Stephan, Matthew
http://hdl.handle.net/2374.MIA/6167
Dr. Matthew Stephan - Assistant Professor, Computer Science & Software Engineering2024-03-29T11:43:18ZSupplemental Material for study on using multiple monitors in introduction level programming courses
http://hdl.handle.net/2374.MIA/6612
Supplemental Material for study on using multiple monitors in introduction level programming courses
Stephan, Matthew
This entry contains our supplemental material, including Data and its analysis, for our study investigating multiple monitors as an intervention in early programming educational laboratories.
Supplemental Material for Emerging Trends in Collaborative Modelling: A Survey
http://hdl.handle.net/2374.MIA/6283
Supplemental Material for Emerging Trends in Collaborative Modelling: A Survey
Stephan, Matthew
This entry contains the supplemental material and data for our paper Emerging Trends in Collaborative Modelling: A Survey. Paper abstract:
Just as in other engineering disciplines, software engineering is well suited to collaboration; Having different perspectives and diverse experiences strengthens engineering projects. Software modeling is a fundamental aspect of software engineering and is becoming increasingly collaborative. Collaborative modeling approaches are maturing and related research is growing significantly. While surveys exist on collaborative modeling tools and research, they are aimed at academics and can be verbose. In this article, we conduct a research survey intended to provide practitioners and researchers an accessible and abstract at-a-glance perspective of emerging trends and directions in collaborative modeling. We complete a systematic literature review, which we crosscheck with existing surveys. To explicate trends in the last five complete years and overall trends, we perform concept extraction and domain analysis by analyzing abstracts. We visualize these trends in word clouds and trend charts, and provide insights. We hope this article helps spread awareness of collaborative modeling trends and future directions, and educates practitioners and researchers.
Research Artifacts for Machine Learning DSML Baseball Case Study
http://hdl.handle.net/2374.MIA/6234
Research Artifacts for Machine Learning DSML Baseball Case Study
Koseler, Kaan; Stephan, Matthew
This entry contains all the artifacts created and data utilized in our research on developing a Machine Learning DSML, models, and software for a Baseball Case Study.
Towards the Realization of a DSML for Machine Learning: A Baseball Analytics Use Case
http://hdl.handle.net/2374.MIA/6224
Towards the Realization of a DSML for Machine Learning: A Baseball Analytics Use Case
Koseler, Kaan; Stephan, Matthew
Using machine learning (ML) for big data is challenging, requiring specialized knowledge of the domain, learning algorithms, and software engineering. To demonstrate the viability of model-driven engineering in the ML domain we consider an ML use case of baseball analytics by extending and applying an existing, but untested, ML domain specific modeling language (DSML). Additionally, we aim to make ML software development more accessible and formalized, and help facilitate future research in this area. This paper describes our plan, initial work, and anticipated contributions in extending, testing, and validating this DSML, and implementing a code generation scheme that is targeted at a binary classification baseball problem.
Keywords: Model driven engineering * Domain specific modeling languages * Machine Learning * Analytics * Baseball