In this session, a panel of members of the current Fellows Committee of the IEEE Education Society will share advice on preparing nominations for Fellow of the IEEE and Fellow of the American Society for Engineering Education. The panelists will also provide advice on applications for competitive national and international awards. The panelists will explain how to enhance career planning to become eligible for awards and honors.

In introductory computer programming courses, students experience a range of emotions. Students often experience anxiety and frustration when they encounter difficulties in writing programs. Continued frustration can discourage students from pursuing engineering and computing careers. Although prior research has shown how emotions affect students’ motivation and learning, little is known about students’ emotions in programming courses. In this qualitative study of first-year engineering students taking an introductory programming course, we examined the emotions that these students experienced during programming tasks and the reasons for experiencing those emotions. Our study was grounded in the control-value theory of achievement emotions. Each research participant came to two laboratory sessions: a programming session and a retrospective think-aloud interview session. In the programming session, each participant worked individually on programming problems. We collected screen capture, biometrics, and survey responses. In the interview session, each participant watched a video of their actions during the programming session. After every 2 minutes of viewing, the participants reported the emotions that they had experienced during this 2-minute period. We performed a thematic analysis of the interview data. Our results indicate that the participants experienced frustration most frequently. Sometimes they experienced multiple emotions. For example, one participant felt annoyed because she had made a mistake, but she felt joy and pride when she fixed the mistake. To promote student learning, educators should take students’ emotions into account in the design of curriculum and pedagogy for introductory programming courses.

This work-in-progress research paper reports on an ongoing research study which addresses two research questions: RQ1) What are the moral narratives of engineering practitioners across industry sectors? RQ2) How are the engineers' moral narratives influenced by the organizational cultures of their workplaces? As stories that support and sustain one's moral life, moral narratives influence one's ethical decision-making. While it is expected that a better understanding of engineers' moral narratives can be helpful for improving engineering ethics education, little is known about engineers' moral narratives. This paper explains the methodological approach we are taking to answer the research questions. This paper also describes potential implications of this study by introducing a new pedagogical approach called Scaffolded Ethics Autobiography for engineering ethics education.

The 32 chapters of the 2019 Cambridge Handbook of Computing Education Research synthesize the existing research in computing education and propose new directions for future research. An author from each chapter will summarize their chapter with auto-advancing slides. Attendees will be introduced to the breadth of content in the new handbook and can identify chapters of interest. This fits uniquely as a special session, and will likely be informative, inspiring, and overwhelming.

This Work-in-Progress Research paper describes (1) the contemporary research space on ethics education in engineering; (2) our long-term research plan; (3) the theoretical underpinnings of Phase 1 of our research plan (phenomenography); and (4) the design and developmental process of a phenomenographic interview protocol to explore engineers' experiences with ethics. Ethical behavior is a complex phenomenon that is complicated by the institutional and cultural contexts in which it occurs. Engineers also have varied roles and often work in a myriad of capacities that influence their experiences with and understanding of ethics in practice. We are using phenomenography, a qualitative research approach, to explore and categorize the ways engineers experience and understand ethical engineering practice. Specifically, phenomenography will allow us to systematically investigate the range and complexity of ways that engineers experience ethics in professional practice in the health products industry. Phenomenographic data will be obtained through a specialized type of semi-structured interview. Here we introduce the design of our interview protocol and its four sections: Background, Experience, Conceptual, and Summative. We also describe our iterative process for framing questions throughout each section.

In this work in progress paper for the research category, we describe preliminary results from our design-based research project. Students leave engineering majors for many reasons. However, studies on goal orientation theory have shown that mastery goal orientation has positive correlations with outcomes such as persistence, self-efficacy, and self-regulated learning. We developed and taught an eight-week, one-credit course called Engineering the Mind as an intervention to encourage students to adopt positive learning dispositions and behaviors. We used the Stages of Change model as our theoretical framework to understand how engineering students adopt these learning dispositions and behaviors. We used multiple methods to assess student outcomes, collecting pre-post survey data as well as students' self-reflection assignments. In our preliminary results, we describe students in different Stages of Change based on their weekly course assignment on planning.

