Conference Session

ERM Potpourri

Collection

2006 Annual Conference & Exposition

Authors

Nadia Kellam, University of South Carolina; Veronica Addison, University of South Carolina; Michelle Maher, University of South Carolina; Mann Llewellyn, University of Queensland; David Radcliffe, University of Queensland; Walter Peters, University of South Carolina

Tagged Divisions

Educational Research and Methods

a v e b e e n d i s

Conference Session

Fostering Student Learning

Collection

2011 ASEE Annual Conference & Exposition

Authors

Stephen Snyder, Taylor University; Rachel Tomasik; Bethany Smith, Taylor University

Tagged Divisions

Educational Research and Methods

statistically significant difference between them in intrinsicmotivation (p .05 3: EXPERIENCED1: EVENT GROUP 2: NOVICE GROUP GROUP 4: EXPERT GROUP 1. Intrinsic 1. Intrinsic 1. Intrinsic1. Intrinsic Motivation Motivation* Motivation*** Motivation*** a. a. a. a.Contextualization* Contextualization Contextualization** Contextualization b. Curiosity* b. Curiosity b. Curiosity*** b. Curiosity*** c. Challenge

Conference Session

Student Attitudes and Perceptions

Collection

2009 Annual Conference & Exposition

Authors

Farrokh Attarzadeh, University of Houston; Deniz Gurkan, University of Houston; Miguel Ramos, University of Houston; Mequanint Moges, University of Houston; Victor Gallardo, University of Houston; Mehrube Mehrubeoglu, Texas A&M University, Corpus Christi; Reddy Talusani, Houston Community College System; Shruti Karulkar, University of Houston

Tagged Divisions

Educational Research and Methods

AC 2009-344: PERCEPTION OF UNDERGRADUATE FRESHMAN STUDENTS ONROLE MODELS AND CORRELATION WITH THEIR EDUCATIONBACKGROUNDFarrokh Attarzadeh, University of HoustonDeniz Gurkan, University of HoustonMiguel Ramos, University of HoustonMequanint Moges, University of HoustonVictor Gallardo, University of HoustonMehrube Mehrubeoglu, Texas A&M University, Corpus ChristiReddy Talusani, Houston Community College SystemShruti Karulkar, University of Houston Page 14.951.1© American Society for Engineering Education, 2009 Perception of Undergraduate Freshman Students on Role Models and Correlation with Their Educational BackgroundAbstractThis paper reports the latest

Conference Session

Graduate Education

Collection

2017 ASEE Annual Conference & Exposition

Authors

Marissa A. Tsugawa-Nieves, University of Nevada, Reno; Heather Perkins, North Carolina State University; Blanca Miller, University of Nevada, Reno; Jessica Nicole Chestnut, North Carolina State University; Cheryl Cass, North Carolina State University; Adam Kirn, University of Nevada, Reno

Tagged Divisions

Educational Research and Methods

Paper ID #18227The Role of Engineering Doctoral Students’ Future Goals on Perceived TaskUsefulnessMrs. Marissa A. Tsugawa-Nieves, University of Nevada, Reno Marissa Tsugawa-Nieves is a graduate research assistant studying at the University of Nevada, Reno in the PRiDE Research Group. She is currently working towards a Ph.D. in Engineering Education. She expects to graduate May of 2019. Her research interests include student development of identity and motivation in graduate engineering research and teaching environments. She is also interested in k-12 integration of engineering in math and science curricula.Heather Perkins

Conference Session

ERM Potpourri

Collection

2013 ASEE Annual Conference & Exposition

Authors

Denise Wilson, University of Washington; Cheryl Allendoerfer, University of Washington; Mee Joo Kim, University of Washington- Seattle; Elizabeth Burpee; Rebecca A Bates, Minnesota State University, Mankato; Tamara Floyd Smith P.E., Tuskegee University; Melani Plett, Seattle Pacific University; Nanette M Veilleux, Simmons College

Tagged Divisions

Educational Research and Methods

Paper ID #7645STEM Students outside the Classroom: The Role of the Institution in Defin-ing Extracurricular ActivityDr. Denise Wilson, University of Washington Denise Wilson received the B.S. degree in mechanical engineering from Stanford University in 1988 and the M.S. and Ph.D. degrees in electrical engineering from the Georgia Institute of Technology in 1989 and 1995, respectively. She also holds an M.Ed. from the University of Washington (2008) and has worked in industry (Applied Materials). She is currently a faculty member with the Electrical Engineering De- partment, University of Washington, Seattle, and she was

