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Contribution: This article proposes a new definition of conceptual understanding (CU) specific to engineering. It then measures CU of signals and systems (S&S) in senior undergraduate students and describes how students approach conceptual problems. Background: Previous studies across multiple engineering subjects show students have low CU at the end of courses. However, little is known about CU semesters after a course. Research Questions: What is the CU of S&S concepts among electrical engineering senior students? Methodology: This mixed method study uses quantitative concept inventory data (n=467) and think-aloud interviews (n=12) to measure CU. The results come from two universities. Findings: Seniors' scores on the concept inventory are typical of scores presented at the end of an S&S course. Many struggled with the concept of linearity, made a common error when finding the maximum value in graphical convolution, and had low confidence on relating frequencies in time to a Fourier transform representation, but seniors had relatively high CU of time invariance and filtering.

Background: Despite well-documented benefits, instructor adoption of active learning has been limited in engineering education. Studies have identified barriers to instructors' adoption of active learning, but there is no well-tested instrument to measure instructors perceptions of these barriers. Purpose: We developed and tested an instrument to measure instructors' perceptions of barriers to adopting active learning and identify the constructs that coherently categorize those barriers. Method: We used a five-phase process to develop an instrument to measure instructors' perceived barriers to adopting active learning. In Phase 1, we built upon the Faculty Instructional Barriers and Identity Survey (FIBIS) to create a draft instrument. In Phases 2 and 3, we conducted exploratory factor analysis (EFA) on an initial 45-item instrument and a refined 21-item instrument, respectively. We conducted confirmatory factor analysis (CFA) in Phases 4 and 5 to test the factor structure identified in Phases 2 and 3. Results: Our final instrument consists of 17 items and four factors: (1) student preparation and engagement; (2) instructional support; (3) instructor comfort and confidence; and (4) institutional environment/rewards. Instructor responses indicated that time considerations do not emerge as a standalone factor. Conclusions: Our 17-item instrument exhibits a sound factor structure and is reliable, enabling the assessment of perceived barriers to adopting active learning in different contexts. The four factors align with an existing model of instructional change in science, technology, engineering, and mathematics (STEM). Although time is a substantial instructor concern that did not comprise a standalone factor, it is closely related to multiple constructs in our final model.

Despite numerous benefits, students do not always respond positively and engage in active learning in STEM courses. Understanding the factors that influence how students' respond to active learning is important to devising interventions that support their learning, especially for students from historically underrepresented groups. This study examines the influence of students' belongingness and self-efficacy on their affective and behavioural response to active learning and the moderating influence of students' gender-identity. We surveyed 579 students from 25 Science, Engineering, and Computer Science courses across 14 universities. Using a latent indirect-effect model, we found that belongingness and self-efficacy independently predicted students' affective response to active learning and evaluation of the class. Belongingness also predicted students' self-reports of their behavioural participation in active learning. Using measurement invariance, we also found that, despite mean differences in value, positivity, and distraction, there were no gender differences in the strength of relations between variables. These findings suggest that belongingness and self-efficacy play an important role in how all students respond to active learning and that fostering an atmosphere that supports both may reduce student's resistance to engaging in active learning.

In this paper, we report and expand on research questions designed to improve undergraduate engineering education. These questions are based on a yearlong process that included a three-step Delphi study and a subsequent two-day workshop. The Delphi study, conducted during winter and spring of 2015, engaged subject matter experts from engineering education research and engineering academic administration. It resulted in the formation of three writing teams, one each for the critical areas of improving student learning in undergraduate engineering education, improving and diversifying pathways of engineering students to increase retention, and using technology to enhance learning and engagement in engineering. Participants in the two-day workshop, held in October 2015, were chosen for their expertise in one or more of the three areas, such that the workshop could explore priorities for research that would address each of these areas. Using results from the Delphi study and the workshop, the writing teams worked to synthesize and expand on the research questions to guide future work. This paper addresses the first area: improving student learning in undergraduate engineering education. Comprehensive, systemic, and systematic improvement of student learning in the undergraduate engineering education system will require change across numerous elements in the system. The Delphi study and the subsequent workshop clustered major issues related to these numerous elements into four themes: (1) change the organizational culture, (2) research effective assessment practices, (3) promote adoption of research-based teaching practices, and (4) characterize successful faculty development. For each of the four themes, we present a rationale to support selection of the theme and offer categories to organize the research questions. We expect these questions will catalyze scholars to generate new areas of research, will inspire engineering instructors to pursue ideas for improving teaching and assessment in their classrooms, and will galvanize administrators to apply insights to change institutional policies, teaching and assessment activities, faculty development initiatives, and, ultimately, their organizational cultures.

