Emily M. Hastings, Ph.D.

Emily M. Hastings, Ph.D.

Assistant Professor of Computer Science at the University of Wisconsin-Eau Claire (she/her)

Publications

You can also find my articles on my Google Scholar profile. See my Kudos page for non-technical descriptions of some of my papers.

Conferences and Journals

Composing Team Compositions: An Examination of Instructors’ Current Algorithmic Team Formation Practices

Published in Proceedings of the ACM on Human-Computer Interaction - CSCW, 2023

Instructors using algorithmic team formation tools must decide which criteria (e.g., skills, demographics, etc.) to use to group students into teams based on their teamwork goals, and have many possible sources from which to draw these configurations (e.g., the literature, other faculty, their students, etc.). However, tools offer considerable flexibility and selecting ineffective configurations can lead to teams that do not collaborate successfully. Due to such tools' relative novelty, there is currently little knowledge of how instructors choose which of these sources to utilize, how they relate different criteria to their goals for the planned teamwork, or how they determine if their configuration or the generated teams are successful. To close this gap, we conducted a survey (N=77) and interview (N=21) study of instructors using CATME Team-Maker and other criteria-based processes to investigate instructors' goals and decisions when using team formation tools. The results showed that instructors prioritized students learning to work with diverse teammates and performed "sanity checks" on their formation approach's output to ensure that the generated teams would support this goal, especially focusing on criteria like gender and race. However, they sometimes struggled to relate their educational goals to specific settings in the tool. In general, they also did not solicit any input from students when configuring the tool, despite acknowledging that this information might be useful. By opening the "black box" of the algorithm to students, more learner-centered approaches to forming teams could therefore be a promising way to provide more support to instructors configuring algorithmic tools while at the same time supporting student agency and learning about teamwork.

Recommended citation: Emily M. Hastings, Vidushi Ojha, Benedict V. Austriaco, Karrie Karahalios, and Brian P. Bailey. 2023. Composing Team Compositions: An Examination of Instructors' Current Algorithmic Team Formation Practices. Proc. ACM Hum.-Comput. Interact. 7, CSCW2, Article 305 (October 2023), 24 pages. https://doi.org/10.1145/3610096

Access online here. Download here.

A Learner-Centered Technique for Collectively Configuring Inputs for an Algorithmic Team Formation Tool

Published in Proceedings of the 53rd ACM Technical Symposium on Computer Science Education, 2022

The configuration that an instructor enters into an algorithmic team formation tool determines how students are grouped into teams, impacting their learning experiences. One way to decide the configuration is to solicit input from the students. Prior work has investigated the criteria students prefer for team formation, but has not studied how students prioritize the criteria or to what degree students agree with each other. This paper describes a workflow for gathering student preferences for how to weight the criteria entered into a team formation tool, and presents the results of a study in which the workflow was implemented in four semesters of the same project-based design course. In the most recent semester, the workflow was supplemented with an online peer discussion to learn about students' rationale for their selections. Our results show that students want to be grouped with other students who share the same course commitment and compatible schedules the most. Students prioritize demographic attributes next, and then task skills such as programming needed for the project work. We found these outcomes to be consistent in each instance of the course. Instructors can use our results to guide team formation in their own project-based design courses and replicate our workflow to gather student preferences for team formation in any course.

Recommended citation: Emily M. Hastings, Sneha R. Krishna Kumaran, Karrie Karahalios, and Brian P. Bailey. 2022. A Learner-Centered Technique for Collectively Configuring Inputs for an Algorithmic Team Formation Tool. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2022). Association for Computing Machinery, New York, NY, USA, 969-975. DOI:https://doi.org/10.1145/3478431.3499331.

Access online here. Download here.

A Data-Driven Framework for Team Formation for Maintenance Tasks

Published in International Journal of Prognostics and Health Management, 2021

Even as maintenance evolves with new technologies, it is still a heavily human-driven domain; multiple steps in the maintenance workflow still require human expertise and intervention. Various maintenance activities require multiple maintainers, all with different skill sets and expertise, and from various positions and levels within the organization. Responding to maintenance requests, training exercises, or executing larger maintenance projects all can require maintenance teams. Having the correct assortment of individuals both in terms of skills and management experience can help improve the efficiency of these maintenance tasks. This paper presents a workflow for creating teams of maintainers by adapting accepted practices from the human-computer interaction (HCI) community. These steps provide a low-cost solution to help account for the needs of maintainers and their management, while matching skills of the maintainers with the needs of the activity.

