The introduction of generative artificial intelligence (GenAI) has been met with a mix of reactions by higher education institutions, ranging from consternation and resistance to whole-hearted acceptance. Previous work has looked at the discourse and policies adopted by universities across the U.S. as well as educators, along with the inclusion of GenAI-related content and topics in higher education. Building on previous research, this study reports findings from a survey of engineering educators on their use of and perspectives toward generative AI. Specifically, we surveyed 98 educators from engineering, computer science, and education who participated in a workshop on GenAI in Engineering Education to learn about their perspectives on using these tools for teaching and research. We asked them about their use of and comfort with GenAI, their overall perspectives on GenAI, the challenges and potential harms of using it for teaching, learning, and research, and examined whether their approach to using and integrating GenAI in their classroom influenced their experiences with GenAI and perceptions of it. Consistent with other research in GenAI education, we found that while the majority of participants were somewhat familiar with GenAI, reported use varied considerably. We found that educators harbored mostly hopeful and positive views about the potential of GenAI. We also found that those who engaged more with their students on the topic of GenAI, both as communi-cators (those who spoke directly with their students) and as incorporators (those who included it in their syllabus), tend to be more positive about its contribution to learning, while also being more attuned to its potential abuses. These findings suggest that integrating and engaging with generative AI is essential to foster productive interactions between instructors and students around this technology. Our work ultimately contributes to the evolving discourse on GenAI use, integration, and avoidance within educational settings. Through exploratory quantitative research, we have identified specific areas for further investigation.

Generative artificial intelligence (GenAI) is increasingly becoming a part of work practices across the technology industry and being used across a range of industries. This has necessitated the need to better understand how GenAI is being used by professionals in the field so that we can better prepare students for the workforce. An improved understanding of the use of GenAI in practice can help provide guidance on the design of GenAI literacy efforts including how to integrate it within courses and curriculum, what aspects of GenAI to teach, and even how to teach it. This paper presents a field study that compares the use of GenAI across three different functions - product development, software engineering, and digital content creation - to identify how GenAI is currently being used in the industry. This study takes a human augmentation approach with a focus on human cognition and addresses three research questions: how is GenAI augmenting work practices; what knowledge is important and how are workers learning; and what are the implications for training the future workforce. Findings show a wide variance in the use of GenAI and in the level of computing knowledge of users. In some industries GenAI is being used in a highly technical manner with deployment of fine-tuned models across domains. Whereas in others, only off-the-shelf applications are being used for generating content. This means that the need for what to know about GenAI varies, and so does the background knowledge needed to utilize it. For the purposes of teaching and learning, our findings indicated that different levels of GenAI understanding needs to be integrated into courses. From a faculty perspective, the work has implications for training faculty so that they are aware of the advances and how students are possibly, as early adopters, already using GenAI to augment their learning practices.

Providing rich, constructive feedback to students is essential for supporting and enhancing their learning. Recent advancements in Generative Artificial Intelligence (AI), particularly with large language models (LLMs), present new opportunities to deliver scalable, repeatable, and instant feedback, effectively making abundant a resource that has historically been scarce and costly. From a technical perspective, this approach is now feasible due to breakthroughs in AI and Natural Language Processing (NLP). While the potential educational benefits are compelling, implementing these technologies also introduces a host of ethical considerations that must be thoughtfully addressed. One of the core advantages of AI systems is their ability to automate routine and mundane tasks, potentially freeing up human educators for more nuanced work. However, the ease of automation risks a “tyranny of the majority”, where the diverse needs of minority or unique learners are overlooked, as they may be harder to systematize and less straightforward to accommodate. Ensuring inclusivity and equity in AI-generated feedback, therefore, becomes a critical aspect of responsible AI implementation in education. The process of developing machine learning models that produce valuable, personalized, and authentic feedback also requires significant input from human domain experts. Decisions around whose expertise is incorporated, how it is captured, and when it is applied have profound implications for the relevance and quality of the resulting feedback. Additionally, the maintenance and continuous refinement of these models are necessary to adapt feedback to evolving contextual, theoretical, and student-related factors. Without ongoing adaptation, feedback risks becoming obsolete or mismatched with the current needs of diverse student populations. Addressing these challenges is essential not only for ethical integrity but also for building the operational trust needed to integrate AI-driven systems as valuable tools in contemporary education. Thoughtful planning and deliberate choices are needed to ensure that these solutions truly benefit all students, allowing AI to support an inclusive and dynamic learning environment.

To prepare students for the future workforce, it is important to provide them with knowledge and guidance about professional software engineering practices. One critical element of workplace practices is learning on the job. There are few studies of how software engineers learn on the job and little focus within the literature on how this understanding can help train students. In this paper, we present a qualitative interview-based field study of workplace learning among professional software engineers. We conducted in-depth interviews with ten software engineers and analyzed the data using thematic analysis based on the learning ecologies framework. In our findings, we identify and discuss professionals’ motivation and reasons for learning, the different resources they use to learn, the challenges they face in their learning, and their views on the relationship between formal education and on the job learning. We draw implications of the findings for teaching and learning, and future research.