Conference Session

Knowing Our Students, Part 2

Collection

2007 Annual Conference & Exposition

Authors

Paul Santi, Colorado School of Mines

Tagged Divisions

Educational Research and Methods

pediments begins forming, the first set stopschanging in size. Revised instruction consisted of a short in-class group exercise, where studentswere asked to visualize a machine to make pediments and to describe what the machine had todo.Two concept tests were developed for testing before and after the revised instruction. Each testcontained a diagram and three to five multiple choice questions for each misconception. Theclass was divided into two groups: the first group was given test version A (reproduced as Figure1) as a pre-test and B (Figure 2) as a post-test, and the second group was given version B as a Page 12.798.3pre-test and A as a post

Conference Session

Assessment

Collection

2006 Annual Conference & Exposition

Authors

Joanne Mathews, Illinois Institute of Technology; Daniel Ferguson, Illinois Institute of Technology; Margaret Huyck, Illinois Institute of Technology; Abhinav Pamulaparthy, Illinois Institute of Technology

Tagged Divisions

Educational Research and Methods

reliability of the Fall 2005 LO post-test was higher than that of Page 11.240.3the pre-test, due to the removal of the questions from the assessment. The removal of questionswas based on Item Analysis, which indicated how the internal stability of the section wouldchange with respect to the removal of that specific question. The following question was asample question that was confusing to most of the students and was therefore removed from theassessment tool:A project manager makes a narrative description of the work that must be done for his/herproject. This is called a: a. Project plan b. Control chart c. Statement of work d. Project

Conference Session

Assessment and Evaluation in Engineering Education I

Collection

2007 Annual Conference & Exposition

Authors

Patrick Tebbe, Minnesota State University-Mankato; Stewart Ross, Minnesota State University-Mankato; Brian Weninger, Minnesota State University-Mankato; Sharon Kvamme, Minnesota State University-Mankato; Jess Boardman, Minnesota State University-Mankato

Tagged Divisions

Educational Research and Methods

use and comparisons between the different texts. Afocus group discussion was then held between the grant assessment coordinator, the courseinstructor, and the students concerning the different texts. Page 12.280.3 Table 1: Selection of textbook used in the student focus groupTextbook A Traditionally formatted text Periodic real world comments and examplesTextbook B Traditionally formatted text Greater use of real world specificsTextbook C Graphically formatted text Numerous real world aspects includedTextbook D Largely online text Limited real world commentsWith regard

Conference Session

Teaching and Learning Strategies II

Collection

2015 ASEE Annual Conference & Exposition

Authors

David Reeping, Ohio Northern University; Kenneth J Reid, Virginia Tech

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods

Measure of Similarity Classified Course A AND / OR Classified Course B Quantitative By Outcome Summary Figure 4: Depiction of the Function of the Mathematical Model Page 26.795.7Use of the Proposed Methodology Thus FarIdentify System to ModelClearly, the first step in

Conference Session

Student Success III: Affect and Attitudes

Collection

2016 ASEE Annual Conference & Exposition

Authors

Ryan R. Senkpeil, Purdue University, West Lafayette; Edward J. Berger, Purdue University, West Lafayette

Tagged Divisions

Educational Research and Methods

first year GPA to ascertain the predictive power of cognitive factors alone.  Non-Cognitive Model: This model added seven non-cognitive factors on top of the two cognitive factors. These additional variables were regressed onto students’ composite first year GPA to discover if non-cognitive factors predict first year GPA better than the model with cognitive factors alone.In the Cognitive-Only Model high school GPA and standardized test score predicted a significantamount of variance in first year GPA (F(2,327) = 10.60, p < .001). Also, high school GPA (b =.40, SE = .11, p < .001; β = .19) and standardized test score (b = .02, SE = .007, p < .05; β = .11)were both significant individual predictors in the

Conference Session

Misconceptions

Collection

2013 ASEE Annual Conference & Exposition

Authors

Karla L. Sanchez, Purdue University; Alejandra J. Magana, Purdue University, West Lafayette; David Sederberg, Purdue University; Grant P Richards, Purdue University, West Lafayette; M. Gail Jones, NC State University; Hong Z Tan, Purdue University

Tagged Divisions

Educational Research and Methods

methods to: (a) leverage the understanding of complex phe- nomena in science and engineering and (b) support scientific inquiry learning and innovation. Specific efforts focus on studying cyberinfrastructure affordances and identifying how to incorporate advances from the learning sciences into authoring curriculum, assessment, and learning materials to appropriately support learning processes.Dr. David Sederberg, Purdue UniversityDr. Grant P Richards, Purdue University, West Lafayette Dr. Grant P. Richards is a Clinical Assistant Professor in Electrical and Computer Engineering Technology at Purdue University. His research focuses on learning styles and visual learning tools.Dr. M. Gail Jones, NC State University Gail

