Knowledge Bases (KBs) have become more complex because some facts in KBs include more than two entities. The construction and completion of these KBs require a new relation extraction task to retrieve complex facts from the text. To address this issue, we present a new N-ary Document-Level relation extraction task that involves extracting relations that 1) include an arbitrary number of entities, and 2) can span multiple sentences within a document. This new task requires inferring relation labels and entity completeness, i.e., whether the entities in the document are (insufficient to describe the relation. We propose a reinforcement learning-based relation classifier training framework that can adapt most existing binary document-level relation extractors to this task. Extensive experimental evaluation demonstrates that our proposed framework is effective in reducing the impact of noise introduced by distant supervision or unrelated sentences in the document.

This full research paper explores how academics and engineering professionals view the incorporation of DEI into engineering ethics codes. Engineering professional societies have integrated DEI explicitly or implicitly into their codes of ethics. Yet, DEI is contested and even banned in many institutions of higher education in the US. Thus, there is a need to understand the relationship between ethics and DEI in engineering, including how engineering practitioners and scholars view DEI should be canonized in the profession. In this study, we addressed two research questions: (1) “To what extent and in what ways did participants view it to be important to incorporate DEI into an engineering ethics code?” and (2) “How do participants view the incorporation of DEI into the IEEE code of ethics?” The research methodology included a qualitative analysis of interview responses from engineering academics and practicing engineers regarding the integration of DEI into engineering codes of ethics, as well as their perceptions of IEEE's current code of ethics. We conducted semi-structured interviews with 25 engineering faculty members and 25 engineering practitioners between June 2022 and April 2023. During interviews, we shared the IEEE code of ethics and asked participants to share their perspectives of the code. Participants felt that embedding DEI principles into engineering ethics codes was important to ensure equitable treatment, access, and representation across all levels of the profession. Many participants felt that ethics codes should have a more concerted focus on DEI and DEI principles, whereas some participants questioned whether DEI should be within an engineering ethics code at all. Findings from this study can inform future innovations at the nexus of ethics and DEI. We specifically hope this work will encourage professional societies to continue integrating DEI within engineering codes of ethics with the undergirding confidence that many stakeholders across both engineering and academic communities are supportive of such integration.

This research-to-practice full paper aims to analyze the public's comments on generative artificial intelligence (GAI) in computer science (CS) education, by the BERT-based model and Large Language Model (LLM) approaches to sentiment analysis and contextualize the results within broader educational and technological landscapes. Artificial intelligence (AI) has played a crucial role in advancing technical development throughout many areas. Evidence points toward the likelihood of major developmental breakthroughs unfolding soon in those sectors. Education is one such area. While there is certainly a possibility for hype and unfulfilled promises, the advent of available GAI platforms, such as ChatGPT, has caused a surge of scholarly interest in the impact of these technologies on CS education. Amid the growing debate, both the potential benefits and concerns of GAI in this sector are increasingly coming to the fore as people grapple with the tradeoffs associated with these technologies when applied in education settings. One can imagine the range of conversations around the topic, but that is difficult to use as input for policymakers and administrators without a more concrete understanding. To wit, there remain open questions about which benefits and concerns people tend to focus on when discussing GAI in education. This large-scale qualitative study addresses that gap by exploring the public's perspectives on GAI in CS education. We engage in this work by collecting and analyzing data from social media platforms, specifically Reddit comments. The social media dataset was analyzed using machine learning (ML) techniques to identify topics based on sentiment analysis. The study's objective was to document and characterize the public's perspectives concerning the general characteristics of GAI, its features related to learning, and its usability in educational settings. Through sentiment analysis using Large Language Models (LLM), the study revealed an overall positive public perception toward using generative AI in CS education, with over 57% of comments being favorable, while also identifying prominent topics of interest and concerns, such as the potential benefits of personalized learning support and automated grading, as well as issues like academic dishonesty, perpetuation of biases, over-reliance on AI hindering critical thinking, displacement of human instructors, and the need for updated curricula. The insights gleaned from the analysis will be instrumental in computing educators gaining a more profound comprehension of GAI 'srole in education and the subsequent development of GAI -enriched curricula.