The omnipresence of software systems across all aspects of society has necessitated that future technology professionals are aware of ethical concerns raised by the design and development of software and are trained to minimize harm by undertaking responsible engineering. This need has become even more urgent with artificial intelligence (AI) driven software deployment. In this paper we present a study of an interactive pedagogical intervention –role-play case studies –designed to teach undergraduate technology students about ethics with a focus on software systems. Drawing on the situated learning perspective from the Learning Sciences, we created case studies, associated stakeholder roles, discussion scripts, and pre and post discussion assignments to guide students’ learning. Open-ended data was collected from thirty-nine students and analyzed qualitatively. Findings from the study show that by taking on different perspectives on a problem, students were able to identify a range of ethical issues and understand the role of the software system process holistically, taking context, complexity, and trade-offs into account. In their discussion and reflections, students deliberated the role of software in society and the role of humans in automation. The curricula, including case studies, are publicly available for implementation.

This research paper presents findings from a re-flective journal study of undergraduate students' everyday in-teractions with artificial intelligence (AI). As the application of AI continues to expand, students in technology programs are poised to be both producers and users of the technologies. They are also positioned to engage with AI applications within and outside the classroom. While focusing on the curriculum when examining students' AI knowledge is common, extending this connection to students' everyday interactions with AI provides a more complete picture of their learning. In this paper, we explore student's awareness and engagement with AI in the context of school and their daily lives. Over six weeks, 22 undergraduate students participated in a reflective journal study and submitted a weekly journal entry about their interactions with AI. The participants were recruited from a technology and society course that focuses on the implications of technology on people, communities, and processes. In their weekly journal entries, participants reflected on interactions with AI on campus (coursework, advertises campus events, or seminars) and beyond (social media, news, or conversations with friends and family). The journal prompts were designed to help them think through what they had read, watched, or been told and reflect on the development of their own perspectives, knowledge, and literacy on the topic. Overall, students described nine categories of interactions: coursework, news and current events, using software and applications, university events, social media related to their work, personal discussions with friends and family, interacting with content, and gaming. Students reported that completing the diaries allowed them time for reflection and made them more aware of the presence of AI in their daily lives and of its potential benefits and drawbacks. This research contributes to the ongoing work on AI awareness and literacy by bringing in perspectives from beyond a formal educational context.

In this research paper we examine undergraduate students' use of and perceptions of generative AI (GenAI). Although the initial hype around ChatGPT has subsided, GenAI applications continue to make inroads across learning activities. Like any other emerging technology, there is a lack of consensus around using GenAI within higher education. Students are early adopters of the technology, utilizing it in atypical ways and forming a range of perceptions and aspirations about it. To understand where and how students are using these tools and how they view them, we present findings from an open-ended survey response study with undergraduate students pursuing information technology degrees. Students were asked to describe 1) their understanding of GenAI; 2) their use of GenAI; 3) their opinions on the benefits, downsides, and ethical issues pertaining to its use in education; and 4) how they envision GenAI could ideally help them with their education. Thirty-seven students provided responses ranging in length from 20 to 300 words for each question. Responses were iteratively coded by researchers to uncover patterns in the data and then categorized thematically. Findings reveal that students' definitions of GenAI differed substantially and included many misconceptions - some highlight it as a technique, an application, or a tool, while others described it as a type of AI. There was a wide variation in the use of GenAI by students, with two common uses being writing and coding. They identified the ability of GenAI to summarize information and its potential to personalize learning as an advantage. Students identified two primary ethical concerns with using GenAI: plagiarism and dependency, which means that students do not learn independently. They also cautioned that responses from GenAI applications are often untrustworthy and need verification. Overall, they appreciated that they could do things quickly with GenAI but were cautious as using the technology was not necessarily in their best long-term as it interfered with the learning process. In terms of aspirations for GenAI, students expressed both practical advantages and idealistic and improbable visions. They said it could serve as a tutor or coach and allow them to understand the material better. We discuss the implications of the findings for student learning and instruction.

In order to diversify and increase the engineering workforce, there is a documented need to better support nontraditional students (NTS) pursuing engineering degrees. Yet, most models of student support are currently based on the traditional model of engineering education that takes place in a place-based, residential, research driven institutions. In this paper, we analyze semi-structured interviews to understand the needs of NTS in engineering and outline ways in which they can be better supported towards their degree. We use theoretical work on Moore's Theory of Transactional Distance which focuses on dialogue, structure, and learner autonomy. We found that NTS in our sample were dedicated to completing their degrees for practical, and specific reasons and were looking for a work-life-education balance. Participants almost never used any resources other than their instructors and cared most about flexibility of scheduling their classes around their other commitments. This contrasts with current work within the literature that argues for increasing engagement and offering a range of services to engineering students. Our research discusses findings from interviews of five nontraditional students in engineering. The semi-structured interviews were transcribed then open-coding and thematic analysis were performed by the research team. Results showcase major themes from the interviews such as Financial Security, Home Commitments, Motivation, Personal Well-Being, Academic Support, Social Support, and Other Concerns. We discuss these findings in the context of determining ways to support nontraditional students on campuses where these students are most prevalent and often overlooked within the research community.