This paper investigates what instructional factors influence conceptual understanding (CU) of signals and systems for senior undergraduate engineering students. Previous results show students in signals and systems courses typically gain little CU, though evidence-based instructional practices, such as active learning, can increase gains in CU. However, few studies consider CU of senior students or other instructional practices that increase CU. To explore possible factors, we interviewed two faculty members, eight undergraduate seniors, five graduate students, and four practicing engineers then analyzed the transcribed interviews using a constant comparative method. Participants identified lectures presenting CU along-side mathematical expressions; lectures emphasising purpose and connections; hands-on activities where students have control, receive immediate feedback, or where they have to apply and synthesise concepts; and repetition of concepts across multiple courses as factors that helped build CU. Grades that emphasise procedural knowledge over CU and heavy workloads were noted as hindrances to CU. This paper relates these findings to theories on conceptual understanding and previous results on factors that influence student learning.

Active learning increases student learning, engagement, and interest in STEM and subsequently, the number and diversity of graduates. Yet, its adoption has been slow, partially due to instructors' concerns about student resistance. Consequently, researchers proposed explanation and facilitation instructional strategies designed to reduce this resistance. Using surveys from 2-year and 4-year institutions including minority-serving institutions, we investigate the relationship between students' affective and behavioral responses to active learning, instructors' use of strategies, and active learning type. Analyses revealed low levels of student resistance and significant relationships between both explanation and facilitation strategy use and positive student responses.

Background: Demographic characteristics are known to influence participation in cocurricular activities. Less studied are the effects of other background characteristics. Purpose: We hypothesize that considering college knowledge and students' proactive behaviors in tandem with demographics provides better models for predicting such participation. Method: We developed a questionnaire and administered it to 3,618 domestic third- and fourth-year undergraduate engineering students at a large public R1 Midwestern university, yielding 860 responses. Logistic regression models predicting five types of cocurricular participation were constructed with demographic characteristics, college knowledge, and proactive behaviors in all combinations as predictors. Results: Four of five types of cocurricular participation were better modeled using factors beyond demographics. Two were better modeled using only proactive behavior as predictors and two were better modeled using demographics in combination with either college knowledge or proactive behavior. Only one type of participation could be best predicted by demographics alone. Conclusions: These findings contribute quantitative evidence establishing relationships between participation in engineering cocurricular activities and a wider range of factors than previously reported. Furthermore, they provide guidance for creating intervention programs because, unlike demographics, college knowledge and proactive behavior can be shaped by either the individual or the institution.

Flexible classrooms are designed to support active learning through movable tables and chairs that can be rearranged into different layouts and through technology such as wall-mounted monitors and whiteboards. This paper investigates how instructors experienced with active learning use the furniture and technology of a flexible classroom to facilitate active learning. We present a case study of one flexible classroom across courses at different levels of the engineering curriculum, analysing how the instructors used the classroom to implement nine strategies for facilitating active learning. Our findings provide many examples of how the flexible classroom allowed instructors to easily walk around the room and interact with students in a way that better facilitated active learning. The flexible classroom also supported the instructors in developing a routine for active learning and designing activities to better engage students. We conclude by describing evidence-based recommendations for flexible classroom professional development informed by our analyses.