This work-in-progress paper describes an exploratory investigation of the relationship between Grit and retention of engineering students. Grit is a noncognitive trait that psychologists have used to predict success more accurately than cognitive traits like intelligence. We administered the Grit Scale in a first-year engineering design project course (n = 465). Using binary logistic regression, we found that Grit was not a significant predictor of retention, but one of Grit's subscales, Perseverance of Effort, was significant for both one- and two-year retention in engineering. Our results suggest that we may be able to improve retention by helping students develop persistence to overcome their challenges and difficulties.

Engineering social responsibility is the responsibility of engineers to evaluate the broader impacts of their work on public welfare. Despite the central role of social responsibility to the engineering profession, social responsibility does not seem to be adequately emphasized in engineering curriculum. This study seeks to understand how students understand the social responsibilities of engineers. Interviews with ten students and three professors in engineering were analyzed with thematic analysis. The results from this study indicate that students are aware of ways that engineers can both benefit and harm society. When asked what influenced their views on social responsibility, students identified personal and extracurricular experiences, not engineering courses. Engineering programs are encouraged to incorporate more explicit instruction about the social dimensions of engineering to support the development of socially responsible engineers.

Recently “grit” has been defined by psychologists as a personal attribute that correlates with persistence through difficulties. We present our preliminary findings on grit and engineering students in this work-in-progress paper. We administered the Grit Scale in a first-year engineering design project course in a large public university in Fall 2014 and Spring 2015 (n = 475). Using binary logistic regression, we showed that grit alone was not a significant predictor of retention in engineering. For future work, we will include two-year retention data and also consider moderating variables such as gender and ethnicity to determine whether their interaction with grit tells a different story. If the moderating variables reveal that grit is more important in retaining students with certain attributes, the Grit Scale may be useful in identifying at-risk students. If not, however, we will need to reconsider the Grit Scale when it is applied to engineering students.

Ancillary learning opportunities outside the classroom take different forms such as peer-led team learning (PLTL) and supplemental instruction (SI). To gain the benefits of ancillary sessions with less overhead than PLTL and SI, we facilitated optional weekly supervised homework sessions for AAE 35200, an aerospace structural mechanics course for third year engineering students at Purdue University. In these sessions, a graduate teaching assistant used the assigned homework problems to demonstrate key concepts on structural mechanics. In order to understand the attributes of the participants, a survey was administered. Also, session attendance was recorded and compared against the exam and homework results. Based on the collected data during the Fall 2015 and Spring 2016 semesters, we have reached the following conclusions: a) attending the supervised homework sessions does not significantly affect the exam score, whereas the opposite is true for the homework scores and b) SAT-Math scores do not seem to have a strong influence on both the exam and homework scores.

Studies suggest that students' attitudes toward engineering courses may determine whether they persist in engineering, rather than academic performance alone. We take a mixed-methods approach to explore gritty attitudes and to confirm whether previous research on grit can be applied to undergraduate engineering students. We address two main research questions: (1) Does the grit scale predict retention among first-year undergraduate engineering students? (2) What does grit specifically look like for engineering students? To answer the first question, we invited all first-year engineering students at one university to complete the grit scale during Fall 2014. We will check whether the scores on the grit scale correlate positively with retention in engineering. To answer the second question, we invited all students who were persisting in engineering after having earned a D or an F grade in a required technical course to participate in a semi-structured interview. We hope to capture how these engineering students respond to academic setbacks and what they believe about effort, perseverance, and academic success.