Recommended citation: Reslan, M., Hastings, E., Brundage, M. P., & Sexton, T. (2021). A Data-Driven Framework for Team Formation for Maintenance Tasks. IJPHM, 12, 003.

Access online here. Download here.

LIFT: Integrating Stakeholder Voices into Algorithmic Team Formation

Published in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020

Team formation tools assume instructors should configure the criteria for creating teams, precluding students from participating in a process affecting their learning experience. We propose LIFT, a novel learner-centered workflow where students propose, vote for, and weigh the criteria used as inputs to the team formation algorithm. We conducted an experiment (N=289) comparing LIFT to the usual instructor-led process, and interviewed participants to evaluate their perceptions of LIFT and its outcomes. Learners proposed novel criteria not included in existing algorithmic tools, such as organizational style. They avoided criteria like gender and GPA that instructors frequently select, and preferred those promoting efficient collaboration. LIFT led to team outcomes comparable to those achieved by the instructor-led approach, and teams valued having control of the team formation process. We provide instructors and designers with a workflow and evidence supporting giving learners control of the algorithmic process used for grouping them into teams.

Recommended citation: Emily M. Hastings, Albatool Alamri, Andrew Kuznetsov, Christine Pisarczyk, Karrie Karahalios, Darko Marinov, and Brian P. Bailey. 2020. LIFT: Integrating Stakeholder Voices into Algorithmic Team Formation. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). Association for Computing Machinery, New York, NY, USA, 1-13. https://doi.org/10.1145/3313831.3376797

Access online here. Download here.

Agreement Behavior of Isolated Annotators for Maintenance Work-Order Data Mining

Published in Proceedings of the Annual Conference of the PHM Society 2019, 2019

Maintenance work orders (MWOs) are an integral part of the maintenance workflow. These documents allow technicians to capture vital aspects of a maintenance job, including observed symptoms, potential causes, and solutions implemented. MWOs have often been disregarded during analysis because of the unstructured nature of the text they contain. However, research efforts have recently emerged that clean MWOs for analysis. One such approach is a tagging method which relies on experts classifying and annotating the words used in the MWOs. This method greatly reduces the volume of words used in the MWOs and links words, including misspellings, that have the same or similar meanings. However, one issue with this approach and with the practical usage of data-annotation tools on the shop-floor more generally is the usage of only one expert annotator at a time. How do we know that the classifications of a single annotator are correct, or if it is, for example, feasible to divide the tagging task among multiple experts? This paper examines the agreement behavior of multiple isolated experts classifying and annotating MWO data, and provides implications for implementing this tagging technique in authentic contexts. The results described here will help improve MWO classification leading to more accurate analysis of MWOs for decision-making support.

Recommended citation: Hastings, E., Sexton, T., Brundage, M. P., & Hodkiewicz, M. (2019). Agreement Behavior of Isolated Annotators for Maintenance Work-Order Data Mining. Proceedings of the Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.791

Access online here. Download here.

Structure or Nurture? The Effects of Team-Building Activities and Team Composition on Team Outcomes

Published in Proceedings of the ACM on Human-Computer Interaction - CSCW, 2018

How can instructors group students into teams that interact and learn effectively together? One strand of research advocates for grouping students into teams with "good" compositions such as skill diversity. Another strand argues for deploying team-building activities to foster interpersonal relations like psychological safety. Our work synthesizes these two strands of research. We describe an experiment (N=249) that compares how team composition vs. team-building activities affect student team outcomes. In two university courses, we composed student teams either randomly or using a criteria-based team formation tool. Teams further performed team-building activities that promoted either team or task outcomes. We collected project scores, and used surveys to measure psychological safety, perceived performance, and team satisfaction. Surprisingly, the criteria-based teams did not statistically differ from the random teams on any of the measures taken, despite having compositions that better satisfied the criteria defined by the instructor. Our findings argue that, for instructors deploying a team formation tool, creating an expectation among team members that their team can perform well is as important as tuning the criteria in the tool. We also found that student teams reported high levels of psychological safety, but these levels appeared to develop organically and were not affected by the activities or compositional strategies tested. We distill these and other findings into implications for the design and deployment of team formation tools for learning environments.