Background: Despite the evidence supporting the effectiveness of active learning in undergraduate STEM courses, the adoption of active learning has been slow. One barrier to adoption is instructors' concerns about students' affective and behavioral responses to active learning, especially student resistance. Numerous education researchers have documented their use of active learning in STEM classrooms. However, there is no research yet that systematically analyzes these studies for strategies to aid implementation of active learning and address students' affective and behavioral responses. In this paper, we conduct a systematic literature review and identify 29 journal articles and conference papers that researched active learning, affective and behavioral student responses, and recommended at least one strategy for implementing active learning. In this paper, we ask: (1) What are the characteristics of studies that examine affective and behavioral outcomes of active learning and provide instructor strategies?; and (2) What instructor strategies to aid implementation of active learning do the authors of these studies provide? Results: In our review, we noted that most active learning activities involved in-class problem solving within a traditional lecture-based course (N = 21). We found mostly positive affective and behavioral outcomes for students' self-reports of learning, participation in the activities, and course satisfaction (N = 23). From our analysis of the 29 studies, we identified eight strategies to aid implementation of active learning based on three categories. Explanation strategies included providing students with clarifications and reasons for using active learning. Facilitation strategies entailed working with students and ensuring that the activity functions as intended. Planning strategies involved working outside of the class to improve the active learning experience. Conclusion: To increase the adoption of active learning and address students' responses to active learning, this study provides strategies to support instructors. The eight strategies are listed with evidence from numerous studies within our review on affective and behavioral responses to active learning. Future work should examine instructor strategies and their connection with other affective outcomes, such as identity, interests, and emotions.

Student socialisation into the norms and environment of their institution is a crucial process during their college experience. During the COVID-19 pandemic, these processes were disrupted, and little published research exists exploring comparisons of these student socialisation processes before and during the pandemic. Accordingly, we report on a quantitative study conducted at a large mid-western university in the United States, which compared survey data from 2018 to data collected in 2021. We operationalised student socialisation processes using Ashford and Black's proactive behaviours scale to explore how these behaviours and relevant socialisation outcomes changed from before to during the pandemic. The primary socialisation outcomes of interest were engineering identity and social capital. During the COVID pandemic, students reported lower levels of social capital. However, we simultaneously observed higher ratings of students' engineering identity. Next, using multivariate linear regression, we examined which proactive behaviours best predicted socialisation outcomes in both datasets. We found all proactive behaviours were significant predictors of social capital. Proactive behaviours did not influence student's sense of engineering identity. This study contributes to the growing literature on the pandemic's impact on engineering undergraduate students and how proactive behaviours are related to their socialisation outcomes. [ABSTRACT FROM AUTHOR]

Recent research has supported the use of student-centered teaching practices, such as active learning, because of its effectiveness in improving student learning and retention when compared with traditional, lecture-based teaching practices. Despite evidence supporting the effectiveness of active learning in improving STEM undergraduate education, the adoption of active learning by instructors has been slow for reasons, including negative student response to active learning. In this systematic literature review, we examine students' negative responses to active learning and reasons for the negative responses noted in 57 published STEM studies. Our findings identify three types of negative responses: affect, engagement, and evaluation. The reasons behind negative response represented six overarching categories based on student feedback: limited value, lack of time, difficulty and increased workload, lack of guidance, logistical difficulties, unfamiliarity with active learning, lack of preparation, and confidence. We leverage different theoretical perspectives to explain the reasons behind negative responses and offer insights for lowering the barrier for instructors to adopt active learning in STEM classrooms.

Contribution: This paper presents evidence demonstrating ways in which flexible classrooms (which have movable tables and chairs that can be rearranged into different layouts) afford active learning. It highlights the quantitative increase in active learning that occurs for one instructor and discusses how the affordances of the flexible classroom support qualitatively better instructor-student and student-peer interaction during active learning. Background: Research has shown that students benefit from active learning, but instructors still perceive many barriers to implementing it. Flexible classrooms may reduce some of these barriers, and their affordances may promote better student engagement and allow instructors to use more active learning than traditional lecture-style classrooms do. Research Questions: What are the differences in the amount of active learning used by an instructor between flexible classrooms and traditional classrooms? How do instructors and students use the affordances of flexible classrooms during active learning? Methodology: An instructor at a large Midwestern university taught the course Introduction to Electronics Circuits in a traditional classroom one semester and in a flexible classroom the next. The two research questions were addressed through complementary quantitative and qualitative analyses of video data, classroom observations, and instructor interviews to detail the amount of active learning and the way the instructor facilitated it in the flexible classroom. Findings: The time the instructor devoted to active learning increased in the flexible classroom, while the time she devoted to instructor-led examples decreased. The affordances of the flexible classroom also encouraged more frequent and better student-instructor and student-peer interaction.

