07221 Executive Summary - Information Visualization - Human-Centered Issues in Visual Representation, Interaction, and Evaluation

Information Visualization (InfoVis) focuses on the use of visualization techniques to help people understand and analyze data. While related fields such as Scientific Visualization involve the presentation of data that has some physical or geometric correspondence, Information Visualization centers on abstract information without such correspondences. One important aim of this seminar was to bring together theoreticians and practitioners from Information Visualization and related fields as well as from application areas. The seminar has allowed a critical reflection on actual research efforts, the state of field, evaluation challenges, etc. This document summarizes the event

Design and Instantiation of an Interactive Multidimensional Ontology for Game Design Elements – a Design and Behavioral Approach

While games and play are commonly perceived as leisure tools, focus on the strategic implementation of isolated gameful elements outside of games has risen in recent years under the term gamification. Given their ease of implementation and impact in competitive games, a small set of game design elements, namely points, badges, and leaderboards, initially dominated research and practice. However, these elements reflect only a small group of components that game designers use to achieve positive outcomes in their systems. Current research has shifted towards focusing on the game design process instead of the isolated implementation of single elements under the term gameful design. But the problem of a tendency toward a monocultural selection of prominent design elements persists in-game and gameful design, preventing the method from reaching its full potential. This dissertation addresses this problem by designing and developing a digital, interactive game design element ontology that scholars and practitioners can use to make more informed and inspired decisions in creating gameful solutions to their problems. The first part of this work is concerned with the collation and development of the digital ontology. First, two datasets were collated from game design and gamification literature (game design elements and playing motivations). Next, four explorative studies were conducted to add user-relevant metadata and connect their items into an ontological structure. The first two studies use card sorting to assess game theory frameworks regarding their suitability as foundational categories for the game design element dataset and to gain an overview of different viewpoints from which categorizations can be derived. The second set of studies builds on an explorative method of matching dataset entries via their descriptive keywords to arrive at a connected graph. The first of these studies connects items of the playing motivations dataset with themselves, while the second connects them with an additional dataset of human needs. The first part closes with the documentation of the design and development of the tool Kubun, reporting on the outcome of its evaluation via iterative expert interviews and a field study. The results suggest that the tool serves its preset goals of affording intuitive browsing for dedicated searches and serendipitous findings. While the first part of this work reports on the top-down development process of the ontology and related navigation tool, the second part presents an in-depth research of specific learning-oriented game design elements to complement the overall research goal through a complementary bottom-up approach. Therein, two studies on learning-oriented game design elements are reported regarding their effect on performance, long-term learning outcome, and knowledge transfer. The studies are conducted with a game dedicated to teaching correct waste sorting. The first study focuses on a reward-based game design element in terms of its motivatory effect on perfect play. The second study evaluates two learning-enhancing game design elements, repeat, and look-up, in terms of their contribution to a long-term learning outcome. The comprehensive insights gained through the in-depth research manifest in the design of a module dedicated to reporting research outcomes in the ontology. The dissertation concludes with a discussion on the studies’ varying limitations and an outlook on pathways for future research

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data