About

Colin J. Neill is a Professor of Software and Systems Engineering at Penn State Great Valley, where he also serves as Chancellor, Dean, and Chief Academic Officer. He earned his Ph.D. in software and systems engineering, an M.Sc. in communication systems, and a B.Eng. in electrical engineering from the University of Wales, Swansea, United Kingdom. His teaching encompasses a wide range of courses in system design and architecture, project management, and systems thinking. Prior to his tenure at Penn State, Dr. Neill worked on time and mission critical system modeling and design, as well as manufacturing systems and production management, with organizations including the University of Wales, Swansea, Oxford University, the Rover Car Company, and British Aerospace. He has authored over 80 articles focusing on the development and evolution of complex software and systems, along with their management and governance. Dr. Neill is a Senior Member of the IEEE, a member of INCOSE, and serves as associate editor-in-chief of Innovations in Systems and Software Engineering. His research and professional contributions are recognized through awards such as the 2009 Teaching Excellence Award, the 2006 Distinguished Service Award, and the 2005 Distinguished Research Award at Penn State Great Valley.

Research topics

• Artificial Intelligence
• Computer Science
• Knowledge management
• Human–computer interaction
• Psychology
• Mathematics
• Mathematics education

Selected publications

• Implementing Peer tutoring in an online course

  2025-01-17 · 1 citations

  articleOpen access1st authorCorresponding

  Previous research in a large scale experiment provided no evidence that working on a successful and effective team had a positive effect on individual student performance.Thus, to facilitate individual learning, we implemented peer tutoring while students worked on an effective team in an online graduate software engineering course.This paper presents an online peer tutoring design.The results of a constant comparative qualitative analysis will be presented in order to provide insight into the success of this peer tutoring implementation.I.

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• Improving Individual Learning in Software Engineering Team Projects

  2020-09-04 · 2 citations

  articleOpen accessSenior author

  The focus of our research is on determining the factors that facilitate both team success and individual learning during team-oriented project-based learning. Of particular interest is the efficacy of collaborative learning approaches in general for individual engineering students. Our results from a large scale experiment provide no evidence that working on a successful and effective team affects individual exam performance. Thus, we will propose a qualitative study to determine the best ways to structure team work to enhance individual leaning.

  PublisherOA PDFDOI

• Improving Team Learning in Systems Design

  2020-09-04 · 2 citations

  articleOpen access1st authorCorresponding

  A detailed statistical experiment to study the effect of the cognitive collaborative model (CCM) on learning has been designed. The subjects collaboratively solved an analysis and design problem in a graduate engineering course. In previous experiments, we showed the benefits of the CCM in improving engineering team performance and investigated the mechanisms that facilitate this improvement.

  PublisherOA PDFDOI

• Improving Learning Outcomes Using Cognitive Models In Systems Design

  2020 · 3 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  System design courses typically incorporate team projects as both active learning components of courses and for student assessment. Research indicates, however, that actually working within a team generates a new set of problems, referred to as Problem B: managing the diversity of the problem solvers in contrast to Problem A: solving the actual problem the team is working on. Given the presence of Problem B, there is a risk that student learning will actually suffer because of the team. To mitigate this risk, we propose the use of the Cognitive Collaborative Model (CCM) in team system design exercises.

  PublisherOA PDFDOI

• Improving Team Performance: The Cognitive Style Factor

  2020 · 3 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  It is widely considered that success in the design and development of an engineering system is contingent upon the team having a shared vision of the problem they are solving.The goal of this research is to determine which factors improve the performance of an engineering team.One of the aspects explored is the effectiveness of arranging teams based upon each team member's cognitive problem solving style preference using the Adaption-Innovation framework 1 .This paper presents a complete experiment evaluating concept map data from the design stage of engineering, graduate student, teams.

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• A Study of Individual Learning in Software Engineering Team Projects

  2020-09-11 · 1 citations

  article1st authorCorresponding

  Abstract A Study of Individual Learning in Software Engineering Team ProjectsA large scale experiment to determine if improved team cognition leads to improved individuallearning has been designed. Specifically, the goal of this research is to determine if working onan effective team benefits or impedes a student’s learning of the course content. The literatureappears to focus on team performance, team outcomes, and benefits of teams by combiningindividual resources; but does not focus on the benefits of the individuals on the team, where abenefit could be learning for example.Clearly, an individual’s cognitive activities when on a team are influenced both positively andnegatively by social factors. This could be due to the cognitive diversity of the individual teammembers not being managed or the many social factors that may influence an individual’scognitive processes. Social factors may include social loafing (doing less work because youbelieve others on the team will be doing it) or social facilitation (the mere presence of others caneither enhance or impede individual performance [1]. But again, these factors appear to bebenefiting or impeding the team not the individual.Previously, we studied the effect of the cognitive collaborative model (CCM) on facilitating teamcognition, and the degree of team cognition that is needed to improve team outcomes. Wedetermined the CCM in fact increases team performance via mental model convergence [2] andreduces the effects of cognitive diversity [3]. To further extend this research, in this experiment,we studied how improving team cognition effects individual learning.The CCM is a six-stage cognitive model that takes into consideration the cognitive and socialactivities that occur during collaborative problem solving by facilitating problem formulation,solution planning, and system design tasks during collaboration. The CCM model prescribestactics to ensure collaboration. Using pre and post testing, we studied course outcomes ofsoftware engineering graduate students learning software systems design that have also utilizedthe CCM in a systems design project. We hypothesized that the students who utilized the CCMfor their system design project will have a significantly higher course outcome. 1. J.M. Levine, L.V. Resnick, and E.T. Higgins. (1993) Social foundations of cognition. Annual Review of Psychology, Vol. 44, January 1993, pp. 585-612. 2. DeFranco, J.F., Neill, C.J., Clariana, R.B. (2011). A Cognitive Collaborative Model To Improve Performance in Engineering Teams – A Study of Team Outcomes and Mental Model Sharing, Systems Engineering Journal, 14 (3). 3. DeFranco, J.F., Neill, C.J., “Problem-Solving Style and its Impact on Engineering Team Effectiveness”, CSER (Conference on Systems Engineering Research) 2011.

