Kshitij Sharma

Studying Teacher Cognitive Load in Multi-tabletop Classrooms Using Mobile Eye-tracking

Increasing affordability is making multi-tabletop spaces (e.g., in school classrooms) a real possibility, and first design guidelines for such environments are starting to appear. However, there are still very few studies into the usability of such multi-tabletop classrooms as a whole, considering well-established constructs such as cognitive load. In this poster we present an exploratory study of the usage of mobile eye-tracking techniques to follow cognitive load of a teacher during a lesson in such a multi-tabletop space. By analyzing several eye-tracking measures over three sessions of a collaborative learning lesson on fractions, we obtained insights on the user experience of the facilitator in these concrete sessions. We also show the potential of eye-tracking to identify critical episodes in the usage of a multi-tabletop space under realistic usage conditions, in a less intrusive and more objective manner.

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.

How to Quantify Student's Regularity?

Studies carried out in classroom-based learning context, have consistently shown a positive relation between students’ conscientiousness and their academic success. We hypothesize that time management and regularity are main constructing blocks of students’ conscientiousness in the context of online education. In online education, despite intuitive arguments supporting on-demand courses as more flexible delivery of knowledge, completion rate is higher in the courses with rigid temporal constraints and structure. In this study, we further investigate how students’ regularity affects their learning outcome in MOOCs. We propose several measures to quantify students regularity. We validate accuracy of these measures as predictors of students’ performance in the course.

Using Mobile Eye-Trackers to Unpack the Perceptual Benefits of a Tangible User Interface for Collaborative Learning

In this study, we investigated the way users memorize, analyze, collaborate, and learn new concepts on a Tangible User Interface (TUI). Twenty-seven pairs of apprentices in logistics (N = 54) interacted with an interactive simulation of a warehouse. Their task was to discover efficient design principles for building storehouses. In a between-subjects experimental design, half of the participants used 3D physical shelves, whereas the other half used 2D paper shelves. This manipulation allowed us to control for the “representational effect” of 3D tangibles: the first group saw the warehouse as a small-scale model with realistic shelves, whereas the second group had access to a more abstract layout with rectangular pieces of paper. Both groups interacted with the system in the same way. We found that participants in the first group (i.e., who used 3D realistic shelves) better memorized a warehouse layout, built a more efficient model, and scored higher on a learning test. Additionally, students wore eye-tracking goggles while completing those tasks; preliminary results suggest that 3D interfaces increased joint visual attention, which was found to be a significant predictor for participants’ task performance and learning gains. Implications for designing TUIs in collaborative settings are discussed.

Iterative Design of an Upper Limb Rehabilitation Game with Tangible Robots

Rehabilitation aims to ameliorate deficits in motor control via intensive practice with the affected limb. Current strategies, such as one-on-one therapy done in rehabilitation centers, have limitations such as treatment frequency and intensity, cost and requirement of mobility. Thus, a promising strategy is home-based therapy that includes task specific exercises. However, traditional rehabilitation tasks may frustrate the patient due to their repetitive nature and may result in lack of motivation and poor rehabilitation. In this article, we propose the design and verification of an effective upper extremity rehabilitation game with a tangible robotic platform named Cellulo as a novel solution to these issues. We first describe the process of determining the design rationales to tune speed, accuracy and challenge. Then we detail our iterative participatory design process and test sessions conducted with the help of stroke, brachial plexus and cerebral palsy patients (18 in total) and 7 therapists in 4 different therapy centers. We present the initial quantitative results, which support several aspects of our design rationales and conclude with our future study plans.