Peer Led Team Learning (PLTL) is a nationally recognized curriculum enhancement strategy adopted in various forms by over 150 universities and colleges across the United States. Consistent with the outcomes and the vision of ABET Engineering Criteria 2000 and the National Academy of Engineering Engineer 2020, PLTL prepares students to work in teams; apply knowledge of mathematics, science, and engineering to solve problems; communicate effectively; engage in life-long learning; and develop leadership skills. Published PLTL program data have shown that using peer leaders in small group workshop settings boosts performance in critical first-year courses including core math, science and engineering courses. The PLTL model promotes the growth of critical workplace skills for students and peer leaders such as working in teams, listening, critical thinking and leadership. This paper will present the basics of the PLTL instructional model, including sample materials developed for engineering workshops. Consideration of the practicalities of the six critical components will be discussed: integration of the workshop component into the course structure, involvement of the teaching faculty, training and supervision of the peer leaders, creation of challenging materials, and provision of appropriate institutional resources.

In core engineering courses, instructors administer multiple examinations as major assessments of students' learning. When a student is unable to take an exam, the instructor must estimate the missing exam score in order to calculate the student's course grade. Using exam score data from multiple offerings of two large engineering courses, we compared the accuracy of several methods to estimate an exam score, including linear regression methods. The standard error of regression of the ordinary least squares (OLS) regression model was consistently about 0.5. For final exam scores, the standard errors of linear models with equal weights were nearly the same as the standard errors of the OLS regression models. For other exam scores, the equal weight model was somewhat less accurate. The results of this study provide practical guidance to instructors who need to estimate missing exam scores.

Lab kits allow students to take home laboratory equipment to complete experiments on their own time. Kits like these can expand access to hands-on experiences for online courses and to budget-strapped campuses. Although students like these kits, no previous studies compared student learning outcomes on assignments using these new kits with previous laboratory equipment. During the 2014-2015 academic year, we conducted a quasi-experiment to compare students' achievement of learning outcomes. Half of the laboratory sections in each semester used the existing equipment, while the other sections used the new kit. The objectives of the laboratory assignments were the same and the instructions were kept as close as possible between the two groups.

A learning philosophy, as discussed in this paper, is a collection of beliefs about how one learns and approaches learning activities and tasks. Learning is often studied from the instructor's point of view, but learning from the student's perspective is not examined much. A baseline understanding of how students learning philosophies might be disclosed and interpreted are described by the nineteen student responses to an open-ended survey in an undergraduate elective course titled “Engineering in Global Context.” The findings suggest implications for course and curriculum design in order to promote higher levels of thinking and to develop appropriate epistemological beliefs. Partially through the lenses of Bloom's Taxonomy and Schommer's Epistemological Dimensions, this paper offers a glimpse into students' conceptions about learning.

Few studies of the climate in engineering for lesbian, gay, bisexual and transgender (LGBT) students have been conducted. According to these studies, LGBT students are often forced to cope with hostile climates in engineering. To address the question of how LGBT students experience the climate in engineering, we interviewed a total of 16 students at two institutions in the Midwest. We analyzed the interview transcripts using open coding based on a combination of Meyer's Minority Stress Theory and Tinto's Theory of Student Departure. Preliminary results indicate that LGBT students experience more situations of exclusion within engineering than in other areas of their campuses. Based on their experiences, students advocate increased visibility for LGBT students in engineering and a mentoring program to provide support from engineering faculty and graduate students who also identify as LGBT.

To compare the challenges that domestic and international students experience in adjusting to college, we conducted a mixed-methods study of first-year undergraduate engineering students at a large public university in the Midwest. We administered a survey to all first-year engineering students. We conducted separate focus groups of domestic and international first-year students. While the domestic and international students identified similar adjustment issues, international students had more difficulty with making American friends, understanding cultural references, adapting to American food, and becoming acquainted with unfamiliar teaching methods and assignments.

To describe how advanced engineering students decide their post-baccalaureate plans, we conducted a mixed-methods study with engineering students at a large public university in the Midwest. According to the surveys, there were no statistically significant differences between men and women in their choices of post-baccalaureate plans. Students who had positive undergraduate research experiences and students who felt attached to their departments as undergraduates were more likely to enter graduate school immediately after graduation. Students who had industrial internships were more likely to enter professional practice immediately. From the interview data, we defined eight decision style archetypes that characterize how students make post-baccalaureate plans.

