Measuring the Engagement of the Learner in a Controlled Environment Using Three Different Biosensors

Irrespective of the educational model, the major challenge is how to achieve maximum efficiency of the education process and keep learners engaged during learning. This paper investigates the relationship between emotions and engagement in the E-learning environment, and how recognizing the learners emotions and changing the content delivery accordingly can affect the efficiency of the E-learning process. The proposed experiment aims to identify ways to increase the engagement of the learners, hence, enhance the efficiency of the learning process and the quality of learning. A controlled experiment was conducted to investigate participants emotions using bio sensors such as eye tracker, EEG, and camera to capture facial images in different emotional states. One-way analysis of variance (ANOVA) test and t-Test was carried out to compare the performance of the three groups and show if there was an effect of using the Affective E-learning system to improve the learners performance. Our findings support the conclusion that using bio sensors as a quantitative research tool to investigate human behaviours and measure emotions in real time can significantly enhance the efficiency of E-learning

Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD

Multimodal data as a means to understand the learning experience

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xDelia final report: emotion-centred financial decision making and learning

xDelia is a 3-year pan-European project building on the knowledge, skills, and competences of seven partner organisations from a variety of research disciplines and from business. The principal objective of xDelia is to develop technology-enhanced learning approaches that help improve the financial decision making of investors who trade frequently using an electronic trading platform. We focus on emotions, and how they affect maladaptive decision biases and trading performance. Our earlier field work with traders has shown that the development of emotion regulation skills is a key facet of trader expertise. For that reason we consider expert traders our benchmark for adaptive behaviour rather than normative rationality. Our goal is to provide investors with the tools and techniques to develop greater self-awareness of internal states, increase their ability to reflect critically on emotion-informed choices, develop emotion management skills, and support the transfer of these skills to the real-world practice setting of financial trading. This report provides a comprehensive overview of what xDelia is about and what we have achieved over the life of the project. In the sections that follow, we explain the decision problems investors are faced with in a fast paced environment and the limitations of traditional approaches to reduce cognitive errors; introduce an alternative, technology-enhanced learning approach of diagnosis and feedback, skill development, and transfer; describe the learning intervention comprising twelve autonomous learning elements that we have developed; and present evidence from thirty-five studies we have conducted on learning effects and stakeholder acceptance

Cognitive load estimation in VR flight simulator

This paper discusses the design and development of a low-cost virtual reality (VR) based flight simulator with cognitive load estimation feature using ocular and EEG signals.  Focus is on exploring methods to evaluate pilot’s interactions with aircraft by means of quantifying pilot’s perceived cognitive load under different task scenarios. Realistic target tracking and context of the battlefield is designed in VR. Head mounted eye gaze tracker and EEG headset are used for acquiring pupil diameter, gaze fixation, gaze direction and EEG theta, alpha, and beta band power data in real time. We developed an AI agent model in VR and created scenarios of interactions with the piloted aircraft. To estimate the pilot’s cognitive load, we used low-frequency pupil diameter variations, fixation rate, gaze distribution pattern, EEG signal-based task load index and EEG task engagement index. We compared the physiological measures of workload with the standard user’s inceptor control-based workload metrics. Results of the piloted simulation study indicate that the metrics discussed in the paper have strong association with pilot’s perceived task difficulty

Student Teacher Interaction While Learning Computer Science: Early Results from an Experiment on Undergraduates

The scope of this paper was to find out how the students in Computer Science perceive different teaching styles and how the teaching style impacts the learning desire and interest in the course. To find out, we designed and implemented an experiment in which the same groups of students (86 students) were exposed to different teaching styles (presented by the same teacher at a difference of two weeks between lectures). We tried to minimize external factors' impact by carefully selecting the dates (close ones), having the courses in the same classroom and on the same day of the week, at the same hour, and checking the number and the complexity of the introduced items to be comparable. We asked for students' feedback and we define a set of countable body signs for their involvement in the course. The results were comparable by both metrics (body language) and text analysis results, students prefer a more interactive course, with a relaxing atmosphere, and are keener to learn in these conditions.Comment: CSEDU 2023, 15th International Conference on Computer Supported Educatio

Sensors and Wearables in Oncology: A study of the Barriers and Facilitators to Adoption

Innovation, although a subject of considerable debate (e.g., Baregheh et al., 2009; Christensen, 1997), can be defined as the introduction and dissemination of a new or a different idea into use or practice that drives impact (Solis and Sinfield, 2014). Many studies and editorials have highlighted the complexity of the United States health system and detailed the slow speed by which innovative ideas materialize into impactful innovations (Continuing America’s leadership (2017); England & Stewart (2007); Kannampallil, Schauer, Cohen & Patel (2011)). While there are many advances in sensor and wearable technologies in this instance, the adoption rate by oncologists has been slow. This slow or lack of adoption has a deep impact on the care, comfort, and potential survival of cancer patients. This study intends to describe barriers and facilitators to sensor technology adoption in oncology, then map those barriers and facilitators across two sets of stakeholders (oncologists and technologists). This qualitative study highlights key barriers including costs of technology, lack of time by oncologists, lack of communication between the two group, cultural and organizational factors, as well as global and policy factors. The enablers included the desire by both groups to work together for the benefit of the patients, as well as the need for tailored interventions leveraging an architected framework to propel this collaboration and align the stakeholders. The result of the study is a comprehensive conceptual framework and next steps detailed a short, medium, and long-term approach leading to adaptation, adoption, and diffusion. Being a first study of its kind, this can lead to further advancement in the field in terms of research and translational science