Conference Session

ERM Technical Session 19: Thinking about the Engineering Curriculum

Collection

2019 ASEE Annual Conference & Exposition

Authors

Marina Miletic, University of New Mexico; Vanessa Svihla, University of New Mexico; Jamie Gomez, University of New Mexico; Eva Chi, University of New Mexico; Sang M. Han, University of New Mexico; Catherine Anne Hubka, University of New Mexico; Yan Chen, University of New Mexico; Sung "Pil" Kang, University of New Mexico; Abhaya K. Datye, University of New Mexico

Tagged Divisions

Educational Research and Methods

, and conclusions or recommendations expressed inthis material are those of the authors and do not necessarily reflect the views of the NationalScience Foundation.References[1] G. M. Rogers and J. K. Sando, “Stepping Ahead: An Assessment Plan Development Guide,”Rose-Hulman Institute of Technology, Terre Haute, Indiana, 1996.[2] M. J. Allen, Assessing Academic Programs in Higher Education. John Wiley & Sons, 2007.National Academy of Engineering Committee on the Engineer of 2020 Phase I, “The engineer of2020: Visions of engineering in the new century,” National Academy of Engineering,Washington, D.C., 2004.[3] T. Curran, C. Doyle, E. Cummins, K. McDonnell, and N. Holden, “Enhancing the first yearlearning experience for biosystems engineering

Conference Session

Teaching and Learning in Online Environments

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

Javeed Kittur, Arizona State University; Jennifer M. Bekki, Arizona State University; Samantha Ruth Brunhaver, Arizona State University

Tagged Divisions

Educational Research and Methods

-sampling and down-sampling strategies depending on the class. SMOTE creates syntheticcases for a minority class by randomly selecting the nearest neighbors. Once we are satisfied withthe dataset itself, the features selected from the random forest output will be ultimately combinedwith associative classification to discover relationships between student-LMS interactions andpersistence decisions.AcknowledgementsThis paper is based on research supported by the National Science Foundation (NSF) under AwardNumber 1825732. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the NSF.References1. Seaman, J. E., Allen, I. E., & Seaman, J. (2018

Conference Session

Instruments and Methods for Studying Student Experiences and Outcomes

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

David Reeping, Virginia Polytechnic Institute and State University; Cherie D. Edwards, Virginia Commonwealth University

Tagged Divisions

Educational Research and Methods

, quantitative data collected from initial drafts of our survey instruments were incorporated into the instructor interviews. Instructors were allowed to see this student response during the interview and were asked to reflect on and interpret this numerical data.” [50, p. 15]This method of integration could be represented in the mixed column and explicitly referencednear the end of the design as shown in Figure A1 in Appendix A. They could also refer to such aprocess as blending across strands [2] as they used one type of data to elicit additional data aselaboration.While Shekar et al. [50] showed how one could situate their study as a methodologicalcontribution, a component of Faber and Benson [32] we would like to highlight is the idea

Conference Session

Approaches to Curriculum and Policy

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

Venugopalan Kovaichelvan, TVS Institute for Quality and Leadership ; Calvin Sophistus King Ph.D., Dr. Mahalingam College of Engineering and Technology

Tagged Divisions

Educational Research and Methods

Conference Session

Knowing our Students, Part 1

Collection

2007 Annual Conference & Exposition

Authors

Gary Lichtenstein, Stanford University; Heidi Loshbaugh, Colorado School of Mines; Brittany Claar, Colorado School of Mines; Tori Bailey, Stanford University; Sheri Sheppard, Stanford University

Tagged Divisions

Educational Research and Methods

major in Chemical Engineering. Many of the seventeen students weinterviewed expressed a definite disinterest in pursuing Chemical Engineering, based on theirexperiences in college chemistry. Interestingly, this choice is not reflective of the quality ofteaching; a number of students who made this assertion praised their chemistry professor andclaimed that it was their own inability to visualize the material that made it an unattractive coursefor them.MT has recently introduced a biological engineering minor and a humanitarian engineeringminor. A third, long-standing minor option is in public policy, although students must apply tothe program in the fall semester of their first year to be accepted; many students who mightgravitate toward the

Conference Session

Problem Solving and Misconceptions

Collection

2008 Annual Conference & Exposition

Authors

Thomas Litzinger, Pennsylvania State University; Carla Firetto, Pennsylvania State University; Lucas Passmore, Pennsylvania State University; Peggy Van Meter, Pennsylvania State University; Kelli Higley, Pennsylvania State University; Christine B. Masters, Pennsylvania State University; Francesco Costanzo, Pennsylvania State University; Gary L. Gray; Stephen Turns, Pennsylvania State University; Jonna Kulikowich

