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  • Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis

    Gated

    Abstract: Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representations that can process and relate information from multiple modalities. In this paper, we propose two methods for unsupervised learning of joint multimodal representations using sequence to sequence (Seq2Seq) methods: a […]

  • Emotion in a 360-Degree vs. Traditional Format Through EDA, EEG and Facial Expressions

    Gated

    Abstract: Digital video advertising is growing exponentially. It is expected that digital video ad spending of the US will see double-digit growth annually through 2020 (eMarketer, 2016). Moreover, advertisers are spending on average more than $10 million annually on Digital Video, representing an 85% increase from 2 years (iab, 2016). This huge increase is mediated by […]

  • The Effects of Designers Contextual Experience on the Ideation Process and Design Outcomes

    Gated

    Abstract: Personal context-specific experience can affect how a designer evaluates a design problem and proposes solutions. However, this effect was seldom discovered in a quantitative manner in problem-solving design tasks. This paper uses empirical evidence and quantitative methods to show the effects of novice designers’ contextual experience on design tasks, particularly as it relates to […]

  • Integrating metacognitive judgments and eye movements using sequential pattern mining to understand processes underlying multimedia learning

    Gated

    Abstract: Metacomprehension is key to successful learning of complex topics when using multimedia materials. The goal of this study was to determine if eye-movement dyads could be: (1) identified by sequence mining techniques, and (2) aligned with self-reported metacognitive judgments during learning with multimedia materials that contain conceptual discrepancies designed to interfere with participants’ metacomprehension. Thirty-two […]

  • Assessment of human driver safety at Dilemma Zones with automated vehicles through a virtual reality environment

    Gated

    Abstract: Ensuring the safety of mixed traffic environments, in which human drivers interact with autonomous vehicles, is an impending challenge. A virtual traffic environment provides a risk-free opportunity to let human drivers interact with autonomous vehicles, indicating how variability in traffic environments and human responses compromises safety. Analyzing the section of road preceding an intersection […]

  • Game Scenes Evaluation and Player’s Dominant Emotion Prediction

    Gated

    Abstract: In this paper, we present a solution for computer assisted emotional analysis of game session. The proposed approach combines eye movements and facial expressions to annotate the perceived game objects with the expressed dominate emotions. Moreover, our system EMOGRAPH (Emotional Graph) gives easy access to information about user experience and predicts player’s emotions. The […]

  • How Do Different Levels of AU4 Impact Metacognitive Monitoring During Learning with Intelligent Tutoring Systems?

    Gated

    Abstract: We investigated how college students’ (n = 40) different levels of action unit 4 (AU4: brow lowerer), metacognitive monitoring process use and pre-test score were associated with metacognitive monitoring accuracy during learning with a hypermedia-based ITS. Results revealed that participants with high pre-test scores had the highest accuracy scores with low levels of AU4 and use […]

  • How Are Students’ Emotions Associated with the Accuracy of Their Note Taking and Summarizing During Learning with ITSs?

    Gated

    Abstract: The goal of this study was to examine 38 undergraduate and graduate students’ note taking and summarizing, and the relationship between emotions, the accuracy of those notes and summaries, and proportional learning gain, during learning with MetaTutor, an ITS that fosters self-regulated learning while learning complex science topics. Results revealed that students expressed both […]

  • Leverage Score Ordering

    Gated

    Abstract: In this paper we present Leverage Score Ordering, a novel technique for determining the ordering of data in the training of deep neural networks. Our technique is based on the distributed computation of leverage scores using random projections. These computed leverage scores provide a flexible and efficient method to determine the optimal ordering of training […]

  • Impact of Learner-Centered Affective Dynamics on Metacognitive Judgements and Performance in Advanced Learning Technologies

    Gated

    Abstract: Affect and metacognition play a central role in learning. We examine the relationships between students’ affective state dynamics, metacognitive judgments, and performance during learning with MetaTutorIVH, an advanced learning technology for human biology education. Student emotions were tracked using facial expression recognition embedded within MetaTutorIVH and transitions between emotions theorized to be important to […]

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