Stephan, Matthew
stephamd
Recent Submissions
Item Supplemental Material for study on using multiple monitors in introduction level programming courses
Stephan, MatthewThis entry contains our supplemental material, including Data and its analysis, for our study investigating multiple monitors as an intervention in early programming educational laboratories.Item Supplemental Material for Emerging Trends in Collaborative Modelling: A Survey
Stephan, MatthewThis 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.Item Research Artifacts for Machine Learning DSML Baseball Case Study
Koseler, Kaan; Stephan, MatthewThis 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.Item Towards the Realization of a DSML for Machine Learning: A Baseball Analytics Use Case
Koseler, Kaan; Stephan, MatthewUsing 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 * BaseballItem A Survey of Baseball Machine Learning: A Technical Report
Koseler, Kaan; Stephan, MatthewStatistical analysis of baseball has long been popular, albeit only in limited capacity until relatively recently. The recent proliferation of computers has added tremendous power and opportunity to this field. Even an amateur baseball fan can perform types of analyses that were unimaginable decades ago. In particular, analysts can easily apply machine learning algorithms to large baseball data sets to derive meaningful and novel insights into player and team performance. These algorithms fall mostly under three problem class umbrellas: Regression, Binary Classification, and multiclass classification. Professional teams have made extensive use of these algorithms, funding analytics departments within their own organizations and creating a multi-million dollar thriving industry. In the interest of stimulating new research and for the purpose of serving as a go-to resource for academic and industrial analysts, we have performed a systematic literature review of machine learning algorithms and approaches that have been applied to baseball analytics. We also provide our in- sights on possible future applications. We categorize all the approaches we encountered during our survey, and summarize our findings in two tables. We find two algorithms dominated the literature, 1) Support Vector Machines for classification problems and 2) Bayesian Inference for both classification and Regression problems. These algorithms are often implemented manually, but can also be easily utilized by employing existing software, such as WEKA or the Scikit-learn Python library. We speculate that the current popularity of neural networks in general machine learning literature will soon carry over into baseball analytics, although we found relatively fewer existing articles utilizing this approach when compiling this report.