Tagged Divisions

Educational Research and Methods

Conference Session

Retention and Persistence in Engineering

Collection

2013 ASEE Annual Conference & Exposition

Authors

Maria-Isabel Carnasciali, University of New Haven; Amy E Thompson, University of New Haven; Terance Joshua Thomas, University of New Haven

Tagged Divisions

Educational Research and Methods

instrument deployedby Walstrom et al. 24 Questions pertaining to demographics, parents’ education, and recollectionof desire to study engineering were added to the instrument. A combination of multiple choiceand open-ended questions were used. In addition, questions were customized to reflect thechoices available at UNH. (Refer to Appendix A for complete survey tool questions; note thatthe questions in the appendix appear numbered to facilitate analysis – the actual tool did not havequestions numbered.) The survey was approved by the University’s Institutional Review Board.The on-line application Survey Monkey® was used to deploy and collect the data. Emailinvitations with unique links were sent out to 235 full-time engineering undergraduates

Conference Session

Novel Pedagogies 2

Collection

2013 ASEE Annual Conference & Exposition

Authors

James A. Kaupp, Queen's University; Brian M Frank P.Eng., Queen's University; Ann Shih-yi Chen, Queen's University

Tagged Divisions

Educational Research and Methods

information, considering implicationsand reflective evaluation of assumptions displayed by the experimental group in the post-test wassimilar to the methodology covered by instruction and model eliciting activities the subjectsexperienced in APSC 100. The control group, having no explicit critical thinking instruction,displayed increased use of concepts and the beginnings of using supplemental information toinform their conclusions. But, similar to the experimental group pre-test, did not begin toconsider the credibility or quality of the supplemental information.These observed differences may also be attributed to the varying educational backgrounds thedifferent groups may posses, or the differences in individual experiences during the semester. Asa

Conference Session

Novel Pedagogies 1

Collection

2013 ASEE Annual Conference & Exposition

Authors

Arthur C Heinricher, Worcester Polytechnic Institute; Paula Quinn, Quinn Evaluation Consulting; Richard F. Vaz, Worcester Polytechnic Institute; Kent J Rissmiller, Worcester Polytechnic Institute

Tagged Divisions

Educational Research and Methods

fulltime on project advising. Furthermore, both students and advisorsapply competitively to participate. It is reasonable to expect that a great deal of the differencesbeing seen between on-campus and off-campus project impact can be attributed to those factors,rather than simply to the location of the project.The changes over time are more difficult to interpret with confidence. For example, anincreasing trend (as seen in Figure 1) could reflect changes in the program over time or decay inthe impact of the program with passing time. We expect that the positive trend for questionsrelated to cultural awareness (Figure 1) is related to the increased availability of and emphasis on

Conference Session

ERM Potpourri

Collection

2013 ASEE Annual Conference & Exposition

Authors

Erick Jacob Nefcy, Oregon State University; Audrey Briggs Champagne, University at Albany, SUNY; Milo Koretsky, Oregon State University

Tagged Divisions

Educational Research and Methods

. Any opinions,findings, and conclusions or recommendations expressed in this material are those of the authorsand do not necessarily reflect the views of the National Science Foundation.IX. References[1] Koretsky, M.D., Amatore, D., Barnes, C., & Kimura, S. (2008). Enhancement of student learning in experimental design using a virtual laboratory. IEEE Transactions on Education, 51(1), 76–85.[2] Koretsky, M.D., Kelly, C. & Gummer, E. (2011). Student Perceptions of Learning in the Laboratory: Comparison of Industrially-situated Virtual Laboratories to Capstone Physical Laboratories. Journal of Engineering Education, 100(3), 540–573.[3] Koretsky, M.D., Kelly, C. & Gummer, E. (2011). Student Learning in

Conference Session

Thinking About the Engineering Curriculum

Collection

2012 ASEE Annual Conference & Exposition

Authors

Olga Pierrakos, James Madison University; Anna Zilberberg; Christopher W. Swan, Tufts University; Angela R. Bielefeldt, University of Colorado, Boulder; Kurt Paterson P.E., Michigan Technological University; John J. Duffy, University of Massachusetts, Lowell; Sean Mcvay, James Madison Univeristy

Tagged Divisions

Educational Research and Methods

reviews, (e)piloting the items to a small sample to ensure clarity, and (f) scrutinizing the self-report nature ofthe instrument. More specifically, pilotingthe survey with a group of LTS experts (N=5) and alsowitha group of LTS non-experts (N=5) enabled us to gain insight into the degree to whichresponses on the instrument reflected the faculty‟s actual knowledge of the construct of interestand to examine how the instrument functions across different population groups.Shortly prior to a two-day EFELTS LTS Experts Summit in September 2011, participantscompleted the LTS Faculty Survey online administered on the Qualtrics platform. Demographicinformation on the participants was collected, as well information regarding their positions attheir

