Gaze Analysis methods for Learning Analytics

Eye-tracking had been shown to be predictive of expertise, task-based success, task-difficulty, and the strategies involved in problem solving, both in the individual and collaborative settings. In learning analytics, eye-tracking could be used as a powerful tool, not only to differentiate between the levels of expertise and task-outcome, but also to give constructive feedback to the users. In this dissertation, we show how eye-tracking could prove to be useful to understand the cognitive processes underlying dyadic interaction; in two contexts: pair program comprehension and learning with a Massive Open Online Course (MOOC). The first context is a typical collaborative work scenario, while the second is a special case of dyadic interaction namely the teacher-student pair. We also demonstrate, using one example experiment, how the findings about the relation between the learning outcome in MOOCs and the students' gaze patterns can be leveraged to design a feedback tool to improve the students' learning outcome and their attention levels while learning through a MOOC video. We also show that the gaze can also be used as a cue to resolve the teachers' verbal references in a MOOC video; and this way we can improve the learning experiences of the MOOC students. This thesis is comprised of five studies. The first study, contextualised within a collaborative setting, where the collaborating partners tried to understand the given program. In this study, we examine the relationship among the gaze patterns of the partners, their dialogues and the levels of understanding that the pair attained at the end of the task. The next four studies are contextualised within the MOOC environment. The first MOOC study explores the relationship between the students' performance and their attention level. The second MOOC study, which is a dual eye-tracking study, examines the relation between the individual and collaborative gaze patterns and their relation with the learning outcome. This study also explores the idea of activating students' knowledge, prior to receiving any learning material, and the effect of different ways to activate the students' knowledge on their gaze patterns and their learning outcomes. The third MOOC study, during which we designed a feedback tool based on the results of the first two MOOC studies, demonstrates that the variables we proposed to measure the students' attention, could be leveraged upon to provide feedback about their gaze patterns. We also show that using this feedback tool improves the students' learning outcome and their attention levels. The fourth and final MOOC study shows that augmenting a MOOC video with the teacher's gaze information helps improving the learning experiences of the students. When the teacher's gaze is displayed the perceived difficulty of the content decreases significantly as compared to the moments when there is no gaze augmentation. In a nutshell, through this dissertation, we show that the gaze can be used to understand, support and improve the dyadic interaction, in order to increase the chances of achieving a higher level of task-based success

Visual Aesthetics of E-Commerce Websites: An Eye-Tracking Approach

This study adopts four facets of visual aesthetics (i.e., simplicity, diversity, colorfulness, craftsmanship) to explain how they relate with users’ gaze patterns, based on how much they fixate on certain points, as well as how fast and how much distance their eyes cover. On a sample of 23 experienced users in online shopping, we collect eye-tracking data while looking at high, neutral, and low appealing websites, and then register their perceptions on visual aesthetics towards those websites. Findings show different patterns of gaze behavior related with users’ perceptions on visual aesthetics. Short fixations with high saccade show high simplicity, while high fixation variance and high backtrack shows high diversity. Short fixations with high backtrack show high colorfulness. Low saccade velocity with high skewness shows high craftsmanship. We contribute towards the need of automatizing the process of understanding users’ perceptions of visual aesthetics, as we might be able to predict the user behavior in real time in the future

Wearable Sensing and Quantified-self to explain Learning Experience

Author's accepted manuscript© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The confluence of wearable technologies for sensing learners and the quantified-self provides a unique opportunity to understand learners’ experience in diverse learning contexts. We use data from learners using Empatica Wristbands and self-reported questionnaire. We compute stress, arousal, engagement and emotional regulation from physiological data; and perceived performance from the self-reported data. We use Fuzzy Set Qualitative Comparative Analysis (fsQCA) to find relations between the physiological measurements and the perceived learning performance. The results show how the presence or absence of arousal, engagement, emotional regulation, and stress, as well as their combinations, can be sufficient to explain high perceived learning performanceacceptedVersio

“With-me-ness”: A gaze-measure for students’ attention in MOOCs

We propose a gaze-based indicator of students’ attention in a MOOC video lecture. We report the results from an eye-tracking study during a MOOC lecture. We define the gaze- based indicator of students’ attention as “with-me-ness”. This answers a question from teachers’ perspective “how much are the students with me?” With-me-ness is defined at two levels: perceptual, following teacher’s deictic acts- and conceptual – following teacher discourse. We conducted an experiment with 40 participants and observed a significant and positive correlation between the two levels of with-me-ness and the posttest scores

Displaying Teacher's Gaze in a MOOC: Effects on Students' Video Navigation Patterns

We present an eye-tracking study where we augment a Massive Open Online Course (MOOC) video with the gaze information of the teacher. We tracked the gaze of a teacher while he was recording the content for a MOOC lecture. Our working hypothesis is that displaying the gaze of the teacher will act as cues in crucial moments of dyadic conversation, the teacher-student dyad, such as reference disambiguation. We collected data about students' video interaction behaviour within a MOOC. The results show that the showing the teacher's gaze made the content easier to follow for the students even when complex visual stimulus present in the video lecture

Detecting Trends in Job Advertisements

We present an automatic method for trend detection in job ads. From a job-posting website, we collect job ads from 16 countries and in 8 languages and 6 job domains. We pre-process them by removing stop words, lemmatising and performing cross-domain filtering. Then, we improve the vocabulary by forming n-grams and restrict it by filtering based on named-entity and part-of-speech tags. We split the job ads to compare two time periods: the first halves of 2016 and 2017. A trending word is defined as a word with a higher TF-IDF weight in 2017 than in 2016. The results obtained show a close correlation between the position of a word in its text and its trendiness regardless of country, language or job domain

Contextualizing the co-creation of artefacts within the nested social structure of a collaborative MOOC

MOOCs have traditionally been seen as providing an individual learning experience, however there is an increasing trend towards enabling social learning in MOOCs. To make online learning at scale more social and collaborative, some MOOCs have introduced cohorts. The interaction between a smaller number of learners, within a cohort, facilitates a richer exchange of experiences and ideas as compared to the effect of “drinking from the fire hose” felt in MOOCs without cohorts. Traditionally, these cohorts have been formed randomly. In this paper, we examine the MOOC “Inquiry and Technology for Teachers”, where we formed cohorts based on student demographics relevant to our course design. Furthermore, these cohorts (which we called Special Interest Groups, SIGs) contained a nested social structure of small teams that worked together on co-creating a final artifact. The different social planes (whole course, SIGs, teams, and individuals) were linked together by pedagogical scripts that orchestrated the movement of ideas and artifacts vertically and horizontally. In this contribution, we analyzed the interaction between these social planes to contextualize the co-creation of artefacts

Dual Eye-Tracking: Lessons Learnt

Dual eye-tracking (DUET) is at the confluents of cognitive (and social) psychology and computer science. DUET is a novel methodology to explore the socio-cognitive processes underlying collaboration. The basic aims of DUET are to better understand, through gaze indicators, a socially distributed cognitive system and to support collaboration by real time gaze display of collaborators. We are interested in finding whether and how patterns of eye-movements can reflect the cognition underlying collaboration. This paper concentrates on the major motivations, methodological challenges and the future aspects of DUET

Understanding Collaborative Program Comprehension: Interlacing Gaze and Dialogues

We study the interaction of the participants in a pair program comprehension task across different time scales in a dual eye-tracking setup. We identify four layers of interaction episodes at different time scales. Each layer spans across the whole interaction. The present study concerns the relationship between different layers at different time scales. The first and third layers are based on the utterances of the participants while the second and fourth layers are based on participants' gaze