Engineering education researchers are increasingly integrating qualitative and quantitative research methods to study learning and retention in engineering. While quantitative methods can provide generalisable results, qualitative methods generate rich, descriptive understanding of the investigated phenomenon. On the other hand, a mixed methods approach provides benefits of the two approaches by incorporating them in a single study. However, engineering faculty often faces difficulty in integrating qualitative and quantitative methods and designs in their education research. This article discusses mixed methods in the context of an actual ongoing engineering education research project investigating student resistance to active learning. We describe the research design in phases that show the integration of quantitative and qualitative results, and how these data sources can help influence the direction of the research and triangulate findings. Our mixed method research experience highlights the importance of thinking iteratively between qualitative and quantitative data sources during the instrument development process.

Several reports have highlighted the need to improve teaching within the science, technology, engineering, and mathematics (STEM) fields, particularly at the undergraduate level. These reports have encouraged the use of more evidence-based teaching practices in the classroom to improve teaching. The authors explore how faculty members' decision to adopt evidence-based teaching practices can be affected, both positively and negatively, by different individual and institutional influences. They found that there were three main motivators and three main barriers that influenced the adoption of evidence-based teaching practices. Based on these findings, they offer suggestions for overcoming these barriers.

In spite of considerable evidence of the effectiveness of active learning and other contemporary teaching methods, barriers to adoption of those methods, such as possible student resistance, continue to exist. This study addresses student resistance by analyzing data from 1,051 students who completed our Student Response to Instructional Practices (StRIP) instrument in 18 introductory engineering courses where active learning was implemented. Through descriptive statistics, correlation analyses, and hierarchical linear regression modeling, we demonstrate that students' perceptions of their instructors' use of explanation and facilitation strategies can have a significant impact on student resistance. This study provides a more complete picture of the relative efficacy of these strategies to reduce resistance and confirms that students' perceptions of their instructors' use of these strategies can influence both how students engage with active learning and how students evaluate the course and instructor. We provide evidence-based advice for both new and experienced instructors to reduce student resistance to active learning and other contemporary teaching methods.

Background: Research has shown that active learning promotes student learning and increases retention rates of STEM undergraduates. Yet, instructors are reluctant to change their teaching approaches for several reasons, including a fear of student resistance to active learning. This paper addresses this issue by building on our prior work which demonstrates that certain instructor strategies can positively influence student responses to active learning. We present an analysis of interview data from 17 engineering professors across the USA about the ways they use strategies to reduce student resistance to active learning in their undergraduate engineering courses. Results: Our data reveal that instructor strategies for reducing student resistance generally fall within two broad types: explanation and facilitation strategies. Explanation strategies consist of the following: (a) explain the purpose, (b) explain course expectations, and (c) explain activity expectations. Facilitation strategies include the following: (a) approach non-participants, (b) assume an encouraging demeanor, (c) grade on participation, (d) walk around the room, (e) invite questions, (f) develop a routine, (g) design activities for participation, and (h) use incremental steps. Four of the strategies emerged from our analysis and were previously unstudied in the context of student resistance. Conclusions: The findings of this study have practical implications for instructors wishing to implement active learning. There is a variety of strategies to reduce student resistance to active learning, and there are multiple successful ways to implement the strategies. Importantly, effective use of strategies requires some degree of intentional course planning. These strategies should be considered as a starting point for instructors seeking to better incorporate the use of active learning strategies into their undergraduate engineering classrooms.

Background: Calls for the reform of education in science, technology, engineering, and mathematics (STEM) have inspired many instructional innovations, some research based. Yet adoption of such instruction has been slow. Research has suggested that students' response may significantly affect an instructor's willingness to adopt different types of instruction. Purpose: We created the Student Response to Instructional Practices (StRIP) instrument to measure the effects of several variables on student response to instructional practices. We discuss the step-by-step process for creating this instrument. Design/Method: The development process had six steps--item generation and construct development, validity testing, implementation, exploratory factor analysis, confirmatory factor analysis, and instrument modification and replication. We discuss pilot testing of the initial instrument, construct development, and validation using exploratory and confirmatory factor analyses. Results: This process produced 47 items measuring three parts of our framework. Types of instruction separated into four factors (interactive, constructive, active, and passive); strategies for using in-class activities into two factors (explanation and facilitation); and student responses to instruction into five factors (value, positivity, participation, distraction, and evaluation). Conclusions: We describe the design process and final results for our instrument, a useful tool for understanding the relationship between type of instruction and students' response.