Conference Session

Knowing our Students, Faculty, and Profession

Collection

2010 Annual Conference & Exposition

Authors

Samantha Brunhaver, Stanford University; Russell Korte, University of Illinois, Urbana-Champaign; Micah Lande, Stanford University; Sheri Sheppard, Stanford University

Tagged Divisions

Educational Research and Methods

Conference Session

Active and Inquiry-Based Learning

Collection

2011 ASEE Annual Conference & Exposition

Authors

Muhsin Menekse, Arizona State University; Glenda Stump, Arizona State University; Stephen J. Krause, Arizona State University; Michelene T.H. Chi, Arizona State University

Tagged Divisions

Educational Research and Methods

Explain/Elaborate Question-Answer zoning out Look/Attend Justify/Reason Reciprocal teaching Underline/Highlight Connect/Integrate Argue/Challenge Gesture/Point Answer Questions Collaborate Summarize Reflect/Predict Peer tutoring Paraphrase Self-monitor/Regulate Monitor/Feedback Manipulate tape Compare

Conference Session

Research in Engineering Education II

Collection

2012 ASEE Annual Conference & Exposition

Authors

Dana Denick, Purdue University, West Lafayette; Aidsa I. Santiago-Román, University of Puerto Rico, Mayaguez Campus; Ruth A. Streveler, Purdue University, West Lafayette; Natalie Barrett, Purdue University, West Lafayette

Tagged Divisions

Educational Research and Methods

, (c) StaticEquivalence, (d) Roller joint, (e) Pin-in-slot joint, (f) Loads at surfaces with negligible friction,(g) Representing loads at connections, (h) Limits on friction force, and (i) Equilibrium. Also,each problem had been carefully designed to identify conceptual errors or misconceptions,without the need for mathematical computation.6 Additionally, the developer of CATS, Dr.Steif, has identified a set of distinct errors that reflect known misconceptions exhibited bystudents based on his experience and occurrence in student documentation.9 Table 3 presents alist of these errors and their descriptions. Page 25.1457.4Table 3

Conference Session

Knowing Ourselves: Research on Engineering Education Researchers

Collection

2011 ASEE Annual Conference & Exposition

Authors

Xin (Cindy) Chen, Purdue University; Nikitha Sambamurthy, Purdue University; Corey M. Schimpf, Purdue University, West Lafayette; Hanjun Xian, Purdue University, West Lafayette; Krishna Madhavan, Purdue University, West Lafayette

Tagged Divisions

Educational Research and Methods

organization evolving within Del.icio.us (http://del.icio.us, referred to as“Delicious”, also http://www.delicious.com) and Flickr (http://www.flickr.com)20. It is aconflation of “folk” and “taxonomy.” Nowadays, folksonomy generally represents theassemblage of tags generated through tagging6,10,21. This paper is primarily concerned with thefolksonomy generated from weighted tagging, as tags themselves combined with the assignedweight and confidence will reflect core concepts. Additionally changes and patterns in thefolksonomy will reveal trends in engineering education research.In addition to the property discussed above, many other properties of folksonomies have beenuncovered. An important finding is that as more users tag a resource, these tags

Conference Session

Methodological & Theoretical Contributions to Engineering Education 2

Collection

2014 ASEE Annual Conference & Exposition

Authors

Corey T. Schimpf, Purdue University, West Lafayette; Joyce B. Main, Purdue University, West Lafayette

Tagged Divisions

Educational Research and Methods

each case to begrouped or clustered. The techniques then use one of the methods above, as reflected in differentsorting algorithms, to generate one or more clusters of related cases. It is used across many fieldsincluding education, engineering, and life, social, and physical sciences12,13,35,36 for manypurposes including verifying underlying group structures or as exploratory and data-miningmethods. This study applies a k-means cluster analysis, a well-established technique previouslyused in engineering education research, to identify clusters of institutions with different profilesthat have a greater or fewer number of family-related benefits. Past studies in engineeringeducation research have used k-means to develop skill and ability

Conference Session

Student Learning, Problem Solving, & Critical Thinking 1

Collection

2014 ASEE Annual Conference & Exposition

Authors

Youyi Bi, School of Mechanical Engineering, Purdue University; Tahira N. Reid, Purdue University, West Lafayette

