ABSTRACT Purpose: This short paper describes the dashboard design process for online hate speech monitoring for multiple languages and platforms. Methodology/approach: A case study approach was adopted in which the authors followed a research & development project for a multilingual and multiplatform online dashboard monitoring online hate speech. The case under study is the project for the European Observatory of Online Hate (EOOH). Results: We outline the process taken for design and prototype development for which a design thinking approach was followed, including multiple potential user groups of the dashboard. The paper presents this process's outcome and the dashboard's initial use. The identified issues, such as obfuscation of the context or identity of user accounts of social media posts limiting the dashboard's usability while providing a trade-off in privacy protection, may contribute to the discourse on privacy and data protection in (big data) social media analysis for practitioners. Research limitations/implications: The results are from a single case study. Still, they may be relevant for other online hate speech detection and monitoring projects involving big data analysis and human annotation. Practical implications: The study emphasises the need to involve diverse user groups and a multidisciplinary team in developing a dashboard for online hate speech. The context in which potential online hate is disseminated and the network of accounts distributing or interacting with that hate speech seems relevant for analysis by a part of the user groups of the dashboard. International Information Management Association
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Learning in the workplace is crucial in higher engineering education, since it allows students to transfer knowledge and skills from university to professional engineering practice. Learning analytics endeavors in higher education have primarily focused on classroom-based learning. Recently, workplace learning analytics has become an emergent research area, with target users being workers, students and trainers. We propose technology for workplace learning analytics that allows program managers of higher engineering education programs to get insight into the workplace learning of their students, while ensuring privacy of students' personal data by design. Using a design-based agile methodology, we designed and developed a customizable workplace learning dashboard. From the evaluation with program managers in the computing domain, we can conclude that such technology is feasible and promising. The proposed technology was designed to be generalizable to other (engineering) domains. A next logical step would be to evaluate and improve the proposed technology within other engineering domains.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.