This Full Research paper presents a comparison of two codebook generation methods using natural language processing (NLP): a human and NLP collaboration method and a fully automated NLP method (referred to as Human-NLP and Auto-NLP, respectively). Codebook generation serves as a preliminary step in most qualitative projects, and using NLP as a tool can help support the analysis and efficiency of the researcher. By utilizing NLP in the early stages of codebook generation, there are opportunities for detailed and productive gains when working with large corpora of textual data. Using NLP at this stage also allows the researcher to make sense of any outputs generated through automated means rather than simply accepting the output as it is. The outcome of both methods tested in this work will be used to evaluate and apply the codes across a large dataset. The Human-NLP method involves generating the initial themes using a large-language model (LLM), and the researcher revises the codebook further. The Auto-NLP method involves generating three rounds of codes, summarizing the codes in each until a saturation level has been reached through the overarching themes. The dataset used for this study comes from an analysis of students' perception and recognition of ethical concepts after participating in a semester-long course focused on ethics, society, and technology. The course introduced students to traditional ethics topics, such as those around engineering disasters, but also explored developing topics, such as facial recognition, dataset bias, and the impact of technology on the global food supply. We collected data between fall 2020 and 2022 from six (6) iterations of a semester-long course. A total of 210 student responses to the question - what did this course teach you about ethics - were analyzed. The results from both Human-NLP and Auto-NLP methods were promising in the level of detail summarized and the similarity of themes across the data. Eight (8) themes were finalized through the Human-NLP method, and twelve (12) were generated through the Auto-NLP method. We present a discussion exploring these themes and the limitations of using these methods.

In this research-to-practice paper, we present a study examining how role-play case studies can teach technology students multidimensional ethical thinking on broad, everyday uses of algorithms and technology. Given the complexity of most engineering objects and systems, students need to be able to think not only from a technical or social perspective when it comes to ethical and responsible development and use but also to incorporate an awareness of environmental and sustainability concerns. To enable students to think in this multidimensional manner, we developed a role-play case study that explores different aspects of using e-scooters on a college campus. E-scooters are commonly available across the US on many college campuses, and it is not uncommon for students to have experienced using them or being in an environment where they are frequently used. The case study explores aspects of using e-scooters on campuses considering the technical, social, policy, and sustainability issues. The role-play activity encourages students to work collaboratively with their peers to understand the case study, raise concerns, and ultimately reach a consensus on the future use of the technology. The goal is to get students to recognize ethics in play in everyday spaces. To support this goal, open-ended data were collected from six student teams (6–7 students per team) with a total of 39 students and analyzed using a thematic analysis approach. Students learning about the case were supported through the use of academic and industry resources and frameworks. We found that students expressed a range of ethical concerns and were able to identify dilemmas and value tensions that are inherent in technology use. Thematic analysis of student discussions demonstrated students' uptake of ideas about dignity, well-being, sustainability, regulation, and convenience with reflection, among others. The data highlights students developing empathy and ethical perspectives by taking on a role, “being,” and negotiating from a character's viewpoint and motivation.

Collaborative learning has proven to be a productive approach across domains including computer science. Especially for novices, collaboration among learners with diverse experiences, values, and knowledge can be especially effective. However, there is limited research on how collaborative interactions among learners from different undergraduate disciplines manifest during the learning of computational concepts. This qualitative study investigates interaction among learners in small interdisciplinary student groups in an undergraduate computational thinking (CT) class. In analyzing the social interactions of cohort members, the Differentiating Overt Learning Activities (DOLA) framework developed by Chi was used as a starting point. Several themes (e.g. explain-to-other, self-monitor, connect/plan) suggested by Chi’s overt activities framework emerged naturally in the observations. However, several categories that were not explicitly stated in the DOLA framework also emerged. These are: asking a shallow question versus asking an elaborate question, socializing, being confused or expressing frustration, self-talk, and struggle. Analysis of interactions between cohort members revealed that most of the group interactions were active in nature. Barriers to collaborative learning were accentuated when peers were novices. Students who lacked knowledge of computational concepts found it hard to contribute to a discussion, hindering learning, whereas students who had better conceptual understanding were constructive in their interactions, elaborated on a topic, made connections with previous problems, and displayed self-awareness about their understanding.

In this paper we provide an overview of theoretical and conceptual frameworks used in engineering education research (EER). First, we define the considerations for how the terminology around frameworks is used and how they are applied in the discipline. Then, we conduct a mapping review of different theoretical and conceptual frameworks used in EER that have been identified from grey literature publications in the discipline. We specifically use grey literature for this analysis to explore how frameworks are being used beyond their representation in traditional venues such as journal or conference publications, which are often post-hoc. Given the growth of EER during the past decade, many of the novel ideas and frameworks are likely to be first discussed and available in these venues. Furthermore, within the grey literature we analyze, frameworks are likely to play a broader role, including guiding the research design. The grey literature we analyze includes NSF-funded proposals, and engineering education Ph.D. dissertations from US institutions over the past decade (2011-2021). We examined a total of 194 dissertations and 106 NSF proposal abstracts uncovered using a set of keywords, academic discipline, and research areas. Our analysis approach utilizes and expands on existing categorizations, such as the Engineering Education Research Taxonomy and other categorical approaches within both the engineering education field and adjacent STEM and technology-related disciplines. Finally, we recognize the limitation of examining research situated only in the US but hopefully this study provides a roadmap to extend the analysis across other geographical areas with EER presence.

This panel will discuss the role of different knowledge artifacts in creating, maintaining, and circulating knowledge within the engineering education community. The past decade has seen a significant increase in the venues available for sharing engineering education research and as the field grows and builds more knowledge, it is equally important to also take stock of prior work and of strategies to create novelty. Within this context, what is the role of different knowledge encapsulating artifacts and why do those who engage with creating these artifacts do so? In this panel we touch upon these issues while taking stock of the knowledge base in the field. We will also discuss what the future of knowledge creation in the field might look like given the move towards open access online publications as the primary form of knowledge circulation. Finally, in the post-COVID context, what will and should be the role of in-person events in this process. In terms of equity of participation, what potential avenues are available?

