EG2021

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https://diglib.eg.org/handle/10.2312/2633001

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Browsing EG2021 by Subject "Computing education"

Now showing 1 - 3 of 3
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    Conveying Firsthand Experience: The Circuit Parcours Technique for Efficient and Engaging Teaching in Courses about Virtual Reality and Augmented Reality
    (The Eurographics Association, 2021) Dörner, Ralf; Horst, Robin; Sousa Santos, Beatriz and Domik, Gitta
    Providing the opportunity for hands-on experience is crucial when teaching courses about Virtual Reality (VR) and Augmented Reality (AR). However, the workload on the educator's side for providing these opportunities might be prohibitive. In addition, other organizational challenges can arise, for example, demonstrations of VR/AR application in a course might be too time-consuming, especially if the course is attended by many students. We present the Circuit Parcours Technique to meet these challenges. Here, in a well-organized event, stations with VR/AR demonstrations are provided in parallel, and students are enlisted to prepare and conduct the demonstrations. The event is embedded in a four-phase model. In this education paper, the technique is precisely described, examples for its flexible usage in different teaching situations are provided, advantages such as time efficiency are discussed, and lessons learned are shared from our experience with using this method for more than 10 years. Moreover, learning goals are identified that can be achieved with this technique besides gaining personal experience.
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    Project in Visualization and Data Analysis: Experiences in Designing and Coordinating the Course
    (The Eurographics Association, 2021) Kucher, Kostiantyn; Martins, Rafael M.; Kerren, Andreas; Sousa Santos, Beatriz and Domik, Gitta
    Visual analytics involves both visual and computational components for empowering human analysts who face the challenges of making sense and making use of large and heterogeneous data sets in various application domains. In order to facilitate the learning process for the students at higher education institutions with regard to both the theoretical knowledge and practical skills in visual analytics, the respective courses must cover a variety of topics and include multiple assessment methods and activities. In this paper, we report on the design and first instantiation of a full term project-based course in visualization and data analysis, which was recently offered to graduate and post-graduate students at our department and met with positive feedback from the course participants.
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    Teaching Data-driven Video Processing via Crowdsourced Data Collection
    (The Eurographics Association, 2021) Reimann, Max; Wegen, Ole; Pasewaldt, Sebastian; Semmo, Amir; Döllner, Jürgen; Trapp, Matthias; Sousa Santos, Beatriz and Domik, Gitta
    This paper presents the concept and experience of teaching an undergraduate course on data-driven image and video processing. When designing visual effects that make use of Machine Learning (ML) models for image-based analysis or processing, the availability of training data typically represents a key limitation when it comes to feasibility and effect quality. The goal of our course is to enable students to implement new kinds of visual effects by acquiring training datasets via crowdsourcing that are used to train ML models as part of a video processing pipeline. First, we propose our course structure and best practices that are involved with crowdsourced data acquisitions. We then discuss the key insights we gathered from an exceptional undergraduate seminar project that tackles the challenging domain of video annotation and learning. In particular, we focus on how to practically develop annotation tools and collect high-quality datasets using Amazon Mechanical Turk (MTurk) in the budget- and time-constrained classroom environment. We observe that implementing the full acquisition and learning pipeline is entirely feasible for a seminar project, imparts hands-on problem solving skills, and promotes undergraduate research.