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Auflistung nach Autor:in "Toreini, Peyman"

1 - 4 von 4
  • Zeitschriftenartikel
    Cognitive state detection with eye tracking in the field: an experience sampling study and its lessons learned
    (i-com: Vol. 23, No. 1, 2024) Langner, Moritz; Toreini, Peyman; Maedche, Alexander
    In the future, cognitive activity will be tracked in the same way how physical activity is tracked today. Eye-tracking technology is a promising off-body technology that provides access to relevant data for cognitive activity tracking. For building cognitive state models, continuous and longitudinal collection of eye-tracking and self-reported cognitive state label data is critical. In a field study with 11 students, we use experience sampling and our data collection system esmLoop to collect both cognitive state labels and eye-tracking data. We report descriptive results of the field study and develop supervised machine learning models for the detection of two eye-based cognitive states: cognitive load and flow. In addition, we articulate the lessons learned encountered during data collection and cognitive state model development to address the challenges of building generalizable and robust user models in the future. With this study, we contribute knowledge to bring eye-based cognitive state detection closer to real-world applications.
  • Konferenzbeitrag
    Designing Gaze-Aware Attention Feedback for Learning in Mixed Reality
    (Mensch und Computer 2022 - Tagungsband, 2022) Liu, Shi; Toreini, Peyman; Maedche, Alexander
    Mixed Reality (MR) has demonstrated its potential in the application field of education. In particular, in contrast to traditional learning, students using MR get the possibility of learning and exploring the content in a self-directed way. Meanwhile, research in learning technology has revealed the significance of supporting learning activities with feedback. Since such feedback is often missing in MR-based learning environments, we propose a solution of using eye-tracking in MR to provide gaze-aware attention feedback to students and evaluate it with potential users in a preliminary user study.
  • Konferenzbeitrag
    An Immersive Learning Factory for AI & Data Literacy: An Exploratory Study in the Wild
    (Mensch und Computer 2023 - Tagungsband, 2023) Liu, Shi; Schulz, Thimo; Toreini, Peyman; Maedche, Alexander
    Artificial Intelligence (AI) has already made a strong impact on business and private life. Nonetheless, understanding how AI works and which role data plays in this context still remains unclear for many people. We argue that students with non-technical backgrounds should build up AI & data literacy to understand the key concepts of AI & data and leverage its potential in their field of study and research. For this purpose, we present the concept of an immersive learning factory, where students can explore AI & data concepts with interactive and immersive technologies. In this paper, we demonstrate our overarching idea, as well as the results of our exploratory evaluation with industrial engineering & management students from a data science lecture. Our main contribution includes the conceptual framework of the immersive learning factory, as well as design guidelines for creating immersive learning experiences concluded from the evaluation.
  • Konferenzbeitrag
    Interest-based Recommendation in Academic Networks using Social Network Analysis
    (DeLFI 2016 -- Die 14. E-Learning Fachtagung Informatik, 2016) Toreini, Peyman; Chatti, Mohamed Amine; Thues, Hendrik; Schroeder, Ulrik