In this Research-to-Practice paper, we present findings from a study of role-play discussions for teaching technology ethics. In recent years there has been an increased emphasis on preparing students who are not only aware of microlevel aspects of ethics but are cognizant of broader ethical obligations at organizational and societal levels. In order to prepare engineers who can contribute towards addressing the grand challenges facing the world, such as sustainability, poverty, and social justice, among others, this approach is necessary. Students have to develop the ability to look beyond a narrow problem or issue, e.g., ethical design choice related to the use of a certain chemical compound and harms to users, to the larger environmental implications of this choice. Ethics is taught in a variety of ways in engineering, including diverse pedagogical approaches and topics, but one core technique is the use of case studies. Although case studies have been used by engineering ethics educators for decades, their efficacy for teaching issues beyond micro-ethics and linking different levels of ethical concerns is unclear. In this paper, we present a research study examining the efficacy of one genre of case studies, role-play discussion, in allowing participants to link ethical concerns at multiple levels. As exemplars, we discuss two cases we designed and used. We collected data from 20 groups of 4-6 students each that discussed a specific case and find evidence that when designed with appropriate roles and related narrative, role-play case studies can be an effective pedagogical intervention. This paper presents findings from qualitative analysis of student discussions and other pre/post assessment measures to show how students link micro, meso, and macro ethical concerns through role-play discussions.

In this paper we present findings from analyses of articles published in Advances in Engineering Education (AEE) during the first 14 years (2007-2020) of its publication under the tenure of its founding editor. Our goal in this paper is two-fold: (1) to understand the impact of papers published in the journal as measured by citations, and (2) to understand the coverage of topics over time. To understand the impact and coverage of topics we qualitatively analyzed abstracts of all the articles published in the journal (N=242) and also used data mining techniques (text network mapping). In terms of impact, the topics that have been cited the most include curriculum development, use of technology for teaching and learning, teaching and learning strategies, and program development. We found that in keeping with the mission of the journal, papers largely reported evidence-driven program or curriculum development outcomes and papers published as part of special issues had the most impact as measured by citations.

Full Paper: Digital transformations are reshaping engineering practices with implications for conducting engineering education research. Given the paucity of discussion of digital methods within engineering education research, we believe it is important to examine and present to the community an overview of how digital technology is changing research practices. In this paper we focus on digital ethnography as it has implications for studies of technical education and work, which necessarily involve using, and observing how others employ digital data sources, tools, systems, methods, etc. In this paper we report preliminary results from an in-depth literature search and review. To select the papers for the review, we first examined prior meta-review papers that identified new ethnographic methods appropriate for digital contexts (e.g., network ethnography, trace ethnography, rapid ethnography, connective ethnography, focused ethnography, etc.). We then used these as keywords to search for papers that were representative of these methods and selected the 100 most cited papers from this corpus, with further screening resulting in a final collection of 91 papers. We then conducted free/open coding of the articles followed by thematic coding to identify six categories and dived deeper into one of the categories, focused on different approaches to ethnography, to further explore the various types of ethnographic methods mentioned in the collected literature. We close by discussing how emerging techniques in ethnographic field research can be applied to engineering education research with engineering work practices as an exemplar.

We present a Research-to-Practice paper where we used role-play case studies to improve student understanding of the ethics of algorithms. As the use of algorithmic decision-making continues to grow across areas of society, there is a need to prepare future technology workforce for ethical thinking related. Our work was informed by the situated learning paradigm, and our goal was to improve perspectival thinking among students. Recognizing an issue from multiple perspectives and taking on different perspectives to examine it leads to increased understanding. Drawing on this work, we created and implemented a role-play case study in an undergraduate computing data mining course. The role-play case study focused on the use of algorithms for facial recognition. Data were collected from pre-and post-discussion assignments, and a student survey. Thirty-one students enrolled in the course and completed the ethics module. The data collected in the assignments focused on student's recognition of ethical dilemmas, the change in student's perspective on the case due to creating a collaborative consensus and understanding the complexity of algorithmic decision making. To formally analyze the data, we created a coding schema drawing on the literature and preliminary qualitative analysis of our data. The data were independently coded by multiple coders. The findings indicate that through their participation in collaborative role-play scenarios, students were able to recognize a wide range of issues and offer potential solutions. We discuss the implications of the work. Curriculum material created as part of this work is available as an open education resource.

Keeping up with new knowledge being produced in computing related domains is a difficult task given the pace of change in the field. Specifically, in domains that are undergoing a lot of innovation, such as Data Science or Artificial Intelligence, updating curricula is not easy. Yet, there is a need to be cognizant of new topics in order to create and update curricula and keep it relevant. In this paper we present an innovative approach to help educators keep a better track of changes in a domain and be able to map their curricula objectives to emerging topics and technologies. We leverage Q&A sites, Reddit and StackExchange, which provide a useful online platform for sharing of information and thereby generate a valuable corpus of knowledge. We use Data Science as a case study for our work and through a longitudinal analysis of these sites we identify popular topics and how they have changed over time. We believe innovations such as these are essential for improving computer science education and for bridging the workplace-school divide in teaching of newer topics. Our unique and innovative approach can be applied to other CS topics as well.

