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1. An Exploration of the Effects of Enterprise Social Media Use for Classroom Teams.

   Cherchiglia, L. ; Van Osch, W. ; Liang, Y. ; Averkiadi, E. ( December 2020 , SIGHCI 2020 Proceedings)

   null (Ed.)

   Cherchiglia et al. Effects of ESM Use for Classroom Teams Proceedings of the Nineteenth Annual Pre-ICIS Workshop on HCI Research in MIS, Virtual Conference, December 12, 2020 1 An Exploration of the Effects of Enterprise Social Media Use for Classroom Teams Leticia Cherchiglia Michigan State University leticia@msu.edu Wietske Van Osch HEC Montreal & Michigan State University wietske.van-osch@hec.ca Yuyang Liang Michigan State University liangyuy@msu.edu Elisavet Averkiadi Michigan State University averkiad@msu.edu ABSTRACT This paper explores the adoption of Microsoft Teams, a group-based Enterprise Social Media (ESM) tool, in the context of a hybrid Information Technology Management undergraduate course from a large midwestern university. With the primary goal of providing insights into the use and design of tools for group-based educational settings, we constructed a model to reflect our expectations that core ESM affordances would enhance students’ perceptions of Microsoft Teams’ functionality and efficiency, which in turn would increase both students’ perceptions of group productivity and students’ actual usage of Microsoft Teams for communication purposes. In our model we used three core ESM affordances from Treem and Leonardi (2013), namely editability (i.e., information can be created and/or edited after creation, usually in a collaborative fashion), persistence (i.e., information is stored permanently), and visibility (i.e., information is visible to other users). Analysis of quantitative (surveys, server-side; N=62) and qualitative (interviews; N=7) data led to intriguing results. It seems that although students considered that editability, persistency, and visibility affordances within Microsoft Teams were convenient functions of this ESM, problems when working collaboratively (such as connectivity, formatting, and searching glitches) might have prevented considerations of this ESM as fast and user-friendly (i.e., efficient). Moreover, although perceived functionality and efficiency were positively connected to group productivity, hidden/non-intuitive communication features within this ESM might help explain the surprising negative connection between efficiency and usage of this ESM for the purpose of group communication. Another explanation is that, given the plethora of competing tools specifically designed to afford seamless/optimal team communication, students preferred to use more familiar tools or tools perceived as more efficient for group communication than Microsoft Teams, a finding consistent with findings in organizational settings (Van Osch, Steinfield, and Balogh, 2015). Beyond theoretical contributions related to the impact that ESM affordances have on users’ interaction perceptions, and the impact of users’ interaction perceptions on team and system outcomes, from a strategic and practical point of view, our findings revealed several challenges for the use of Microsoft Teams (and perhaps ESM at large) in educational settings: 1) As the demand for online education grows, collaborative tools such as Microsoft Teams should strive to provide seamless experiences for multiple-user access to files and messages; 2) Microsoft Teams should improve its visual design in order to increase ease of use, user familiarity, and intuitiveness; 3) Microsoft Teams appears to have a high-learning curve, partially related to the fact that some features are hidden or take extra steps/clicks to be accessed, thus undermining their use; 4) Team communication is a complex topic which should be further studied because, given the choice, students will fall upon familiar tools therefore undermining the full potential for team collaboration through the ESM. We expect that this paper can provide insights for educators faced with the choice for an ESM tool best-suited for group-based classroom settings, as well as designers interested in adapting ESMs to educational contexts, which is a promising avenue for market expansion. 

