Rethinking Pedagogical Use of Eye Trackers for Visual Problems with Eye Gaze Interpretation Tasks

Eye tracking technology enables the visualisation of a problem solver's eye movement while working on a problem. The eye movement of experts has been used to draw attention to expert problem solving processes in a bid to teach procedural skills to learners. Such affordances appear as eye movement modelling examples (EMME) in the literature. This work intends to further this line of work by suggesting how eye gaze data can not only guide attention but also scaffold learning through constructive engagement with the problem solving process of another human. Inferring the models’ problem solving process, be it that of an expert or novice, from their eye gaze display would require a learner to make interpretations that are rooted in the knowledge elements relevant to such problem solving. Such tasks, if designed properly, are expected to probe or foster a deeper understanding of a topic as their solutions would require not only following the expert gaze to learn a particular skill, but also interpreting the solution process as evident from the gaze pattern of an expert or even of a novice. This position paper presents a case for such tasks, which we call eye gaze interpretation (EGI) tasks. We start with the theoretical background of these tasks, followed by a conceptual example and representation to elucidate the concept of EGI tasks. Thereafter, we discuss design considerations and pedagogical affordances, using a domain-specific (chemistry) spectral graph problem. Finally, we explore the possibilities and constraints of EGI tasks in various fields that require visual representations for problem solving

Fixation duration and the learning process: an eye tracking study with subtitled videos

Learning is a complex phenomenon and education researchers are increasingly focussing on processes that go into it. Eye tracking has become an important tool in such research. In this paper, we focus on one of the most commonly used metrics in eye tracking, namely, fixation duration. Fixation duration has been used to study cognition and attention. However, fixation duration distributions are characteristically non-normal and heavily skewed to the right. Therefore, the use of a single average value, such as the mean fixation duration, to predict cognition and/or attention could be problematic. This is especially true in studies of complex constructs, such as learning, which are governed by both cognitive and affective processes. We collected eye tracking data from 51 students watching a 12 min long educational video with and without subtitles. The learning gain after watching the video was calculated with pre- and post-test scores. Several multiple linear regression models revealed a) fixation duration can explain a substantial fraction of variation in the pre-post data, which indicates its usefulness in the study of learning processes; b) the arithmetic mean of fixation durations, which is the most commonly reported eye tracking metric, may not be the optimal choice; and c) a phenomenological model of fixation durations where the number of fixations over different temporal ranges are used as inputs seemed to perform the best. The results and their implications for learning process research are discussed

Experimental studies on remanence acquisition processes and regional geomagnetic field variability from archeointensity studies

The dissertation comprises two separate topics. Chapters 2 and 3 are experimental studies on remanence acquisition processes. Chapters 4 and 5 investigate the geomagnetic field variability in Africa and India between 1000 BCE and 1000 CE. Chapter 2 is a study in which the role of flocculation in sedimentary magnetization is analyzed with the help of laboratory redeposition experiments and a simple numerical model. At small floc sizes DRM acquisition is likely to be non-linear but it may record the directions with higher fidelity. In environments having bigger flocs the sediments are likely to record either intensities or directions with high fidelity, but not both. Also flocculation may inhibit a large fraction of magnetic grains from contributing to the net remanence and this might have consequences for intensity normalization in sediments. Chapter 3 presents a fresh perspective on the long standing debate of the nature of magnetocrystalline anisotropy in Mid-Ocean Ridge Basalts (MORBs). A new parameter, IRAT, defined as the ratio of the isothermal remanences in antiparallel directions is used to differentiate between uniaxial single domain grains (IRAT ̃1) and multiaxial single domain grains (IRAT <1). The theoretical predictions were first validated with standard samples and then multiple MORB samples were analyzed. The observed IRAT ratios indicate a dominant non -uniaxial anisotropy in the MORBs. Chapters 4 and 5 are archeointensity studies from two data poor regions of the world viz., Africa and India. With stringent data selection criteria and well established archeological constraints these datasets provide important constraints on the field intensity from 1000 BCE to 1000 CE in Africa and 500 BCE to 1000 CE in India. The African dataset has a higher age resolution than the Indian dataset. The African dataset matches well with the global CALS3k.4 model and shows significant non-axial-dipolar contribution in the region. The Indian dataset is not of a similar resolution but shows that the field might have dropped by as much as 40% in the first half of the first century BCE and remained low during the first century C

Foraminifera optical microscope images with labelled species and segmentation labels

The dataset contains foraminifera images of over 1,000 forams taken under 16 different lighting directions with an optical microscope. The species and locations of the samples are also specified. It also contains manual segmentation of over 400 samples from the images described above. The segmentation labels are matched by their name. To capture these images, a visual identification system was developed in order to automate the identification of target microorganisms. The visual system incorporates a controllable LED lighting ring used to capture images by illuminating the specimens from several directions, mimicking an important step in the traditional identification process. The dataset was originally used for foraminifera identification and segmentation with machine learning and computer vision techniques. This work is a collaboration between the Dr. Edgar Lobaton (Associate Professor at the North Carolina State University), Dr. Thomas Marchitto (Associate Professor at the University of Colorado Boulder) and Dr. Ritayan Mitra (Assistant Professor at IIT Bombay). Please refer to https://research.ece.ncsu.edu/aros/foram-identification/ for more information about the datasets, related studies and downloading the dataset