This Research FULL PAPER extends recent scholarship on the role of technology in workplace learning in professional engineering and computing settings. Digitization of work practices has made a noticeable impact on how engineers gain expertise and solve problems they encounter at work. In this paper we use a workplace learning ecology lens to examine engineers' situated information seeking to identify practices associated with the use of online resources. Building on a previous qualitative interview study, we developed and administered a survey consisting of 16 items to assess use of online resources across learning experiences. We found high use of online resources but with variations among the use of specific resources by field, problem, and learning goal. LinkedIn, Twitter, YouTube, Wikipedia, Reddit, and technology vendor websites were the primary online platforms used by respondents for both learning and problem-solving. Respondents placed different levels of trust in online resources. Social media, especially Twitter, was trusted least across all sources. The highest trust was placed on websites of technology vendors. Findings from this work can help create better educational content as well as pedagogical interventions that use online resources for training the future workforce.

Updating curricula in new computer science domains is a critical challenge faced by many instructors and programs. In this paper we present an approach for identifying emerging topics and issues in Data Science by using Question and Answer (Q&A) sites as a resource. Q&A sites provide a useful online platform for discussion of topics and through the sharing of information they become a valuable corpus of knowledge. We applied latent Dirichlet allocation (LDA), a statistical topic modeling technique, to analyze data science related threads from from two popular Q&A communities "Stack Exchange and Reddit". We uncovered both important topics as well as useful examples that can be incorporated into teaching. In addition to technical topics, our analysis also identified topics related to professional development. We believe that approaches such as these are critical in order to update curriculum and bridge the workplace-school divide in teaching of newer topics such as data science. Given the pace of technical development and frequent changes in the field, this is an inventive and effective method to keep teaching up to date. We also discuss the limitations of this approach whereby topics of importance such as data ethics are largely missing from online discussions.

We explore how different segments of the population in India coped, in terms of business transactions, with the sudden decision of the government to stop accepting certain legal tender bills, popularly referred to as demonetization. The decision to demonetize was followed by a large-scale push for adoption of digital payments. Behavioral changes during such shocks do have specific nuances different from those during normal times. Using the concept of resilience, we examine the drivers of behavior change that differentiated those that were able to make the switch compared to those that weren't. Those technologically more adept were more resilient to the shock, in terms of being able to navigate through new means of exchange. Also, rural poor showed greater resilience than urban poor, a function of the level of homogeneity in those societies with respect to technology adoption and the ability to cope without changing cash practices. We also found that those who had bank accounts and relied largely on those accounts for daily transactions, without being aware of alternate modes, were impacted the most. From a policy perspective this research cautions against unintended consequences of purely access-driven incentives to behavior change, advocating instead a holistic approach.

In recent years, augmented reality games (ARGs) such as Pokémon Go have become increasingly popular. These games not only afford a novel gaming experience but also have the potential to alter how players view their physical realities. In addition to the common experiences and gratifications people derive from games, (location-based) ARGs can afford, for example outdoor adventures, communal activities, and health benefits, but also create problems stemming from, for example privacy concerns and poor usability. This raises some important research questions as to what drives people to use these new applications, and why they may be willing to spend money on the content sold within them. In this study, we investigate the various gratifications people derive from ARGs (Pokémon Go) and the relationship of these gratifications with the players’ intentions to continue playing and spending money on them. We employ data drawn from players of Pokémon Go (N = 1190) gathered through an online survey. The results indicate that game enjoyment, outdoor activity, ease of use, challenge, and nostalgia are positively associated with intentions to reuse (ITR), meanwhile outdoor activity, challenge, competition, socializing, nostalgia and ITR are associated with in-app purchase intentions (IPI). In contrast with our expectations, privacy concerns or trendiness were not associated with reuse intentions or IPI.

Social media provides a mechanism for people to engage with social causes across a range of issues. It also provides a strategic tool to those looking to advance a cause to exchange, promote or publicize their ideas. In such instances, AI can be either an asset if used appropriately or a barrier. One of the key issues for a workforce diversity campaign is to understand in real-time who is participating - specifically, whether the participants are individuals or organizations, and in case of individuals, whether they are male or female. In this paper, we present a study to demonstrate a case for AI for social good that develops a model to infer in real-time the different user types participating in a cause-driven hashtag campaign on Twitter, ILookLikeAnEngineer (ILLAE). A generic framework is devised to classify a Twitter user into three classes: organization, male and female in a real-time manner. The framework is tested against two datasets (ILLAE and a general dataset) and outperforms the baseline binary classifiers for categorizing organization/individual and male/female. The proposed model can be applied to future social cause-driven campaigns to get real-time insights on the macro-level social behavior of participants.

In November 2016, the Government of India banned the vast majority of the nation's banknotes in a move referred to as 'demonetization', with the stated goals of fighting corruption, terrorism, and eventually expanding digital transactions. In this study of 200 shop-keepers in Mumbai and Bengaluru, we found that cash shortage increased digital payment adoption but that digital payments fell after new banknotes became available. Digital payment adoption depended on the nature and scope of transactions, type of product sold, as well as personal factors specific to business owners such as comfort and familiarity with other digital technologies and online transactions. Using theoretical work on market and information behavior, we examined environmental pushes for technology adoption against prevalent transactional practices, trust, and control. We propose that the move toward digital payments must be framed within a larger undertaking of technology-driven modernity that drives these initiatives, rather than just the efficiency or productivity gains digital payments present.