The ability to reason with formal logic is a foundational skill for computer scientists and computer engineers that scaffolds the abilities to design, debug, and optimize. By interviewing students about their understanding of propositional logic and their ability to translate from English specifications to Boolean expressions, we characterized common misconceptions and novice problem-solving processes of students who had recently completed a digital logic design class. We present these results and discuss their implications for instruction and the development of pedagogical assessment tools known as concept inventories.

The mentoring relationship is an important and crucial aspect in the academic and professional development of both mentor and protégé. Although the characteristics of positive mentoring relationships have been identified, there is little prior research in assessing the relationship between undergraduate researchers and their graduate student mentors. We present a document analysis of the reflective journals and mentoring philosophy statements of four graduate student mentors of undergraduate researchers in a summer research program. The preliminary results show that mentors and students can have mismatched expectations, and that as the mentoring relationship evolves, the roles of the mentor and student also evolve. We also present a mentoring model grounded in the data.

Our general education course in digital information technology draws many students from academic disciplines outside science and mathematics. These students often struggle with the relevance of the course topics. To promote student motivation, we are modifying the course to better engage the students in the course topics via a method we term diversity harnessing. Diversity harnessing refers to the diversity in the students' personal interests and chosen academic disciplines. From the students' interests, we intend to gather applications and ideas and to quickly integrate this diverse student-driven subject matter back into the lectures, homework assignments, and examinations. Through diversity harnessing, we expect to find that students are more engaged in the course and that they apply the course content more effectively to their lives and careers beyond the end of the semester. We are assessing the effectiveness of diversity harnessing using both individual interviews and a standard course engagement questionnaire.

A concept inventory (CI) is a standardized assessment tool that evaluates how well a student's conceptual framework matches the accepted conceptual framework of a discipline. In this paper, we present our process in creating and evaluating the alpha version of a CI to assess student understanding of digital logic. We have checked the validity and reliability of the CI through an alpha administration, follow-up interviews with students, analysis of administration results, and expert feedback. So far the feedback on the digital logic concept inventory is positive and promising.

A concept inventory is a standardized assessment tool intended to evaluate a student’s understanding of the core concepts of a topic. In order to create a concept inventory it is necessary to accurately identify these core concepts. A Delphi process is a structured multi-step process that uses a group of experts to achieve a consensus opinion. We present the results of three Delphi processes to identify topics that are important and difficult in each of three introductory computing subjects: discrete mathematics, programming fundamentals, and logic design. The topic rankings can not only be used to guide the coverage of concept inventories, but can also be used by instructors to identify what topics merit special attention.

Location-based tracking systems (LTSs) use a variety of technologies to record the locations of objects. An LTS can increase the risks to the privacy and security of individuals. Previous studies have failed to distinguish between losses and violations of privacy when the locations of individuals are recorded by an LTS. We argue that individual privacy is threatened not by the collection of public location information but by the centralization of aggregated information, and by the combination of location information with other personal information. Further, informed consent should be required when the collection of information might cause a violation of privacy.

The ability to reason with formal logic is a foundational skill for computer scientists and computer engineers that scaffolds the abilities to design, debug, and optimize. By interviewing students about their understanding of propositional logic and their ability to translate from English specifications to Boolean expressions, we characterized common misconceptions and novice problem-solving processes of students who had recently completed a digital logic design class. We present these results and discuss their implications for instruction and the development of pedagogical assessment tools known as concept inventories.

A Delphi process is a structured multi-step process that uses a group of experts to achieve a consensus opinion. We present the results of three Delphi processes to identify topics that are important and difficult in each of three introductory computing subjects: discrete math, programming fundamentals, and logic design. The topic rankings can be used to guide both the coverage of standardized tests of student learning (i.e., concept inventories) and can be used by instructors to identify what topics merit emphasis.