The diversity of engineering education research provides an opportunity for cross-fertilisation of ideas and creativity, but it also can result in fragmentation of the field and duplication of effort. One solution is to establish a standardised taxonomy of engineering education terms to map the field and communicate and connect research initiatives. This report describes the process for developing such a taxonomy, the EER Taxonomy. Although the taxonomy focuses on engineering education research in the United States, inclusive efforts have engaged 266 individuals from 149 cities in 30 countries during one multiday workshop, 7 conference sessions, and several other virtual and in-person activities. The resulting taxonomy comprises 455 terms arranged in 14 branches and 6 levels. This taxonomy was found to satisfy four criteria for validity and reliability: (1) keywords assigned to a set of abstracts were reproducible by multiple researchers, (2) the taxonomy comprised terms that could be selected as keywords to fully describe 243 articles in 3 journals, (3) the keywords for those 243 articles were evenly distributed across the branches of the taxonomy, and (4) the authors of 31 conference papers agreed with 90% of researcher-assigned keywords. This report also describes guidelines developed to help authors consistently assign keywords for their articles by encouraging them to choose terms from three categories: (1) context/focus/topic, (2) purpose/target/motivation, and (3) research approach.

The diversity of engineering education research provides an opportunity for cross-fertilization of ideas and creativity, but it also can result in fragmentation of the field and duplication of effort. One solution is to establish a standardized taxonomy of engineering education terms to map the field and communicate and connect research initiatives. This report describes the process for developing such a taxonomy, the EER Taxonomy. Although the taxonomy focuses on engineering education research in the United States, inclusive efforts have engaged 266 individuals from 149 cities in 30 countries during one multiday workshop, seven conference sessions, and several other virtual and in-person activities. The resulting taxonomy comprises 455 terms arranged in 14 branches and six levels. This taxonomy was found to satisfy four criteria for validity and reliability: (1) keywords assigned to a set of abstracts were reproducible by multiple researchers, (2) the taxonomy comprised terms that could be selected as keywords to fully describe 243 articles in three journals, (3) the keywords for those 243 articles were evenly distributed across the branches of the taxonomy, and (4) the authors of 31 conference papers agreed with 90% of researcher-assigned keywords. This report also describes guidelines developed to help authors consistently assign keywords for their articles by encouraging them to choose terms from three categories: (1) context/focus/topic, (2) purpose/target/motivation, and (3) research approach.

Background: Ample research provides evidence about the influence of effective teaching practices on student success. Yet the adoption of such practices has been slow at many institutions. Efforts to bridge the gap between research and practice are needed. Purpose: We describe an institutional change plan we developed to bridge this research-to- practice gap. Our plan is grounded in research and theories about faculty motivation and organizational change, and we designed it using local evidence from the University of Michigan College of Engineering. Design/Method: We collected local data from three sources to provide context for our institutional change plan. First, faculty focus groups allowed us to determine factors that influence faculty adoption of effective teaching practices. Second, classroom observations allowed us to ascertain current teaching practices. Third, a student survey allowed us to identify teaching practices perceived by students to enhance their success. We used this local evidence with a "who/what/how" decision-making process to design our change plan. Results: Our institutional change plan for accelerating the adoption of effective teaching practices comprises a faculty action plan and an administrative change plan. Although still evolving, there is evidence of the success of both parts. Conclusions: Local evidence is critical in our change plan. Change agents wishing to bridge the research-to-practice gap at their own institutions can design a plan that adapts our process and integrates relevant research and theory with their own local data.