The feminist theory of intersectionality asserts that experiences of social categories, such as gender, vary based on context and demographic factors and can be best understood by capturing and analyzing participants' self-expressions. Social media provide a novel setting to study this phenomenon. We examined participants' self-expressions on a campaign for increasing engineering diversity (#ILookLikeanEngineer) and found that, consistent with an intersectionality perspective, in addition to their identity as an engineer, participants opted to: a) expand upon and provide specifics about their engineering identity; b) expressed their affiliation with an institution or company; c) expressed personal aspects of their identity such as family or hobbies; d) expressed support for someone they knew who was an engineer; e) expressed solidarity with other social causes related to diversity; and f) expressed enthusiasm for or mentioned the campaign humorously. This study highlights the inherent complexity of identify that arises when people self-express themselves.

Higher education institutions in the United States and across the Western world face a critical problem of attrition of college students and this problem is particularly acute within the Science, Technology, Engineering, and Mathematics (STEM) fields. Students are especially vulnerable in the initial years of their academic programs; more than 60% of the dropouts occur in the first two years. Therefore, early identification of at-risk students is crucial for a focused intervention if institutions are to support students towards completion. In this paper we developed and evaluated a survival analysis framework for the early identification of students at the risk of dropping out. We compared the performance of survival analysis approaches to other machine learning approaches including logistic regression, decision trees and boosting. The proposed methods show good performance for early prediction of at-risk students and are also able to predict when a student will dropout with high accuracy. We performed a comparative analysis of nine different majors with varying levels of academic rigor, challenge and student body. This study enables advisors and university administrators to intervene in advance to improve student retention.

In this paper I draw on two case studies to examine participatory learning in Free/Libre and Open Source Software (FLOSS). I contribute to prior work on learning within FLOSS and also to the learning sciences by illustrating how FLOSS supports lifelong learning and working by providing an ecosystem that allows participants to grow their knowledge of both technical and nontechnical skills over time through their association with different projects and people. I trace the learning trajectories of two participants from high school until they enter the professional workforce and beyond. I argue that FLOSS participation represents an ideal networked form of learning as it provides both socio-cognitive support for short term activities and also socio-temporal support for long term participation and learning. It is a unique form of apprenticeship that exists concurrently with formal educational experiences but unlike traditional apprenticeship experiences it succeeds by spanning different spatialities - place/space and technology mix -and temporal scales.

Participants in FLOSS (Free/Libre Open Source Software) projects are atypical in their collaborative practices given the high demand for virtual work. Through a study of workers from two organizations working on FLOSS projects we identify the boundaries, in terms of productivity and quality of life, of virtual work and actions workers take in order to find a work-life balance. We found that although workers valued the flexibility of working from home, they had difficulty focusing on their work for sustained time periods and often felt isolated. This motivated them to use coworking spaces - physical spaces used as work space by workers not on the same team or even the same firm - as a critical part of their space ecology. In conjunction with their media ecology - a mix of communication technologies including IRC - the space/media mix allowed them to balance their work and personal lives. We draw implications for better supporting FLOSS and virtual work practices through design of media/space and work practices.

Studies on technology adoption typically assume that a user's perception of usability and usefulness of technology are central to its adoption. Specifically, in the case of accessibility and assistive technology, research has traditionally focused on the artifact rather than the individual, arguing that individual technologies fail or succeed based on their usability and fit for their users. Using a mixed-methods field study of smartphone adoption by 81 people with visual impairments in Bangalore, India, we argue that these positions are dated in the case of accessibility where a non-homogeneous population must adapt to technologies built for sighted people. We found that many users switch to smartphones despite their awareness of significant usability challenges with smartphones. We propose a nuanced understanding of perceived usefulness and actual usage based on need-related social and economic functions, which is an important step toward rethinking technology adoption for people with disabilities.

Many engineering disciplines are concerned with the creation of tangible objects, however using physical objects to facilitate learning in the engineering classroom is not broadly applied. Tangible objects are primarily used in design projects, with less attention to objects in other engineering activities. Nowadays a lot of attention is given on the Making and Maker movement. Questions remain whatever these activities help people learn engineering. We have completed two studies investigating the efficacy on concrete objects in the same engineering task with different experiential conditions. This work-in-progress is the third study from a line of research, where now virtual objects are included as a new study condition.

The transformation of ideas and inventions into actual products is an arduous process that often requires persistent promotion by the inventor, often collaboratively with others. Through an ethnographic field study of an information systems R&D laboratory I illustrate how innovators achieved the goal of promoting their digital inventions by crafting and performing collective narratives demos that strategically leveraged affordances of digital medium. Even though demos were not part of the formal evaluation metric for researchers, they played a crucial role within the organizations through their use for articulation work. Through this articulation work of narrative construction innovators translated their perceived image of the audience into material properties of their artifact and weaved it into their narrative presentation. These highly personalized and often interactive experiences allowed the innovators to tightly manage impressions of their artifact and of them. Products that could be personalized were more likely to garner audience interest and support within the organization. Given the increase in use of digital technology within organizations, more work is needed to understand how articulation changes with digitization.

Online communities continue to be an important resource for informal learning. Although many facets of online learning communities have been studied, we have limited understanding of how such communities grow over time to productively engage a large number of learners. In this paper we present a study of a large online community called Scratch which was created to help users learn software programming. We analyzed 5 years of data consisting of 1 million users and their 1.9 million projects. Examination of interactional patterns among highly active members of the community uncovered a markedly temporal dimension to participation. As membership of the Scratch online community grew over time, interest-based subcultures started to emerge. This pattern was uncovered even when clustering was based solely on social network of members. This process, which closely resembles urbanism or the growth of physically populated areas, allowed new members to combine their interests with programming.