Engineering Projects in Community Service (EPICS) has been a structured program in engineering education curricula and pedagogies for almost three decades. EPICS aims to engage students with real-world and hands-on engineering practices while providing engineering solutions for the needs of communities locally and globally. Participating in EPICS helps engineering students develop their professional skills while applying their theoretical and technical knowledge towards a real-world service project. It also supports undergraduate engineering students' understanding of engineering practices and applications, thus positively impacting their professional formation as engineers. This paper explores how EPICS impacts engineering students' perspectives about their engineering majors based on their experiences in EPICS programs. A total number of 651 engineering students from various engineering majors and class standing responded to the end-of-semester survey. The survey was conducted in an EPICS program in a United States university during the academic years of 2019/2020 and 2020/2021. We conducted a thematic analysis of the student responses to an open-ended survey by inductive coding. They reflected on how their experiences in EPICS impacted their opinion of engineering as a major. Our findings showed that EPICS positively impacted most engineering students' thinking about engineering as their professional field. Some of the emerging themes included students developing a more nuanced understanding of real-world engineering applications, their professional and personal development, how to transfer theoretical knowledge into practice, engineers' impact on societies through projects, the connection between engineering and other disciplines, and developing a passion for engineering work. Understanding the impact of EPICS on students' perception of engineering itself can help us fully characterize the extent to which service-based programs affect students, thereby providing additional motivation to increase such programs' presence in undergraduate engineering education.

In this WIP research study, we depict our approach to investigating how US faculty members in engineering education conceptualize Diversity, Equity, and Inclusion (DEI). The recruitment of underrepresented faculty members, retention of these faculty members, and institutional racism are continuing issues in higher education. Implementing DEI initiatives within institutions can help engineering departments effectively respond to the aforementioned issues. Prioritizing DEI in institutional cultures is not only morally necessary, but its incorporation may also affect minoritized individuals' feelings of belongingness and performance in research, teaching practices in classrooms, and service. We argue that is essential to understand faculty members' perceptions and experiences with DEI in order to modify institutional cultures in ways that can wholly realize DEI aspirations. The purpose of this study is to address the research questions, “How do faculty members in engineering conceptualize Diversity, Equity, and Inclusion?”, “How do faculty members in engineering experience Diversity, Equity, and Inclusion in their work?”, and “How do faculty members' conceptions manifest in their experiences with DEI in their work?” We explored faculty members' experiences related to DEI in their work environment and participants unpacked experiences in research, teaching, or service. We collected interviews with 25 faculty members from engineering or philosophy programs who teach engineering students in U.S. universities. We used a purposeful recruitment method to include participants who self-identified across a range of demographic characteristics and who brought expertise in either DEI or engineering ethics. Findings from this study provide administrators, faculty members, professionals, and researchers in engineering education with strategies improve cultures by prioritizing DEI in U.S. higher education institutions and organizations.

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.

This study explores how engineering students perceive the benefits of extracurricular and co-curricular participation through the lens of dimensions of wellness. First-year engineering students at a large Midwestern research university were surveyed about their extracurricular experiences. Open-response data from 557 survey responses were analyzed using thematic analysis assisted by natural language processing to identify themes in how students describe the benefits of their participation in terms of dimensions of wellness. Students most frequently describe social, occupational, and intellectual benefits of participation. Findings characterize trends in these descriptions holistically and across different types of activities (e.g., recreational, professional, technical). These trends suggest ways to support students through extracurricular participation, communicate its benefits, and help students navigate choices in selecting beneficial activities to support their well-being, learning, and professional development.