Defined as the systematic study of teaching and learning made public, scholarship of teaching and learning (SoTL) has drawn increasing attention from faculty members and institutions in recent years, perhaps as a response to rising demands for accountability and evidence-based teaching practices. However, the process of setting up a SoTL project, carrying it out, and converting the resultant data into meaningful findings can keep even the most accomplished faculty from embarking on the tas--or, if they do, succeeding at it. Many have described the reward-based challenges of facilitating SoTL in a research university. However, other challenges are less frequently identified. The Investigating Student Learning (ISL) program began at the University of Michigan in 2008 to fund faculty and faculty/postdoc/graduate-student teams to pursue SoTL research on courses and curricula. In designing a comprehensive program to support this activity, the authors drew on the experiences of other campuses' SoTL initiatives. The ISL program has developed a number of structures to facilitate the process for SoTL researchers, adapting practices that have been effective at other institutions and consolidating them into one grant program. The authors discuss these features, other university models for them, and the reasons they were developed. (Contains 2 tables and 11 resources.)

The Two Survey Method (TSM) is a new time-efficient tool for gathering formative student feedback. Based on the Delphi technique, the TSM uses iterative surveys to develop student consensus about key strengths and suggestions for instruction. Evaluation data indicate that both faculty and students are satisfied with the method's efficiency and the data captured. Potential uses for the TSM include assessing courses that have little time for collecting student feedback or supplementing existing feedback to check reliability or develop actionable strategies. (Contains 1 table and 2 footnotes.)

The authors describe three initiatives designed to increase the academic achievement and retention of historically underrepresented students (including females and underrepresented students of color) in engineering. (Contains 2 tables.)

Academic dishonesty (cheating) has been prevalent on college campuses for decades, and the percentage of students reporting cheating varies by college major. This study, based on a survey of 643 undergraduate engineering majors at 11 institutions, used two parallel hierarchical multiple regression analyses to predict the frequency of cheating on exams and the frequency of cheating on homework based on eight blocks of independent variables: demographics, pre-college cheating behavior, co-curricular participation, plus five blocks organized around Ajzen's Theory of Planned Behavior (moral obligation not to cheat, attitudes about cheating, evaluation of the costs and benefits of cheating, perceived social pressures to cheat or not to cheat, and perceived effectiveness of academic dishonesty policies). The final models significantly predict 36% of the variance in "frequency of cheating on exams" and 14% of the variance in "frequency of cheating on homework". Students don't see cheating as a single construct and their decisions to cheat or not to cheat are influenced differently depending on the type of assessment. Secondary findings are that a student's conviction that cheating is wrong no matter what the circumstances is a strong deterrent to cheating across types of assessment and that a student who agrees that he/she would cheat in order to alleviate stressful situations is more likely to cheat on both exams and homework.

We developed an instructional development workshop for science. technology. engineering, and math (STEM) instructors in higher education to promote their adoption of active learning. Our workshop design was based on a proposed framework for motivating adult learners consisting of five elements: (1) expertise of presenters, (2) relevance of content, (3) choice in application, (4) praxis. and (5) group work. We assessed the participating instructors' attitudes (i.e. motivation to use active learning and intentions and motivation to use strategies to reduce student resistance to active learning) immediately before and after the workshop and again five to six months later. We also assessed participants' satisfaction with the workshop. Analyses of our data provided evidence of a change in participants' motivation to use active learning and both their intentions and motivation to use strategies to reduce student resistance to active learning following the workshop. Our quantitative findings and thematic analysis of survey results support the use of the proposed framework for designing instructional development workshops for STEM faculty. The results also show short-term instructional development workshops can be effective and suggest caution in extrapolating immediate post-workshop assessment to the longer-te rm. [ABSTRACT FROM AUTHOR]

Implantable antitachycardia devices rely upon schemes for detecting cardiac arrhythmias which utilize rate and its variations; yet rate parameters often identify nonpathologic tachycardias as potentially dangerous and deliver unwarranted therapy. I have developed a predictive filter based upon the time-sequenced adaptive algorithm to be used as a supplement to rate criteria for detecting and identifying serious arrhythmias. The method does not require a fixed template and is independent of a priori patient information. The algorithm also provides arrhythmia diagnosis immediately at the change in rhythm. Algorithmic parameters were determined based upon a training set of patient data, and performance of the technique was evaluated with a completely new test set of 20 arrhythmia passages. The new algorithm yielded a sensitivity and specificity for ventricular tachycardia of 91% and 82% and for ventricular fibrillation of 71% and 93%. Correlation waveform analysis was used to diagnose the same test set of arrhythmias. It yielded a sensitivity and specificity for ventricular tachycardia of 100% and 67% and for ventricular fibrillation of 50% and 100%.