We present findings from a study examining students' course-taking pathways to graduation and identify the factors regarding course-taking choices that can affect students' performance. The data for the study was collected from two majors within an engineering school at a large public university: computer science (CS) and information technology (IT). Although we look in depth at CS students, we use the other data for a comparative analysis. CS and IT are the most popular and largest majors, respectively, at our institution with almost 2,500 undergraduate students enrolled in them. The results show that there are differences in specific patterns of courses and illustrate relationships between the frequent courses in each semester and the relationships between courses taken in two consecutive semesters. Some major insights from the analysis of trajectory of frequent courses for both groups include: low performers postponed some courses toward the end of the program, and take a collection of courses together that their counterparts do not usually take. This work has direct implications for advising of prospective and current students and can improve programs' curriculum and students' performance. In the next stage of this study we will compare trajectories of students who graduate with those of students who either leave CS and IT or take longer to graduate. This preliminary research is part of a NSF CISE/EHR funded grant project on "BigData and Education".

Identifying the concepts covered in a university course based on a high level description is a necessary step in the evaluation of a university's program of study. To this end, data describing university courses is readily available on the Internet in vast quantities. However, understanding natural language course descriptions requires manual inspection and, often, implicit knowledge of the subject area. Additionally, a holistic approach to curricular evaluation involves analysis of the prerequisite structure within a department, specifically the conceptual overlap between courses in a prerequisite chain. In this work we apply existing topic modeling techniques to sets of course descriptions extracted from publicly available university course catalogs. The inferred topic models correspond to concepts taught in the described courses. The inference process is unsupervised and generates topics without the need for manual inspection. We present an application framework for data ingestion and processing, along with a user-facing web-based application for inferred topic presentation. The software provides tools to view the inferred topics for a university's courses, quickly compare departments by their topic composition, and visually analyze conceptual overlap in departmental prerequisite structures. The tool is available online at http://edmine.cs.gmu.edu.

With the increasing demand on knowledge sharing and problem solving, there is a growing participation on online Question & Answer (Q&A) forums in the recent past. We classify the online community participation on Stack Exchange into two different genres, one is technical and another is non-technical. Though several studies have measured community activity, studies that compare activity across forums within different topic areas are limited. In this work we examine the effect of incentives on contributions by exploring the differences between technical and non-technical communities in terms of user's participation. Given the increased attention on discussion forums as part of online learning, especially MOOCs, we believe that our findings can assist with providing better support for learners across different content areas.

We analyzed informal learning in Scratch Online -- an online community with over 4.3 million users and 6.7 million user-generated content. Users develop projects, which are graphical interfaces involving manipulation of programming blocks. We investigated two fundamental questions: how can we model informal learning, and what patterns of informal learning emerge. We proceeded in two phases. First, we modeled learning as a trajectory of cumulative programming block usage by long-term users who created at least 50 projects. Second, we applied K-means++ clustering to uncover patterns of learning and corresponding subpopulations. We found four groups of users manifesting four different patterns of learning, ranging from the smallest to the largest improvement. At one end of the spectrum, users learned more and in a faster manner. At the opposite end, users did not show much learning, even after creating dozens of projects. The modeling and clustering of trajectory patterns that enabled us to quantitatively analyze informal learning may be applicable to other similar communities. The results can also support administrators of online communities in implementing customized interventions for specific subpopulations.

We present findings from a qualitative field study of a national e-infrastructure project currently underway in India. We study how this large and complex infrastructure for issuing unique biometric-based identification numbers to the 1.2 billion residents of India was aligned across various stakeholders engaged in the project. We find that the focus on 'data' kept the entire infrastructure together and working. However, this narrow focus also made the design team view the people applying for IDs as numbers whose management could be standardized. In reality, the infrastructure encountered people with differing experiences and expectations of state-issued IDs, expectations that had to be managed by agents on the ground. With our focus on the nation state as a site for studying e-infrastructure, we extend the domains in which CSCW research takes place and contribute to the theory of infrastructure building in a context where state-citizen relations are at stake.

In online communities a few experts are able to help a large number of help-seekers -- whether in Q&A communities or other forms of online forums. How is this efficiency achieved? How useful is this help? We show that expert help-giving can be characterized as: (1) Fast -- Most active help-givers gave response promptly and were most responsive during peak activity; (2) Functional -- There was little duplication of help-giving effort; and, (3) Fitting -- Initial responses were of high quality and reduced the need for further clarifications and corrections; high quality responses were provided earlier in the thread. Examination of differences across experts revealed that the most highly rated group of experts responded to 69% of the questions with a median response time of 16 minutes, twice as fast as other experts. Finally, we demonstrate the high quality of response through a taxonomy that characterizes expert responses as: framing, guiding, or engaged help.

We present a design study of the Deep Insights Anywhere, Anytime (DIA2) platform, a web-based visual analytics system that allows program managers and academic staff at the U.S. National Science Foundation to search, view, and analyze their research funding portfolio. The goal of this system is to facilitate users' understanding of both past and currently active research awards in order to make more informed decisions of their future funding. This user group is characterized by high domain expertise yet not necessarily high literacy in visualization and visual analytics-they are essentially casual experts-and thus require careful visual and information design, including adhering to user experience standards, providing a self-instructive interface, and progressively refining visualizations to minimize complexity. We discuss the challenges of designing a system for casual experts and highlight how we addressed this issue by modeling the organizational structure and workflows of the NSF within our system. We discuss each stage of the design process, starting with formative interviews, prototypes, and finally live deployments and evaluation with stakeholders.

