Musical algorithms and data structures in programming instruction

Diese Diplomarbeit präsentiert einen unkonventionellen Zugang zur Didaktik der Informatik - Musik und musikalische Strukturen und deren Anwendung im Programmierunterricht. Nach der kritischen Betrachtung einiger Lerntheorien und deren Relevanz im Informatikunterricht werden diverse Methoden präsentiert, die für den Informatikunterricht von besonderer Bedeutung sind. Die Diplomarbeit beruht auf der Hypothese, dass sich Datenstrukturen und Algorithmen, essentielle Konzepte der Programmierung, durch Musik bzw. musikalische Strukturen darstellen lassen. Zu diesem Zweck wird auch ein Unterrichtskurs sowie ein begleitendes Arbeitsheft erstellt, sowohl für höhere Schulen als auch für Universitätskurse. Weiters wird die Hypothese in einem praktischen Unterrichtsprojekt mit Studenten auf die Probe gestellt. Hierbei stellt sich heraus, dass tatsächlich musikalische Strukturen hervorragend geeignet sind, fundamentale Konzepte des Programmierens zu erklären.This thesis presents an unconventional approach to didactics of computer science - music and musical structures applied in programming instruction. After looking at several learning theories with regard to their relevance for teaching computer science, a few methods vital for good CS teaching are presented. The thesis' central hypothesis is that essential concepts for programming such as algorithms and data structures can be taught by using music as a starting point and musical structures as models for abstract data structures. For this purpose a workbook for students is created and a teaching sequence is proposed that implements the hypothesis for high school or university teaching courses. Finally an evaluation carried out with a sample group of university students tests the hypothesis in practice

On a Comprehensive Metadata Framework for Artificial Data in Unsupervised Learning

Evaluating new methods and algorithms in unsupervised learning obviously requires thorough benchmarking studies on data sets that most closely reflect performance in actual usage. Designing data sets that do exactly that is quite a challenging task in itself; standing up to the challenge in comparison to other methods is another point which poses a risk of compromising the goal of an objective benchmarking study. We want to address the latter by proposing a framework that standardizes the format of artificial data, or rather its metadata. We intend to introduce a web repository that functions as an exchange for metadata of artificial data and an accompanying R package that can generate actual data from the descriptions obtained from the repository. It is therefore much simpler to find data designed by others and which has been used in previous benchmarking studies. This removes some of the temptation to specifically design artificial data in a way so that a proposed method performs significantly better than existing ones, a claim that might not hold in real life applications

Benchmarking in cluster analysis: A white paper

To achieve scientific progress in terms of building a cumulative body of knowledge, careful attention to benchmarking is of the utmost importance. This means that proposals of new methods of data pre-processing, new data-analytic techniques, and new methods of output post-processing, should be extensively and carefully compared with existing alternatives, and that existing methods should be subjected to neutral comparison studies. To date, benchmarking and recommendations for benchmarking have been frequently seen in the context of supervised learning. Unfortunately, there has been a dearth of guidelines for benchmarking in an unsupervised setting, with the area of clustering as an important subdomain. To address this problem, discussion is given to the theoretical conceptual underpinnings of benchmarking in the field of cluster analysis by means of simulated as well as empirical data. Subsequently, the practicalities of how to address benchmarking questions in clustering are dealt with, and foundational recommendations are made