Tagged Divisions

Educational Research and Methods

researchers arestarting to apply eye tracking technology in studying people’s problem solving process; e.g.,Madsen’s study of visual attention in physics problem solving [52].Madsen showed that when solving physics problems, both top-down and bottom-up processesare involved. The top-down processes are internal and determined by one’s prior knowledge andgoals. The bottom-up processes are external and determined by features of the visual stimulisuch as color and luminance contrast. Madsen’s study assumed that eye movements reflect aperson’s moment-to-moment cognitive processes, providing a window into one’s thinking. In aprevious study, the way correct and incorrect solvers viewed relevant and novice-like elements ina physics problem diagram were

Conference Session

Modeling Student Data

Collection

2010 Annual Conference & Exposition

Authors

Michael Dyrenfurth, Purdue University; Mike Murphy, Dublin Institute of Technology; Gary Bertoline, Purdue University

Tagged Divisions

Educational Research and Methods

to exercise considerable restraint in order to secure measures that actually represent the criterion – often very difficult to collect – instead of more easily accessed but potentially invalid proxy measures. For Page 15.1008.5 example, salary data of alumni would be a more easily secured proxy measure for alumni success than more direct measures of the latter. Clearly salary data, unless carefully conditioned, would reflect the large inequities and differential pay scales of varying careers. Data collection refers to the process and source of the actual numbers and descriptors being used in any assessment. Here it is

Conference Session

Student Recruitment and Retention

Collection

2008 Annual Conference & Exposition

Authors

Yvonne Ng, College of St. Catherine

Tagged Divisions

Educational Research and Methods

computer science is attainable, understandable and useful. 8PCM provides a way to frame the curriculum of each course in a major or minor. Instructors usethe parallels to determine the primary and secondary priorities which are then reflected in theevaluation and instructional activity design. Identifying priorities allows the instructor to beflexible and make changes “on the fly” if students lack assumed abilities or if they learn therequired concepts quickly and can handle more challenges.2.2 Objectives: Employment, Desire, FoundationPCM language clarifies the educational value of projects in a computer science curriculum withrespect to the objectives. The ability to work on projects develops employability becausestudents use, practice

Conference Session

Student Teams, Groups, and Collaborations

Collection

2016 ASEE Annual Conference & Exposition

Authors

Penny Kinnear, University of Toronto; Patricia Kristine Sheridan, University of Toronto; Greg Evans, University of Toronto; Doug Reeve, University of Toronto

Tagged Divisions

Educational Research and Methods

participate reflected the demographic of the Faculty, a purelyserendipitous occurrence. Of the 22 participants there were five students who were not visibleminorities in engineering, nine students who appeared to be English dominant and seven whowere female. None of the teams investigated in this paper consist of all monolingual Englishspeakers, and only one team, Team 4, consisted of all domestic students. The language diversityof the teams was representative of the University’s (and in particular the Faculty’s) linguisticdiversity. Given the demographics of the teams and the student population in this course, theprobability of having teams volunteer that did not have similar diversity to the student body wasminimal. The students’ motivations for

Conference Session

Retention

Collection

2017 ASEE Annual Conference & Exposition

Authors

Niranjan Hemant Desai, Purdue University Northwest; George Stefanek, Purdue University Northwest

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods

. Additionally, it was found thatstudents did not want an easy course; they were aware of the challenges that lay ahead them asengineers. However, they did enjoy the excitement that the course added to their curriculum,while preparing them for their future career. The feedback reflected student’s interest in thecourse and reinforced the strong and positive elements of the course’s structure.Improving math skills, Providing community-based support system: Weatherton et al.30 tried toincrease retention by providing freshman students with academic support services in calculus andbasic mathematics. They studied the retention and performance of incoming freshmen that wereinvolved in one of four freshman interest groups (FIG), called FORCES (Focus on

Conference Session

Knowing Our Students III

Collection

2006 Annual Conference & Exposition

Authors

Taryn Bayles, University of Maryland-Baltimore County; Claudia Morrell, University of Maryland-Baltimore County; Anne Spence, University of Maryland-Baltimore County

Tagged Divisions

Educational Research and Methods

program were accepted and from which new studentswere accepted to participate in the program based on the same criteria used for the originalselection of participants.Internship OpportunityThis program provides a paid internship experience for 48 students following the completion ofthirty credit hours in a STEM related field. Internships were provided in companies not currentlyhiring interns from UMBC to increase internship support and encourage the involvement of morebusinesses with UMBC and CCBC. UMBC’s Shriver Center provided leadership for this portionof the project.Assessment and EvaluationThe outcomes for Objective 2 are reflected in student retention in STEM majors, grades, andcommitment to careers in STEM. Attitudes toward STEM were

Conference Session

Student Learning

Collection

2010 Annual Conference & Exposition

Authors

Amani Salim, Purdue University; Heidi Diefes-Dux, Purdue University

Tagged Divisions

Educational Research and Methods