  PublisherDOI

• Twitter Data for Predicting Election Results: Insights from Emotion Classification

  IEEE Technology and Society Magazine · 2019-03-01 · 12 citations

  article

  The advent of social media and microblogging sites has paved the way for individuals and communities to freely express their opinions, feelings, and thoughts on a variety of topics in the form of short and limited size texts such as tweets. These tweets can hold a wealth of information on how individuals communicate their thoughts, emotions (happiness, anxiety, depression, etc.) and feelings within their social network [1].

  PublisherDOI

• Ranking Critical Activities in Complex System Development Projects

  IEEE Systems Journal · 2019-05-17 · 2 citations

  articleSenior author

  This paper proposes a discrete-time Markov chain-based ranking algorithm to rank critical activities in development processes for complex systems. Critical activities are those that lead to greater rework, involve close coordination or interdependence between development units, or produce task volatility. For example, the product development processes of the Ford Motor Company have several activities that are sensitive to information variability which can lead to higher task volatility or higher probability of rework. It is important to identify such activities early to ensure that the requirements for these activities are met and risk mitigation efforts can be planned. Analysis of the results from a variety of process architectures across a range of systems engineering domains shows that the proposed algorithm is more effective in identifying these critical activities than existing network ranking algorithms.

  PublisherDOI

• Using Dependency Structure Models for Ranking Critical Components in Product Architectures

  2019-09-25

  articleOpen access

  Dependency structure models, or their equivalent dependency network models, can be analyzed for identifying critical components of a product architecture – elements within the architecture that, if changed, can have a significant impact on the rest of the system. In this paper we propose a discrete-time Markov chain-based algorithm to analyze dependency structure models of several product architectures to not only identify but also rank such components in order of their criticality. Identifying and ranking these elements allows prudent allocation of resources on a project to mitigate any risks that result from changes made to an architecture as a product evolves. The results show that the proposed algorithm is more effective when compared to other algorithms, namely betweenness centrality, closeness centrality and eigenvalue centrality, that have been used on dependency network models to identify and rank critical components.

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• Power of Predictive Analytics: Using Emotion Classification of Twitter Data for Predicting 2016 US Presidential Elections

  2019-05-31 · 5 citations

  articleOpen access

  Predictive analytics using the twitter feeds is becoming a popular field for research. A tweet holds a wealth of information on how an individual expresses and communicates their feelings and emotions within their social network. Large-scale collection, cleaning, and mining of tweets will not only help in capturing an individual’s emotion but also the emotions of a larger group. However, capturing a large volume of tweets and identifying the emotions expressed in it is a challenging task. Different classification algorithms employed in the past for classifying emotions have resulted in low-to-moderate accuracies thus making it difficult to precisely predict the outcome of an event. Secondly, the presence of diverse emotion annotated datasets, none of which are specific to a particular domain, has limited the potentiality of supervised algorithms for classification purposes. In this study, we demonstrate the potentiality of a lexicon-based classifier, NRC , which can mine emotions and sentiments in tweets. Using the NRC classifier, we initially determined the emotions and the sentiments within the tweets and used that to predict the swing direction of the 19 US states towards the candidates of the 2016 US presidential election. Comparing the predictions from the NRC against with the actual outcome of the election, we observed a ~90% accuracy, a performance superior to the mainstream pollsters indicating the potential emotion and sentiment-based classification holds in predicting the outcome of significant social and political events.

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Frequent coauthors

• Joanna F. DeFranco

  31 shared

• Raghvinder S. Sangwan

  Carnegie Mellon University

  31 shared

• Phillip A. Laplante

  Hydro-Québec

  24 shared

• Nil Kilicay‐Ergin

  10 shared

• Satish Srinivasan

  Pennsylvania State University

  5 shared

• Robert Ludwig

  Friedrich-Alexander-Universität Erlangen-Nürnberg

  3 shared

• Philip A. Laplante

  Pennsylvania State University

  3 shared

• A. N. Bowers

  2 shared

Awards & honors

• 2009 Teaching Excellence Award, Penn State Great Valley Scho…
• 2006 Distinguished Service Award, Penn State Great Valley Sc…
• 2005 Distinguished Research Award, Penn State Great Valley S…