluated, and practically-deployable system. Our two studies quantify where contemporary machine learning techniques are robust, and where they fall short, illuminating where future work remains to bring the vision of automated classroom analytics to reality.

/pdf/edusense-practical-classroom-sensing-at-scale-1bjudzt0gj.pdf

EduSense: Practical Classroom Sensing at Scale

https://link.springer.com/content/pdf/10.1007/s12369-021-00851-0.pdf

Potential Applications of Social Robots in Robot-Assisted Interventions for Social Anxiety

Rehearsing in front of a live audience is invaluable when preparing for important presentations. However, not all presenters take the opportunity to engage in such rehearsal, due to time constraints, availability of listeners who can provide constructive feedback, or public speaking anxiety. We present RoboCOP, an automated anthropomorphic robot head that acts as a coach to provide spoken feedback during presentation rehearsals at both the individual slide and overall presentation level. The robot offers conversational coaching on three key aspects of presentations: speech quality, content coverage, and audience orientation. The design of the feedback strategies was informed by findings from an exploratory study with academic professionals who were experienced in mentoring students on their presentations. In a within-subjects study comparing RoboCOP to visual feedback and spoken feedback without a robot, the robotic coach was shown to lead to significant improvement in the overall experience of presenters. Results of a second within-subjects evaluation study comparing RoboCOP with existing rehearsal practices show that our system creates a natural, interactive, and motivating rehearsal environment that leads to improved presentation quality.

https://dl.acm.org/doi/pdf/10.1145/3090092

RoboCOP: A Robotic Coach for Oral Presentations

Multiple Composite Scenarios: A Game-Based Methodology for the Prevention of Mental Disorders

Anxiety is a significant mental health concern that can be effectively monitored using physiological signals such as galvanic skin response (GSR). While the potential of machine learning (ML) algorithms to enhance the classification of anxiety based on GSR signals is promising, their effectiveness in this context remains largely underexplored. This study addresses this gap by investigating the performance of three commonly used ML algorithms, support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF), in classifying anxiety and stress activity using a benchmark dataset. We employed two feature extraction methods: traditional statistical feature extraction and an innovative automatic feature extraction approach utilizing a 14-layer autoencoder, aimed at improving classification performance. Our findings demonstrate the effectiveness of using GSR signals and the robust performance of the KNN algorithm in accurately classifying anxiety levels. The KNN algorithm achieved the highest accuracy in both the statistical and automatic feature extraction approaches, with results of 96.9% and 98.2%, respectively. These findings highlight the effectiveness of KNN for anxiety detection and emphasize the need for advanced feature extraction techniques to enhance classification outcomes in mental health monitoring. 

Anxiety Detection System Based on Galvanic Skin Response Signals

Fear of public speaking is an anxiety that most people encounter at some point in their lives. Some people with extreme fear of public speaking will avoid it at all costs, even if it means their personal, professional, and social growth may be hindered. AwareMe incorporates aspects of Cognitive Behavioral Therapy (CBT) and good public speaking practices to make an empowering assistive technology for people who suffer from different levels of public speaking anxiety. AwareMe is a device that provides feedback on voice pitch, filler words, and words per minute during presentation practice. It has a detachable wristband, worn during presentations, that provides visual and haptic feedback. Our approach differs from existing solutions because it focuses on bringing the users' attention to how their anxiety influences their speech patterns; this awareness helps them to directly improve their public speaking skills.

AwareMe: Addressing Fear of Public Speech through Awareness

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AwareMe: Addressing Fear of Public Speech through Awareness