In this paper, we characterize functions that simulate independent unbiased coin flips from independent coin flips of unknown bias. We call such functions randomizing. Our characterization of randomizing functions enables us to identify the functions that generate the largest average number of fair coin flips from a fixed number of biased coin flips. We show that these optimal functions are efficiently computable. Then we generalize the characterization, and we present a method to simulate an arbitrary rational probability distribution optimally (in terms of the average number of output digits) and efficiently (in terms of computational complexity) from outputs of many-faced dice of unknown distribution. We also study randomizing functions on exhaustive prefix-free sets.

We conducted a study to demonstrate that formal training in debugging helps students develop skills in diagnosing and removing defects from computer programs. To accomplish this goal in an assembly language course, we designed multiple activities to enhance students' debugging skills. These activities included debugging exercises, debugging logs, development logs and reflective memos, and collaborative assignments. In a previous paper, we reported positive qualitative results. Students agreed that formal debugging training enhanced their debugging skills. In this paper, we present positive quantitative results that support our previous qualitative results. Students who completed the optional debugging exercises spent significantly less time on debugging their programs than those who did not. Furthermore, we develop a model of debugging abilities and habits based on students' comments in their debugging logs, development logs, reflective memos, and evaluation surveys. Students and educators could use the model to diagnose students' current debugging skills and take actions to enhance their skills.

A.V. Aho et al. (Comput. Math. Applic., vol.8, p.205-14, 1982) used communicating finite-state machines to model synchronous protocols for reliable communication across unreliable channels. Their ideas are extended to modeling asynchronous protocols for communication across unreliable channels using finite-state machines communicating via an unreliable shared memory. Lower bounds on the size of machines and the number of symbols in the transmission alphabet required to achieve reliable communication are established. Two types of finite-state machines and two fault models for the shared memory are considered. In each case it is shown that there are robust protocols for deletion and insertion errors. It is also shown that there are no robust protocols for mutation errors. In contrast, in the synchronous case, robust protocols exist for all of these types of errors.>

This paper shows that every tree machine of time complexity t can be simulated on-line by a log-cost random access machine (RAM) of time complexity $O((t\log t)/\log \log t)$. Using information-theoretic techniques, it is shown that this simulation is optimal. It is also shown that every tree machine can be simulated by a unit-cost RAM in real time.

A dictionary is a data structure that supports insertion, deletion, and retrieval operations. To maintain a database, a dictionary machine accepts an arbitrary sequence of instructions at a constant rate. We designed two new VLSI dictionary machines on general-purpose networks that emulate the binary cube. One machine runs on a shuffle-exchange network. It includes a novel architecture to implement pipelining of dictionary instructions. The other machine runs on a cube-connected-cycles network. The design of this machine relies on the existence of a Hamiltonian path, which we establish explicitly for every cube-connected-cycle network.

An optimal method for dynamically embedding trees into arrays is presented. Every multi-head tree machine of time complexity $t(n)$ can be simulated on-line by a multihead d-dimensional machine in time $O(t(n)^{1 + 1/d} /\log t(n))$. An information-theoretic argument gives the worst-case lower bound $\Omega (t(n)^{1+1/d} /\log t(n))$ on the time required.

For all d ≥ 1 and all e ≫ d, every deterministic multihead e-dimensional Turing machine of time complexity T(n) can be simulated on-line by a deterministic multihead d-dimensional Turing machine in time O(T(n)1+1/d-1/e(log T(n))O(1)). This simulation almost achieves the known lower bound Ω(T(n)1+1/d-1/e) on the time required. Furthermore, there is a deterministic d-dimensional machine with just two worktape heads that simulates the e-dimensional machine on-line in time O(T(n)1+1/d-1/delog T(n)). These simulations are interpreted in terms of dynamic embeddings among data structures.