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Auflistung nach Schlagwort "Prediction"

1 - 4 von 4
  • Konferenzbeitrag
    Reasoning about Causal Effects of Regulation and Legislation on Interconnected Markets
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Legat, Christoph; Seebacher, Uwe; Brunner, Dominik
    Regulatory interventions have far-reaching implications beyond the directly affected market, creating dependencies and interconnections among various markets. Understanding the causal effects and dependencies between different markets arising from regulatory actions is crucial for policymakers, industry stakeholders, and researchers. This paper presents a novel causal effect model specifically designed to reason about the impact of regulation and legislation, with a specific focus on the dependencies between different markets beyond the one directly affected by regulation. The proposed model provides a comprehensive framework for analyzing the consequences of regulatory interventions across interconnected markets. By employing advanced causal inference techniques, the model enables the identification and quantification of the causal effects that emerge between markets due to regulatory actions.
  • Konferenzbeitrag
    A refined case-based reasoning approach to academic capacity planning
    (6th Conference on Professional Knowledge Management – From Knowledge to Action, 2011) Poeppelmann, Daniel
    Academic capacity planning is a knowledge-intensive process that has to be based upon predicted course demand. Planners have to take into account students' current course achievements, prospective future course selections, time constraints as well as a wide range of different rules for graduation. The research project proposes a refined case-based reasoning (CBR) approach for anticipating students' future course selection as a means of long-term demand forecasting. The case-base is dynamically interpreted with regard to stored cases' problem descriptions and solutions. Moreover the structure of cases is heterogeneous depending on the students' course achievements. The retain phase of the traditional case-based reasoning cycle is replaced by an adjustment phase that ensures retaining up-to-date, real-world cases only. The results of the case-based reasoning processes are aggregated to support capacity planning.
  • Konferenzbeitrag
    Supporting Informed Negotiation Processes in Group Recommender Systems
    (i-com: Vol. 14, No. 1, 2015) Gross, Tom
    Group recommender systems make suggestions to groups of users who want to share experiences or products. Despite their high potential for helping users, GRS face diverse challenges that can be clustered into two groups: predictions and processes. Generating predictions of the goodness of the fit of recommendations to the group has been seen as a core challenge of recommender systems from their beginning, while supporting the processes of discussion for reaching consensus on the item to pick is a more recent challenge. In this paper I report on a base platform for GRS with powerful algorithms for generating and explaining recommendations with high predictions, and an easy and effective process model for GRS.
  • Konferenzbeitrag
    Using data to improve programming instruction
    (DeLFI 2018 - Die 16. E-Learning Fachtagung Informatik, 2018) Öztürk, Alisan; Bonfert-Taylor, Petra; Fügenschuh, Armin
    Programming classes are difficult by nature and educators are eager to find ways to deal with high dropout rates. Today’s technologies allow us to capture programming-related student data, which can be used to identify students in need of assistance and in getting insights in student learning. In order to assist novice programming students in learning how to program, we developed a web-based programming environment, which is used by students throughout the whole course. While it also provides students with enhanced error messages, all data of students’ interactions are captured. Through this data, we identified two metrics, related to small programming assignments, which highly correlate with student performance. These metrics along other features further enabled us to implement machine learning algorithms that could accurately predict dropout-prone students, early on in the course. Overall, methods of educational data mining can be utilized to assist both, students and educators in introductory programming courses.