Voluntary pupil size change as control in eyes only interaction

We investigate consciously controlled pupil size as an input modality. Pupil size is affected by various processes, e.g., physical activation, strong emotional experiences and cognitive effort. Our hypothesis is that given continuous feedback, users can learn to control pupil size via physical and psychological self-regulation. We test it by measuring the magnitude of self evoked pupil size changes following seven different instructions, while providing real time graphical feedback on pupil size. Results show that some types of voluntary effort affect pupil size on a statistically significant level. A second controlled experiment confirms that subjects can produce pupil dilation and constriction on demand during paced tasks. Applications and limitations to using voluntary pupil size manipulation as an input modality are discussed. ACM Classification Keyword

Development and Usability Evaluation of an E-learning Application Using Eye-tracking

The primary goal of this research is to use eye-tracking in the development and usability evaluation of an e-learning tool called "Problem Solving Environment for Continuous Process Design" (PSE). The PSE is meant to aid engineering students in learning the design processes of automated manufacturing systems. PSE is a user-interactive Flash application which gives the user an opportunity to virtually design an automated industrial process by manipulating the parameters associated with it. PSE is evaluated using eye-tracking experiments in which users' eye movements are tracked using camera and sensors to determine users' gaze direction and fixations. The data collected from the experiment is used to determine if use of visual cues improved the usability of the PSE. Results show that use of visual cues for gaze direction improved the usability of the PSE application, based on faster task completion times and improved navigability

A gaze-based learning analytics model: in-video visual feedback to improve learner's attention in MOOCs

In the context of MOOCs, “With-me-ness” refers to the extent to which the learner succeeds in following the teacher, specifically in terms of looking at the area in the video that the teacher is explaining. In our previous works, we employed eye-tracking methods to quantify learners’ With-me-ness and showed that it is positively correlated with their learning gains. In this contribution, we describe a tool that is designed to improve With-me-ness by providing a visual aid superimposed on the video. The position of the visual aid is suggested by the teachers’ dialogue and deixis, and it is displayed when the learner’s With-me-ness is under the average value, which is computed from the other students’ gaze behavior. We report on a user-study that examines the effectiveness of the proposed tool. The results show that it significantly improves the learning gain and it significantly increases the extent to which the students follow the teacher. Finally, we demonstrate how With-me-ness can create a complete theoretical framework for conducting gaze based learning analytics in the context of MOOCs

An Experimental Study of Learning Behaviour in an ELearning Environment

To reach an adaptive eLearning course, it is crucial to control and monitor the student behaviour dynamically to implicitly diagnose the student learning style. Eye tracing can serve that purpose by investigate the gaze data behaviour to the learning content. In this study, we conduct an eye tracking experiment to analyse the student pattern of behaviour to output his learning style as an aspect of personalisation in an eLearning course. We use the electroencephalography EEG Epoc that reflects users emotions to improve our result with more accurate data. Our objective is to test the hypothesis whether the verbal and visual learning Styles reflect actual preferences according to Felder and Silverman Learning Style Model in an eLearning environment. Another objective is to use the outcome presented in this experiment as the starting point for further exhaustive experiments. In this paper, we present the actual state of our experiment, conclusions, and plans for future development

Employees Use Of Empathy To Improve Their Job Behaviour

Being simply cognitively capable would be inadequate for employees to satisfy job performance requirements associated with their job behaviour. Before an employee performs his job, he must understand what it entails because the activities and behaviours associated with a particular job are defined largely by the expectations and demands of other people, both inside and outside any organization. For instance, a teachers role is defined by the expectations of his or her pupils, their parents, school managers, and society at large. In order to create positive relationships within the organization, employees need to offer encouraging words to each other when they try something new, be patient, understanding, and empathetic. The aim of the study is therefore to determine the need to develop empathy as a social skill of emotionally intelligent teachers.The quantitative research method was used in this study. The findings strongly suggest that teachers must develop their social skill of empathy as part of their job behaviour. Empathy is an essential emotional intelligence trait for teachers to overtly include in their instructional strategies. Teachers who use instructional empathy will reduce anxiety and tension in the learning environment. Evidently, expressing empathy in the organization requires the employee to thoughtfully consider everyones feelings in the process of making intelligent decisions. This study affirms that an employee who has a developed sense of empathy will have a much better understanding of how to handle different situations which could range from dealing with underachieving learners to working with gifted pupils

Eye-tracking perspectives of students’ learning trough MOOCs

Activating student knowledge (ASK) before receiving learning materials improves their learning outcome (Tormey and LeDuc (2014)). We implement ASK through priming by using two versions of the same pretest in a dual eye-tracking study in a MOOC context. We propose an additional activity, a collaborative concept-map, based on the MOOC lecture to enable the students to reflect on what they learnt. The priming affects the learning gain, individual and collaborative gaze patterns. Textual priming stands better than schema priming in terms of learning outcome. Finally, the pairs having participants with similar gaze to each other have more learning gain

Inside Out: Detecting Learners' Confusion to Improve Interactive Digital Learning Environments

Confusion is an emotion that is likely to occur while learning complex information. This emotion can be beneficial to learners in that it can foster engagement, leading to deeper understanding. However, if learners fail to resolve confusion, its effect can be detrimental to learning. Such detrimental learning experiences are particularly concerning within digital learning environments (DLEs), where a teacher is not physically present to monitor learner engagement and adapt the learning experience accordingly. However, with better information about a learner's emotion and behavior, it is possible to improve the design of interactive DLEs (IDLEs) not only in promoting productive confusion but also in preventing overwhelming confusion. This article reviews different methodological approaches for detecting confusion, such as self-report and behavioral and physiological measures, and discusses their implications within the theoretical framework of a zone of optimal confusion. The specificities of several methodologies and their potential application in IDLEs are discussed

Relationship between Personality Disorder and Moral Judgment Considering the Moderating Role of Empathy: Application of Eye-Tracking Tasks

The purpose of this study is to examine the relationship between individuals' moral judgment and their personality and disorder, and the role of empathy as a moderating component. The statistical population of this study includes all individuals aged 18 to 36 years and with at least a bachelor's degree. In the present study, after administering the Millon Clinical Multiaxial Inventory-III (MCMI-III) to eligible volunteers, 40 subjects (12 males and 28 females) were purposively selected into two groups, normal and personality disorders. All subjects then answered the Defining Issues Test (DIT) and the Interpersonal Reactivity Index (IRI). An eye tracking tool was also used in this study to measure empathy. Data were analyzed using descriptive statistics and a logistic regression model in SPSS software. The results showed that there was a significant relationship between personality disorder and moral judgment (p = 0.03), but empathy as a moderator was not significant in this regard. Competitive examination of two empathy measures was not significant in the present study. The results suggest that personality disorders reduce individuals' moral judgment. The present study is a first step in the application of the new eye-tracker technology in the study of psychological structures

iFocus: A Framework for Non-intrusive Assessment of Student Attention Level in Classrooms

The process of learning is not merely determined by what the instructor teaches, but also by how the student receives that information. An attentive student will naturally be more open to obtaining knowledge than a bored or frustrated student. In recent years, tools such as skin temperature measurements and body posture calculations have been developed for the purpose of determining a student\u27s affect, or emotional state of mind. However, measuring eye-gaze data is particularly noteworthy in that it can collect measurements non-intrusively, while also being relatively simple to set up and use. This paper details how data obtained from such an eye-tracker can be used to predict a student\u27s attention as a measure of affect over the course of a class. From this research, an accuracy of 77% was achieved using the Extreme Gradient Boosting technique of machine learning. The outcome indicates that eye-gaze can be indeed used as a basis for constructing a predictive model