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2. Interpersonal Interactions that Foster Inclusion: Building Supports for Diversity in Engineering Teams

   Rodríguez-Simmonds, Héctor E. ; Jackson, Benjamin P. ; Pearson, Nelson S. ; Langus, Tara C. ; Major, Justin C. ; Kirn, Adam ; Godwin, Allison ( January 2018 , ASEE annual conference & exposition)

   Teaming is a core part of engineering education, especially in the first and last years of engineering when project work is a prevalent focus. The literature on the effects of working in diverse teams is mixed. Negative findings include decreased affect, increased frustration, and sustained conflict in teams. Positive findings include increased productivity, production of high quality products, and divergent-thinking and idea generation. Given these mixed findings, it becomes important to not only understand the practical outputs of working in diverse teams, but also how the experience of working in diverse teams influences whether students see themselves as engineers and whether or not they feel they belong in engineering. Our project, Building Supports for Diversity through Engineering Teams, investigates how students’ attitudes towards diversity influence how students experience work in diverse teams through addressing two main research questions: 1) What changes occur in students’ diversity sensitivity, multicultural effectiveness, and engineering practices as a result of working in diverse teams? 2) How do students’ perceptions of diversity, affect, and engineering practices change because of working on diverse teams? Using a multi-method approach, we deployed survey instruments to determine changes in student’s attitudes about teaming, diversity sensitivity, and openness attitudes. We also observed students working in teams and interviewed these students about their perceptions of diversity and experiences in their teams. Preliminary results of the quantitative phase show that variance in students’ attitudes about diversity significantly increase over the semester, further reflecting the mixed results that have been seen previously in the literature. Additionally, Social Network Analysis was used to characterize the social structure practices of a multi-section, large-enrollment first-year engineering course. This reveals the underlying social structure of the environment, its inclusiveness, and how diverse students work with others on engineering. Initial results indicate that students are included in social networks regardless of gender and race. Preliminary results of the qualitative phase, using Interpretive Phenomenological Analysis, have yielded relationships between student’s definitions, valuation, and enactment of diversity in engineering spaces. Individual student’s incoming attitudes of diversity and previous experiences interact with practical needs in first-year engineering classrooms to create different microclimates within each team. These microclimates depict tensions between what instructors emphasize about diversity, stereotypes of engineering as focused on technical instead of social skills, and pragmatic forces of “getting the job done.” This knowledge can help explain some of the complexity behind the conflicting literature on diversity in teams. Ultimately, this research can help us understand how to build inclusive and diverse environments that guide students to learn how to understand their own complex relationship, understanding, and enactment of diversity in engineering. By understanding how students make sense of diversity in engineering spaces, educators and researchers can figure out how to introduce these concepts in relevant ways so that students can inclusively meet the grand challenges in engineering. This curriculum integration, in turn, can improve team interactions and the climate of engineering for underrepresented groups. 

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3. AURORA: Auditing PageRank on Large Graphs

   https://doi.org/10.1109/BigData.2018.8622563  

   Kang, Jian ; Wang, Meijia ; Cao, Nan ; Xia, Yinglong ; Fan, Wei ; Tong, Hanghang ( December 2018 , 2018 IEEE International Conference on Big Data (Big Data))

   Ranking on large-scale graphs plays a fundamental role in many high-impact application domains, ranging from information retrieval, recommender systems, sports team management, biology to neuroscience and many more. PageRank, together with many of its random walk based variants, has become one of the most well-known and widely used algorithms, due to its mathematical elegance and the superior performance across a variety of application domains. Important as it might be, state-of-the-art lacks an intuitive way to explain the ranking results by PageRank (or its variants), e.g., why it thinks the returned top-k webpages are the most important ones in the entire graph; why it gives a higher rank to actor John than actor Smith in terms of their relevance w.r.t. a particular movie? In order to answer these questions, this paper proposes a paradigm shift for PageRank, from identifying which nodes are most important to understanding why the ranking algorithm gives a particular ranking result. We formally define the PageRank auditing problem, whose central idea is to identify a set of key graph elements (e.g., edges, nodes, subgraphs) with the highest influence on the ranking results. We formulate it as an opti-mization problem and propose a family of effective and scalable algorithms (Aurora) to solve it. Our algorithms measure the influence of graph elements and incrementally select influential elements w.r.t. their gradients over the ranking results. We perform extensive empirical evaluations on real-world datasets, which demonstrate that the proposed methods (Aurora) provide intuitive explanations with a linear scalability. 