Recommended citation: Emily M. Hastings, Farnaz Jahanbakhsh, Karrie Karahalios, Darko Marinov, and Brian P. Bailey. 2018. Structure or Nurture? The Effects of Team-Building Activities and Team Composition on Team Outcomes. Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 68 (November 2018), 21 pages. https://doi.org/10.1145/3274337

Access online here. Download here.

Comparing Global Link Arrangements for Dragonfly Networks

Published in 2015 IEEE International Conference on Cluster Computing, 2015

High-performance computing systems are shifting away from traditional interconnect topologies to exploit new technologies and to reduce interconnect power consumption. The Dragonfly topology is one promising candidate for new systems, with several variations already in production. It is hierarchical, with local links forming groups and global links joining the groups. At each level, the interconnect is a clique, with a link between each pair of switches in a group and a link between each pair of groups. This paper shows that the intergroup links can be made in meaningfully different ways. We evaluate three previously-proposed approaches for link organization (called global link arrangements) in two ways. First, we use bisection bandwidth, an important and commonly-used measure of the potential for communication bottlenecks. We show that the global link arrangements often give bisection bandwidths differing by 10s of percent, with the specific separation varying based on the relative bandwidths of local and global links. For the link bandwidths used in a current Dragonfly implementation, it is 33%. Second, we show that the choice of global link arrangement can greatly impact the regularity of task mappings for nearest neighbor stencil communication patterns, an important pattern in scientific applications.

Recommended citation: E. Hastings, D. Rincon-Cruz, M. Spehlmann, S. Meyers, A. Xu, D. P. Bunde, and V. J. Leung, Comparing global link arrangements for dragonfly networks, in 2015 IEEE International Conference on Cluster Computing, Sept 2015, pp. 361-370.

Access online here.

Other Peer-Reviewed Publications

Introduction to Computer Science

Published in Common Ground Research Networks Learning Design and Leadership Modules, 2017

This learning module is intended to be an introduction to computer science suitable for K-12 students, along the same lines as the Girls Who Code program, or a summer College for Kids" course, though it would be equally applicable to beginning undergraduate students in their first computer science course. It provides an overview of what computer science is and what computer scientists do, an introduction to four key concepts of CS (loops, variables, conditionals, and functions), and suggestions for next steps students can take to continue their journey learning CS."

Recommended citation: Hastings, Emily. (2017, December 9). Introduction to Computer Science. Common Ground Research Networks Learning Design and Leadership Modules. Retrieved from https://cgscholar.com/bookstore/works/introduction-to-computer-science?category_id=364.

Access online here.

Learning Theory Exegesis: Collaborative Learning

Published in CGScholar EPSY 408 SU17 Community, 2017

Teamwork has become increasingly popular in recent years, with more and more workplaces expecting their employees to work collaboratively with each other. In response, organizations like the Accreditation Board for Engineering and Technology have increasingly required educational institutions to incorporate teamwork into their curricula, and a collaborative approach to learning has grown in popularity (Ruiz Ulloa & Adams, 2004). Like many students, I have always been a little wary of group work, having had bad experiences in the past. I have, however, had positive ones as well; there are a number of benefits to working collaboratively, including a shared (and so perhaps reduced) workload, exposure to different viewpoints, and the ability to take on larger projects. I am, therefore, interested in studying the theory behind collaborative learning, as I hope to one day help my own students experience the positive aspects of group learning while avoiding the negative ones. This topic is also closely related to my current projects for my Ph.D., a point to which I will return later.

Recommended citation: Hastings, Emily. (2017, July 17). Learning Theory Exegesis: Collaborative Learning. CGScholar EPSY 408 SU17 Community. Retrieved from https://cgscholar.com/community/profiles/user-28138/publications/143052.

Access online here.