Keep Me in the Loop: Real-Time Feedback with Multimodal Data

This paper describes the CPR Tutor, a real-time multimodal feedback system for cardiopulmonary resuscitation (CPR) training. The CPR Tutor detects training mistakes using recurrent neural networks. The CPR Tutor automatically recognises and assesses the quality of the chest compressions according to five CPR performance indicators. It detects training mistakes in real-time by analysing a multimodal data stream consisting of kinematic and electromyographic data. Based on this assessment, the CPR Tutor provides audio feedback to correct the most critical mistakes and improve the CPR performance. The mistake detection models of the CPR Tutor were trained using a dataset from 10 experts. Hence, we tested the validity of the CPR Tutor and the impact of its feedback functionality in a user study involving additional 10 participants. The CPR Tutor pushes forward the current state of the art of real-time multimodal tutors by providing: (1) an architecture design, (2) a methodological approach for delivering real-time feedback using multimodal data and (3) a field study on real-time feedback for CPR training. This paper details the results of a field study by quantitatively measuring the impact of the CPR Tutor feedback on the performance indicators and qualitatively analysing the participants’ questionnaire answers.

Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations

Online education platforms play an increasingly important role in mediating the success of individuals’ careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform principles, context, and pedagogy. Though the importance of ensuring equality of learning opportunities has been well investigated in traditional institutions, how this equality can be operationalized in online learning ecosystems through recommender systems is still under-explored. In this paper, we shape a blueprint of the decisions and processes to be considered in the context of equality of recommended learning opportunities, based on principles that need to be empirically-validated (no evaluation with live learners has been performed). To this end, we first provide a formalization of educational principles that model recommendations’ learning properties, and a novel fairness metric that combines them to monitor the equality of recommended learning opportunities among learners. Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle to a certain degree for all learners, constrained to their individual preferences. Under this view, we explore the learning opportunities provided by recommender systems in a course platform, uncovering systematic inequalities. To reduce this effect, we propose a novel post-processing approach that balances personalization and equality of recommended opportunities. Experiments show that our approach leads to higher equality, with a negligible loss in personalization. This paper provides a theoretical foundation for future studies of learners’ preferences and limits concerning the equality of recommended learning opportunities.

The Power of Voice to Convey Emotion in Multimedia Instructional Messages

This study examines an aspect of the role of emotion in multimedia learning, i.e., whether participants can recognize the instructor’s positive or negative emotion based on hearing short clips involving only the instructor’s voice just as well as also seeing an embodied onscreen agent. Participants viewed 16 short video clips from a statistics lecture in which an animated instructor, conveying a happy, content, frustrated, or bored emotion, stands next to a slide as she lectures (agent present) or uses only her voice (agent absent). For each clip, participants rated the instructor on five-point scales for how happy, content, frustrated, and bored the instructor seemed. First, for happy, content, and bored instructors, participants were just as accurate in rating emotional tone based on voice only as with voice plus onscreen agent. This supports the voice hypothesis, which posits that voice is a powerful source of social-emotional information. Second, participants rated happy and content instructors higher on happy and content scales and rated frustrated and bored instructors higher on frustrated and bored scales. This supports the positivity hypothesis, which posits that people are particularly sensitive to the positive or negative tone of multimedia instructional messages.