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Institute
- Institut für Informatik und Computational Science (1112)
- Hasso-Plattner-Institut für Digital Engineering gGmbH (19)
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The field of machine learning studies algorithms that infer predictive models from data. Predictive models are applicable for many practical tasks such as spam filtering, face and handwritten digit recognition, and personalized product recommendation. In general, they are used to predict a target label for a given data instance. In order to make an informed decision about the deployment of a predictive model, it is crucial to know the model’s approximate performance. To evaluate performance, a set of labeled test instances is required that is drawn from the distribution the model will be exposed to at application time. In many practical scenarios, unlabeled test instances are readily available, but the process of labeling them can be a time- and cost-intensive task and may involve a human expert. This thesis addresses the problem of evaluating a given predictive model accurately with minimal labeling effort. We study an active model evaluation process that selects certain instances of the data according to an instrumental sampling distribution and queries their labels. We derive sampling distributions that minimize estimation error with respect to different performance measures such as error rate, mean squared error, and F-measures. An analysis of the distribution that governs the estimator leads to confidence intervals, which indicate how precise the error estimation is. Labeling costs may vary across different instances depending on certain characteristics of the data. For instance, documents differ in their length, comprehensibility, and technical requirements; these attributes affect the time a human labeler needs to judge relevance or to assign topics. To address this, the sampling distribution is extended to incorporate instance-specific costs. We empirically study conditions under which the active evaluation processes are more accurate than a standard estimate that draws equally many instances from the test distribution. We also address the problem of comparing the risks of two predictive models. The standard approach would be to draw instances according to the test distribution, label the selected instances, and apply statistical tests to identify significant differences. Drawing instances according to an instrumental distribution affects the power of a statistical test. We derive a sampling procedure that maximizes test power when used to select instances, and thereby minimizes the likelihood of choosing the inferior model. Furthermore, we investigate the task of comparing several alternative models; the objective of an evaluation could be to rank the models according to the risk that they incur or to identify the model with lowest risk. An experimental study shows that the active procedure leads to higher test power than the standard test in many application domains. Finally, we study the problem of evaluating the performance of ranking functions, which are used for example for web search. In practice, ranking performance is estimated by applying a given ranking model to a representative set of test queries and manually assessing the relevance of all retrieved items for each query. We apply the concepts of active evaluation and active comparison to ranking functions and derive optimal sampling distributions for the commonly used performance measures Discounted Cumulative Gain and Expected Reciprocal Rank. Experiments on web search engine data illustrate significant reductions in labeling costs.
This talk will describe My Digital Life (TU100), a distance learning module that introduces computer science through immediate engagement with ubiquitous computing (ubicomp). This talk will describe some of the principles and concepts we have adopted for this modern computing introduction: the idea of the ‘informed digital citizen’; engagement through narrative; playful pedagogy; making the power of ubicomp available to novices; setting technical skills in real contexts. It will also trace how the pedagogy is informed by experiences and research in Computer Science education.
A survey has been carried out in the Computer Science (CS) department at the University of Baghdad to investigate the attitudes of CS students in a female dominant environment, showing the differences between male and female students in different academic years. We also compare the attitudes of the freshman students of two different cultures (University of Baghdad, Iraq, and the University of Potsdam).
Durch den bundesweiten Rückgang der Schülerzahlen und einer steigenden Zahl von Bildungsangeboten geraten Universitäten und Hochschulen in den nächsten Jahren weiter in eine Wettbewerbssituation, weshalb sie effektive Marketingmaßnahmen entwickeln müssen, um Schülerinnen und Schüler möglichst frühzeitig für das jeweilige Angebot (z. B. Informatik- und informatiknahe Studiengänge) zu interessieren. Ein Medium, über das sich potenziell sehr viele Jugendliche erreichen lassen, sind dabei soziale Netzwerke. Diese Arbeit präsentiert Ergebnisse einer Studie unter Informatikstudienanfängerinnen und -anfängern zum Nutzungsverhalten sozialer Netzwerke und zieht Schlussfolgerungen zu deren Eignung als Werbe- und Informationskanal für die Zielgruppe der Informatikinteressierten.
Viele Hochschulen nutzen SAP ERP in der Lehre, um den Studierenden einen Einblick in die Funktionsweise und den Aufbau von integrierter Standardsoftware zu ermöglichen. Im Rahmen solcher Schulungen bilden die Studierenden eine Meinung und Bewertung der Software. In diesem Artikel wird untersucht, wie sich klassische Modelle der Nutzungswahrnehmung auf die spezielle Situation von SAP ERP in der Lehre übertragen lassen und welchen Einfluss bestimmte Faktoren haben. Dazu wurden vier Vorher-Nachher-Studien durchgeführt. Die Ergebnisse zeigen, dass die Funktionalität im Laufe der Schulung positiver und die Benutzungsfreundlichkeit als negativer bewertet wird.
In einigen Bereichen der Informatiklehre ist es möglich, die persönlichen Erfahrungen der Studierenden im Umgang mit Informationstechnik aufzugreifen und vor dem Hintergrund theoretischer Konzepte aus der Literatur gemeinsam mit ihnen zu reflektieren. Das hier vorgestellte Lehrkonzept des Reflexionsdialogs erstreckt sich über drei Seminartermine und vorbereitende Selbstlernphasen. Unterstützt wird das Konzept durch DialogueMaps, eine Software zur Visualisierung komplexer Sachverhalte und zur Unterstützung interaktiver Dialoge. Dieser Beitrag beschreibt die Hintergründe des Lehrkonzeptes, das Lehrkonzept selbst sowie die inhaltliche Ausgestaltung im Rahmen eines Mastermoduls „Computergestützte Kooperation“.
In diesem Beitrag berichten wir über die Erfahrungen einer umgestalteten Lehre im Bereich Informatik und Gesellschft (IuG). Die Gründe für die Umge staltung und die Konzeption werden skizziert. Die Erfahrungen haben wir zu Thesen verdichtet: 1. Informatik und Gesellschaft sollte eine Pflichtveranstaltung im Bachelor-Studium sein, in der Studierende einen Überblick erhalten, welche gesellschaftlichen Rahmenbedingungen für sie relevant sind und wie man diese in die Praxis mit einbeziehen kann. 2. Historische Inhalte der Informatik sollen hier aufgearbeitet werden, indem man aktuelle Entwicklungen im Kontext ihrer Genese betrachtet.
Wir stellen die Konzeption und erste Ergebnisse einer neuartigen Informatik- Lehrveranstaltung für Studierende der Geodäsie vor. Das Konzept verbindet drei didaktische Ideen: Kontextorientierung, Peer-Tutoring und Praxisbezug (Course). Die Studierenden sollen dabei in zwei Semestern wichtige Grundlagen der Informatik verstehen und anzuwenden lernen. Durch enge Verzahnung der Aufgaben mit einem für Nichtinformatiker relevanten Kontext, sowie einem sehr hohen Anteil von Selbsttätigkeit der Studierenden soll die Motivation für fachfremde Themen gesteigert werden. Die Ergebnisse zeigen, dass die Veranstaltung sehr erfolgreich war.