5,808 research outputs found
Algorithms as scores: coding live music
The author discusses live coding as a new path in the evolution of the musical score. Live-coding practice accentu- ates the score, and whilst it is the perfect vehicle for the performance of algorithmic music it also transforms the compositional process itself into a live event. As a continuation of 20th-century artistic developments of the musical score, live-coding systems often embrace graphical elements and language syntaxes foreign to standard programming languages. The author presents live coding as a highly technologized artistic practice, shedding light on how non-linearity, play and generativity will become prominent in future creative media productions
Software cost estimation
The paper gives an overview of the state of the art of software cost estimation (SCE). The main questions to be answered in the paper are: (1) What are the reasons for overruns of budgets and planned durations? (2) What are the prerequisites for estimating? (3) How can software development effort be estimated? (4) What can software project management expect from SCE models, how accurate are estimations which are made using these kind of models, and what are the pros and cons of cost estimation models
Does OO sync with the way we think?
Given that corrective-maintenance costs already dominate the software life cycle and look set to increase significantly, reliability in the form of reducing such costs should be the most important software improvement goal. Yet the results are not promising when we review recent corrective-maintenance data for big systems in general and for OO in particular-possibly because of mismatches between the OO paradigm and how we think
Spartan Daily, October 3, 2005
Volume 125, Issue 21https://scholarworks.sjsu.edu/spartandaily/10164/thumbnail.jp
An overview of decision table literature 1982-1995.
This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.
Some aspects of grading Java code submissions in MOOCs
Recently, massive open online courses (MOOCs) have been offering a new online approach in the field of distance learning and online education. A typical MOOC course consists of video lectures, reading material and easily accessible tests for students. For a computer programming course, it is important to provide interactive, dynamic, online coding exercises and more complex programming assignments for learners. It is expedient for the students to receive prompt feedback on their coding submissions. Although MOOC automated programme evaluation subsystem is capable of assessing source programme files that are in learning management systems, in MOOC systems there is a grader that is responsible for evaluating students’ assignments with the result that course staff would be required to assess thousands of programmes submitted by the participants of the course without the benefit of an automatic grader. This paper presents a new concept for grading programming submissions of students and improved techniques based on the Java unit testing framework that enables automatic grading of code chunks. Some examples are also given such as the creation of unique exercises by dynamically generating the parameters of the assignment in a MOOC programming course combined with the kind of coding style recognition to teach coding standards
Data mining for exploring E-learning in a computer science course using online judging
[SPA] En el Espacio Europeo de Enseñanza Superior emergen nuevas metodologías de enseñanza basadas en el proceso de aprendizaje de los estudiantes, que promueven el interés de los estudiantes y ofrecen retroalimentación personalizada. Los sistemas de enjuiciamiento en red son métodos prometedores para estimular la participación de los estudiantes en el proceso de aprendizaje. La enorme cantidad de datos disponible en un sistema de enjuiciamiento en red ofrece la posibilidad de explorar qué parámetros son relevantes para el aprendizaje de la programación de computadores. En este artículo, se identifican los factores que afectan a la corrección de los programas a partir de las actividades de programación en un curso de algoritmos y estructuras de datos. Se utilizan tecnologías de minería de datos como los árboles de decisión, que han demostrado ser muy efectivos como predictores en algunos dominios de aprendizaje electrónico. Los resultados muestran que los parámetros Lenguaje de programación, Número de problema y Titulación pueden ser utilizados como predictores de la corrección de un programa, con una precisión del 60,1%. Como trabajo futuro, pretendemos estudiar los factores que afectan al rendimiento de los trabajadores en un entorno de desarrollo global del software, aplicando la minería de datos a actividades de programación colaborativas. [ENG] New teaching methods based on the students' learning process are being developed in the European Higher Education Area. Most of them are oriented to promote students' interest in the study and offer personalized feedback. On-line judging is a promising method for encouraging students’ participation in the elearning process. The great amount of data available in an on-line judging tool provides the possibility of exploring some of the most indicative attributes for learning programming concepts and techniques. In this paper, the results of programming activities carried out in a course on “Algorithms and Data Structures” has been used to identify the factors that affect the program correction, by using powerful data mining technologies taken from artificial intelligence domain. Concretely, our study uses a decision tree because it has been identified as the best predictor in some elearning domains. An overall accuracy of 60.1% in the prediction of the program correction was achieved with three input parameters (Programming language, Number of problem and Degree). In future work, we aim to analyze collaborative activities in order to identify the factors or predictor variables that affect workers’ performance in a global software development contextThis research is part of the PEGASO-PANGEA projects (TIN2009-13718-C02-02) financed by the Spanish Ministry of Science and Innovation (Spain), and the GEODAS project (TIN2012-37493-C03) financed by both the Spanish Ministry of Economy and Competitiveness and European FEDER funds. This work was also supported by the Spanish MINECO, as well as European Commission FEDER funds, under grant TIN2012-38341-C04-03
Advances in Teaching & Learning Day Abstracts 2005
Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2005
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