Existing unsupervised relation extraction methods work by extracting sentence features and using these features as inputs to train a generative model. This model is then used to cluster similar relations. However, these methods do not consider correlations between sentences with the same entity pair during training, which can negatively impact model performance. To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an Encoder-Decoder architecture to train a relation extractor as the encoder. Given multiple sentences with the same entity pair as inputs, CURE is deployed by predicting the shortest path between entity pairs on the dependency graph of one of the sentences. After that, we extract the relation information using the encoder. Then, entity pairs that share the same relation are clustered based on their corresponding relation information. Each cluster is labeled based on the words in the shortest paths corresponding to the entity pairs in each cluster. Experimental results demonstrate the effectiveness of CURE compared to state-of-the-art models across all benchmark datasets.

This research full paper presents research around the Engineering Projects in Community Service (EPICS) program that serves two key purposes to: 1) provide a structured approach for engineering students to engage in real-world, service-based projects and 2) provide technical support and expertise that may be critical to local and global community organizations. Hence, EPICS strives to offer a platform that fosters the collaboration of engineering students and communities. EPICS helps develop undergraduate students’ professional skills extending beyond the theoretical knowledge acquired in classrooms. EPICS has been a fixture in engineering education for over 15 years, with a strong focus on curricular and pedagogical interventions to help students gain professional skills. The purpose of this paper is to explore the perspectives of over 650 students who participated in EPICS at a U.S. university during the academic years of 2019/2020 and 2020/2021. We used natural language processing (NLP) to thematically analyze students’ responses to an open-ended survey administered at the end of their semester participating in the EPICS program. Students’ responses reflect their perspectives on the design process, teamwork, real-world experiences, and the challenges they face during the design process related to other people and the program. In our findings, students’ least favorite parts of EPICS were lectures and design reviews, while their favorite parts of EPICS were teamwork and engaging with community partners. Understanding the themes emerging from the data can help us better implement community-based educational initiatives and find ways to better engage students in community service-learning projects. Our research provides implications for practice and research.

This full research paper documents assessment definitions from engineering faculty members, mainly from Research 1 universities. Assessments are essential components of the engineering learning environment, and how engineering faculty make decisions about assessments in their classroom is a relatively understudied topic in engineering education research. Exploring how engineering faculty think and implement assessments through the mental model framework can help address this research gap. The research documented in this paper focuses on analyzing data from an informational questionnaire that is part of a larger study to understand how the participants define assessments through methods inspired by mixed method strategies. These strategies include descriptive statistics on demographic findings and Natural Language Processing (NLP) and coding on the open-ended response question asking the participants to define assessments, which yielded cluster themes that characterize the definitions. Findings show that while many participants defined assessments in relation to measuring student learning, other substantial aspects include benchmarking, assessing student ability and competence, and formal evaluation for quality. These findings serve as foundational knowledge toward deeper exploration and understanding of assessment mental models of engineering faculty that can begin to address the aforementioned research gap on faculty assessment decisions in classrooms.

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.

This full paper reports on students’ experiences after working on a first-year engineering design project with a real client. The instructors partnered with a Children's Museum in the local area, and students were tasked with developing prototypes of potential exhibits. The purpose of this paper is to present results on students’ perceptions of their experience working with a real client, developing a prototype, and having to interact with project stakeholders (e.g., children). The course design was based on problem-based learning (PBL) and data were collected from 169 first-year engineering students who anonymously filled out an exit survey. Responses were coded and emerging themes are presented. Natural processing language techniques were also used to analyze the open-ended responses.

In this work-in-progress (WIP) study, we begin to identify explicit links between ethics and diversity, equity, and inclusion (DEI) in engineering education and closely related fields. We use systematic literature review procedures coupled with a qualitative content analytic approach to identify these explicit links within engineering education journals and conference papers. Through this WIP, we identify preliminary themes that represent explicit discourses connecting ethics and DEI and we cite associated literature. We unpack four themes that have a prominent presence in the abstracts that we have reviewed: cultural, global, social, and sustainable. These explicit connections will support future systematic review procedures wherein we will aim to identify implicit DEI and ethics connections via an analysis of whole manuscripts. While preliminary, we hope that these four themes can prompt strategies to connect ethics and DEI more purposefully when teaching towards these and related topics.