Moscow, 3 Kochnovsky Proezd
Phone: +7 (495) 772-95-90*22668
Dmitry I. Ignatov
Larisa Ivanovna Antropova
The School of Data Analysis and Artificial Intelligence was created in 2014 as part of the Department of Data Analysis and Artificial Intelligence. The school consists of world-renowned researchers who actively participate in international research projects in the fields of data mining, formal concept analysis, semantic technologies and ontology engineering, multi-modal clustering, machine learning, natural language processing, development of intelligent and recommender systems, social network analysis, and medical informatics.
The following companies were the sponsors of the event: Dassault Systems, Airbus Group, Total, GE Oil&Gas, MathWorks, international accrediting agency ABET, universities and centre specialized in engineering education.
The 18th International Conference on Interactive Collaborative Learning was held as part of the WEEF-2015. The School staff members presented the report ‘Blended Learning in Software Engineering Education: The Application Lifecycle Management Experience with Computer-Supported Collaborative Learning’.
Software engineering education (SEE) process simulates the main professional software lifecycle processes such as analysis, design, construction and maintenance (see SWEBoK, ITIL, etc.). The necessity of meeting both educational needs and requirements from industry explains that using Supported Collaborative Learning (CSCL) techniques in software engineering (SE) should be based on professional tools or on similar to them. The main purpose of this work is to fill the gap between the SEE needs and the current trends in CSCL development. We generalize world experience and suggest the framework of using industry approved methods and tools. We compare CSCL tools and the other collaborative services; analyze the teaching experience of several SE courses supported by different collaborative methods and collaborative web-services. Special attention is paid to formative feedback implementation. Following achieved result we suppose that using best practices from SE will enrich CSCL methodology and tools not only for SE field, but also for other areas of knowledge.
Presentation of the report