Online discussion forums have emerged as a popular Web application to build and support online communities for numerous engineering interest areas and practice. However, a review of engineering education literature reveals scant research on the use of online discussion forums for engineering learning beyond the classroom. This study addresses this gap in knowledge through a study of the “Homework Help” section on AllAboutCircuits.com to examine what students sought help for and for what purpose. We downloaded over 6,000 discussion messages spanning over 8 years and extracted the textual data with a Python program. Instead of analyzing the data through manual means, we utilized the Natural Language Toolkit (NLTK) to capture textual patterns and leverage a topic modeling approach, Latent Dirichlet Allocation, to identify connected clusters of words. Linguistic Inquiry and Word Count (LIWC) analysis was also used to determine how often students use words associated with cognitive processes. We found that the homework help section of informal online discussion forums cater to students seeking help on fundamental ECE topics. Our findings also suggest that online discussion forums are supportive learning environments, as students freely engage in meaningful inquiries and social interactions with other learners.

In this research, we bring attention to one of the largest electrical and computer engineering (ECE) online discussion forums AllAboutCircuits.com. There are close to two hundred thousand learners contributing more than a million messages to this forum for over more than a decade. The massive archives of educational data raise an important question for engineering educators: How do we assess online participation? In this work-in-progress, we propose a multilevel approach to assess online participation based on social network measures and Hierarchical Linear Modeling (HLM) techniques. Specifically, we consider the employment of HLM to model the association between online participation and measures of social capital. Predictors of social capital are established, at both the individual and group levels, to examine the association between these predictors and learners' participation frequencies. We discuss our choice of HLM as a modeling approach over ordinary least squares (OLS) regression methods. We then describe how data is collected from the site of study, AllAboutCircuits.com, and how the modeling approach is conceptualized and used for assessment purposes. The importance of this work lies not only on sheer participation figures - it derives from the argument that online discussion fosters productive collaborative learning and individual reflection.

Mega infrastructure projects require considerable financial and human resources. Their costs are hard to justify, especially in low-income countries, and their sustenance depends to a large extent on their success in gaining political and public legitimacy. This paper examines one such project in India -- 'Aadhaar' -- developed to provide biometric-based identification for the 1.2 billion residents of India. Since its initiation in 2010, the project has issued more than 320 million identification numbers. Aadhaar is India's first state-sponsored e-infrastructure mega project and is unique for its survival in the face of severe opposition. We argue that the project survived because its proponents were able to legitimize it by showcasing its promise of poverty reduction and financial inclusion, and by making it a metaphor for progress and development. We compare the project with two mega projects undertaken earlier in India -- the railroad network and a dam project -- and find that Aadhaar's digital infrastructure made the use of symbolism harder, thereby requiring different strategies to gain legitimacy.

Email has been the primary communication medium in organizations for decades and despite studies that demonstrate its obvious disadvantages, the prevailing thinking is that email is irreplaceable. In this paper I challenge that view through a field study of a distributed firm that is highly successful in developing and delivering products without regular use of email in the workplace. Group blogging and IRC were the primary tools used and they allowed improved coordination and knowledge sharing compared to email. This paper contributes to scant literature in CSCW on firm-level technology use.

Few studies of online communities take exogenous factors into account while explaining community participation. We present preliminary results from a study investigating the impact of steward companies' actions on online community participation. We identified two events: (1) open sourcing of Java by Sun and (2) acquisition of Sun (and consequently of Java) by Oracle, and examined participation in their developer online communities. We found significant change in participation levels around each event with both significant increases and decreases. We conjecture that participation increased if the action was perceived as supportive by developers (e.g. Sun's open sourcing of Java) whereas it decreased if the action was perceived as detrimental by developers (e.g. Oracle's acquisition of Sun).

In this interactive paper we present preliminary results from an analysis of help seeking interactions of newcomers in an online developer community for beginners. We develop the distinctions among newcomers to create different profiles of newcomers which are often clubbed together in a homogeneous group. These profiles are generalizations of activities, behavior and attitudes of newcomers in these communities and they highlight the differences among members of this group.

In this paper we present findings from a project that used topic modeling and associated techniques to chart the emergence and growth of research topics in engineering education research over 9 years, from 2000-2008. As a field engineering education research has undergone significant changes over the past decade. There has been an increase in the number of scholars and practitioners involved in the field, particularly those that are applying rigorous research principles to advance understanding of engineering education. In such a circumstance, it is important to understand the topics, approaches, and ideas that have caught the imagination of people in the community. Since this nature of work has not been done in relation to engineering education research, a significant part of the effort described here is innovative and exploratory in nature where different techniques were tested with the goal to collect a diversity of topics that are of interest to the community. We identify major categories of topics and primary topics of interest to the community. We also identify a lack of engagement with theoretical and analytical ideas as an area of concern.

This workshop will bring together researchers from the engineering design, engineering learning, and creativity communities to explore emerging theoretical and pedagogical issues related to creativity and cognition in engineering design. The need to produce a creative engineering workforce for the 21st century is well recognized. This workshop will help bridge the study of creativity (what is creativity in an engineering context) and cognition (how do engineering students become or act creatively). This workshop will lead to a framework for future research and strategies to prepare creative engineers.