Looking THROUGH versus Looking AT: A Strong Concept in Technology Enhanced Learning

When watching an educational video, our eyes look for relevant information related to the topic that is being explained at that particular moment. Studying the learners’ gaze behavior and particularly how it correlates with their performance, we have found a series of results, which converge to an understanding about learner behavior that is more abstracted than the use situation or the studied learning contexts. In this contribution we present “Looking Through vs. Looking At” as a generative intermediate-level body of knowledge, and show how it can construct a Strong Concept (as developed by Hook [10]) in technology enhanced learning (TEL). “Looking At”, simply put, refers to missing the relevant information because of either looking at the incorrect place or lagging behind the teacher in time. “Looking Through”, on the other hand, is the success in finding the relevant displayed information at the right moment such that the communication, through verbal and visual channels, becomes synchronous. The visual medium becomes transparent and the learning experience shifts from interacting with the material to interacting with the teacher. We define formally and show how to quantify the proposed strong concept in dyadic interaction scenarios. This concept is applicable to MOOC video interaction, but also to other learning scenarios such as (collaborative) problem solving. We put a particular emphasis on the generative aspect of the concept and demonstrate, with examples, how it can help designing solutions for interactive learning situations.

Using Sequential Pattern Mining to Explore Learners’ Behaviors and Evaluate Their Correlation with Performance in Inquiry-Based Learning

This study analyzes students’ behaviors in a remote laboratory environment in order to identify new factors of prediction of academic success. It investigates relations between learners’ activities during practical sessions, and their performance at the final assessment test. Based on learning analytics applied to data collected from an experimentation conducted with our remote lab dedicated to computer education, we discover recurrent sequential patterns of actions that lead us to the definition of learning strategies as indicators of higher level of abstraction. Results show that some of the strategies are correlated to learners’ performance. For instance, the construction of a complex action step by step, or the reflection before submitting an action, are two strategies applied more often by learners of a higher level of performance than by other students. While our proposals are domain-independent and can thus apply to other learning contexts, the results of this study led us to instrument for both students and instructors new visualization and guiding tools in our remote lab environment.

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.

The General Data Protection Regulation: An Opportunity for the HCI Community?

With HCI, researchers conduct studies in interdisciplinary projects involving massive volume of data, artificial intelligence and machine learning capabilities. Awareness of the responsibility is emerging as a key concern for the HCI community. This Community will be impacted by the General Data Protection Regulation (GDPR) [5], that will enter into force on the 25th of May 2018. From that date, each data controller and data processor will face an increase of its legal obligations (in particular its accountability) under certain conditions. The GDPR encourages the adoption of Soft Law mechanisms, approved by the national competent authority on data protection, to demonstrate the compliance to the Regulation. Approved Guidelines, Codes of Conducts, Labeling, Marks and Seals dedicated to data protection, as well as certification mechanisms are some of the options proposed by the GDPR. There may be discrepancies between the realities of HCI fieldwork and the formal process of obtaining Soft Law approval by Competent Authorities dedicated to data protection. Given these issues, it is important for researchers to reflect on legal and ethical encounters in HCI research as a community. This workshop will provide a forum for researchers to share experiences about Soft Law they have put in place to increase Trust, Transparency and Accountability among the shareholders. These discussions will be used to develop a white paper of practical Soft Law mechanisms (certification, labeling, marks, seals...) emerging in HCI research with the aim to demonstrate that the GDPR may be an opportunity for the HCI community.

Exploring Causality Within Collaborative Problem Solving Using Eye-Tracking.

When students are working collaboratively and communicating verbally in a technology enhanced environment, the system is not aware of what collaboration is happening outside of the technology, making it difficult to adapt the system to better support the collaboration of the students. In this paper, we analyze the causal relationships between collaborative and individual gaze measures and the influence that the students dialogue, prior knowledge, or success has on these relationships to find indicators that can be used within an adaptive system. We found that when students are discussing concrete aspects of the problem, the causal relationship between their eye gaze measures changes compared to other types of dialogue patterns. The results also show a clear difference in causal relations when the pairs with high prior knowledge or success are compared with the pairs with low prior knowledge or success. Collaborative gaze causes the individual gaze for pairs with high prior knowledge and the opposite for the pairs with low prior knowledge.