At the end of this post is an audacious idea about the present and future of software development. In the middle are points about mental models: how important and how difficult they are. But first, a…
As a result of the project, the following two tools have been developed:
SiSOB workbench: This is an analysis tool that has been designed as a knowledge worker’s workbench. Its user interface allows the user to combine different components for data conversion, analysis and visual representation. More information.
Download source code
Download user manual
Access workbench
SiSOB data extractor: This system can be used for information crawling and extraction. It can be feed with either bibliographic data sources, such as Scopus or Web of Knowledge, or crawling information directly from the web through search engines. Its main goal is to extract curricular items from a set of researchers from their full names and expertise area. More information.
Download source code
Access data extractor
SISOB Data Exchange Format:
Download API
SISOB Visualization Tool:
Download visualization tool
The project LeMo (monitoring of learning processes on personalizing and non-personalizing learning management systems) aims to develop a prototype of a web based Learning Analytics application, which provides detailed information on user navigational patterns within learning management systems and identifies needs for enhancement and revision of the learning offer. Target groups are content-provider, teacher and researcher. The prototype will support personalizing learning management systems that require a login for access as well as online encyclopedias that are non-personalizing, where neither login nor registration is needed to access content. In this project three Berlin universities cooperate with four partners in the elearning sector.
Mloss is a community effort at producing reproducible research via open source software, open access to data and results, and open standards for interchange.
Processing is an open source programming language and environment for people who want to create images, animations, and interactions. Initially developed to serve as a software sketchbook and to teach fundamentals of computer programming within a visual context, Processing also has evolved into a tool for generating finished professional work. Today, there are tens of thousands of students, artists, designers, researchers, and hobbyists who use Processing for learning, prototyping, and production.
Coderetreat is a day-long, intensive practice event, focusing on the fundamentals of software development and design. By providing developers the opportunity to take part in focused practice, away from the pressures of 'getting things done', the coderetreat format has proven itself to be a highly effective means of skill improvement. Practicing the basic principles of modular and object-oriented design, developers can improve their ability to write code that minimizes the cost of change over time.
development of self-healing systems capable of making inferences about their own behavior, such as diagnosing faults and performance degradations. uses a cost-efficient technique for adaptive diagnosis that combines probabilistic inference with online, active selection of the most-informative measurements called probes. Probes are end-to-end test transactions that collect information about the availability and performance of a distributed system. Given the probe results (symptoms), RAIL performs Bayesian inference in order to find the most likely explanation (cause), An important difference between RAIL's approach and ''passive'' data analysis is in RAIL's ability to select and execute probes online. This approach, called active probing, uses an information-theoretic criterion called information gain in order to select adaptively only a small set of the most informative probes at any given time; this approach significantly reduces the overall number of probes required
The Knowledge Discovery Machine Learning (KDML) group focuses on the neighboring subfields of computer science known as knowledge discovery in databases (KDD, sometimes referred to simply as data mining) and machine learning (ML). For us, these fields include on the one hand the automated analysis of large data sets using intelligent algorithms that are capable of extracting from the collected data hidden knowledge in order to produce models that can be used for prediction and decision making. On the other hand, they also include algorithms and systems that are capable of learning from experience and adapting to their environment or their users.
Z. Jeremic, J. Jovanovic, and D. Gasevic. Advanced Learning Technologies, 2008. ICALT '08. Eighth IEEE International Conference on, page 120 -122. (July 2008)
M. Chatti, M. Jarke, and D. Frosch-Wilke. International Journal of Knowledge and Learning 2007, 3 (4/5):
404-420(2007)Guter Übersichtsartikel zu Wissensmanagement und E-Learning.