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4. TIP: A Trust Inference and Propagation Model in Multi-Human Multi-Robot Teams

   https://doi.org/10.1145/3568294.3580164  

   Guo, Yaohui ; Yang, X. Jessie ; Shi, Cong ( January 2023 , HRI '23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction)

   Trust has been identified as a central factor for effective human-robot teaming. Existing literature on trust modeling predominantly focuses on dyadic human-autonomy teams where one human agent interacts with one robot. There is little, if not no, research on trust modeling in teams consisting of multiple human agents and multiple robotic agents. To fill this research gap, we present the trust inference and propagation (TIP) model for trust modeling in multi-human multi-robot teams. We assert that in a multi-human multi-robot team, there exist two types of experiences that any human agent has with any robot: direct and indirect experiences. The TIP model presents a novel mathematical framework that explicitly accounts for both types of experiences. To evaluate the model, we conducted a human-subject experiment with 15 pairs of participants (N=30). Each pair performed a search and detection task with two drones. Results show that our TIP model successfully captured the underlying trust dynamics and significantly outperformed a baseline model. To the best of our knowledge, the TIP model is the first mathematical framework for computational trust modeling in multi-human multi-robot teams. 

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5. Twitter-Based Knowledge Graph for Researchers

   Vincent, Kyle ; Meno, Emma ( May 2020 , Virginia tech)

   null (Ed.)

   The Twitter-Based Knowledge Graph for Researchers project is an effort to construct a knowledge graph of computation-based tasks and corresponding outputs. It will be utilized by subject matter experts, statisticians, and developers. A knowledge graph is a directed graph of knowledge accumulated from a variety of sources. For our application, Subject Matter Experts (SMEs) are experts in their respective non-computer science fields, but are not necessarily experienced with running heavy computation on datasets. As a result, they find it difficult to generate workflows for their projects involving Twitter data and advanced analysis. Workflow management systems and libraries that facilitate computation are only practical when the users of these systems understand what analysis they need to perform. Our goal is to bridge this gap in understanding. Our queryable knowledge graph will generate a visual workflow for these experts and researchers to achieve their project goals. After meeting with our client, we established two primary deliverables. First, we needed to create an ontology of all Twitter-related information that an SME might want to answer. Secondly, we needed to build a knowledge graph based on this ontology and produce a set of APIs to trigger a set of network algorithms based on the information queried to the graph. An ontology is simply the class structure/schema for the graph. Throughout future meetings, we established some more specific additional requirements. Most importantly, the client stressed that users should be able to bring their own data and add it to our knowledge graph. As more research is completed and new technologies are released, it will be important to be able to edit and add to the knowledge graph. Next, we must be able to provide metrics about the data itself. These metrics will be useful for both our own work, and future research surrounding graph search problems and search optimization. Additionally, our system should provide users with information regarding the original domain that the algorithms and workflows were run against. That way they can choose the best workflow for their data. The project team first conducted a literature review, reading reports from the CS5604 Information Retrieval courses in 2016 and 2017 to extract information related to Twitter data and algorithms. This information was used to construct our raw ontology in Google Sheets, which contained a set of dataset-algorithm-dataset tuples. The raw ontology was then converted into nodes and edges csv files for building the knowledge graph. After implementing our original solution on a CentOS virtual machine hosted by the Virginia Tech Department of Computer Science, we transitioned our solution to Grakn, an open-source knowledge graph database that supports hypergraph functionality. When finalizing our workflow paths, we noted some nodes depended on completion of two or more inputs, representing an ”AND” edge. This phenomenon is modeled as a hyperedge with Grakn, initiating our transition from Neo4J to Grakn. Currently, our system supports queries through the console, where a user can type a Graql statement to retrieve information about data in the graph, from relationships to entities to derived rules. The user can also interact with the data via Grakn's data visualizer: Workbase. The user can enter Graql queries to visualize connections within the knowledge graph. 

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