Conference Session

Studies of Classroom Assessment: Exam Wrappers, Equitable Grading, Test Anxiety, and Use of Reflection

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Soheil Fatehiboroujeni, Cornell University; Matthew Jordan Ford, Cornell University; Hadas Ritz, Cornell University; Elizabeth Mills Fisher, Cornell University

Tagged Divisions

Educational Research and Methods

choicelearning assessments in both courses to participate in a think-aloud study. We incentivized theirparticipation with a small financial reward. We combined selected questions from the twolearning assessments so as to (a) develop a combined assessment that can be finished in athink-aloud interview within an hour and (b) have all the key topical areas in each coursecovered in the new subset. The original fluid mechanics and mechanics of material assessmentsin 2019 had 36 and 20 problems, respectively. In 2020 we combined 11 of the fluids with 19 ofthe solids problems into one assessment for the think-aloud studies. Two researchers in our team each conducted four interviews for a total of eight. To definea measure of how our participants

Conference Session

Fostering Student Learning

Collection

2011 ASEE Annual Conference & Exposition

Authors

Gerald Sullivan, Virginia Military Institute; James C. Squire, Virginia Military Institute; George Mercer Brooke IV, Virginia Military Institute,Department of Physics and Astronomy

Tagged Divisions

Educational Research and Methods

I). Three types of questions were included on the quiz, in order toassess students Objective and Subjective comprehension of the material, as well as their self- Page 22.1453.4assessed enjoyment/interest in the material:Objective Comprehension: The quiz included 7 questions aimed at assessing how wellstudents learned concepts from the lecture concerning spring mass damper systems. Forexample:“7. A new car design tends to ride too “rough”, meaning on bad roads the passenger cab vibrates too much. What parts might need to be redesigned to fix this? a) The dampers and the springs. They are interrelated. b) Only the dampers

Conference Session

Concept Inventories and Assessment of Knowledge

Collection

2015 ASEE Annual Conference & Exposition

Authors

Carli Denyse Flynn, Syracuse University; Cliff I. Davidson, Syracuse University; Sharon Dotger; Meredith Sullivan

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods

Graduated cylinders 2 Q8c Ice blocks 4 HECI Q9c Cool tea 2 Q10c Sponge dye 2 a. Question 1a scored with a 2 point value (1 point for each numerical value and units) b. Question modified from Carlson et al. (2002)17 c. Questions taken with permission from the HECI18Administration The instrument was administered in the Spring 2014 semester in a sophomore civil andenvironmental engineering class of 78 students (57 civil engineers, 15 environmental engineers,4 other). The average GPA at the beginning of course

Conference Session

Curricular Innovations 1

Collection

2017 ASEE Annual Conference & Exposition

Authors

Mark J Indelicato, Rochester Institute of Technology (CAST); Miguel Bazdresch, Rochester Institute of Technology (CAST); George H Zion, Rochester Institute of Technology (CAST); Joseph (Yossi) Nygate, Rochester Institute of Technology (CAST); Surabhi M Sarda, Rochester Institute of Technology (COE)

Tagged Divisions

Educational Research and Methods

paired with anincorrect explanation, indicates the student guessed. This is identified as “Scenario 2”. Incorrect“yes” or “no’ responses with incorrect “why” responses indicates “no understanding” as isidentified as “Scenario 3”. Instances of misunderstanding, guessing and no understanding areidentified. Each part of the question is assigned a metric or maximum point total. The scores foreach part are summed and represent the total score for that question. Each question had a total of5 points, resulting in a total of 15 points for all three questions. For each of the pre- and post-instruction surveys, the following data is collected: • Individual question scores for each student – Parts A and B individually • Individual question scores

Conference Session

Novel Pedagogies 2

Collection

2013 ASEE Annual Conference & Exposition

Authors

Liu Junhua, Singapore University of Technology and Design; Yue Zhang, Singapore University and Technology and Design; Justin Ruths, Singapore University of Technology and Design; Diana Moreno, Singapore University of Technology and Design (SUTD); Daniel D. Jensen, U.S. Air Force Academy; Kristin L. Wood, Singapore University of Technology and Design (SUTD)

Tagged Divisions

Educational Research and Methods

course inventory topics,Figure 7 shows exemplar practical exercises included as part of the pre-reading materials, andFigure 8 shows sample codes as part of the pre-reading materials that express OOP concepts. (b)  Summaries  of  key  concepts  integrated  in  the  peer-­‐to-­‐ (a)  Underlined  important  OOP  concepts peer  pre-­‐reading  materials (c)  Theoretical  explanations Figure 6. Exemplar excerpts of pre-reading materials (Sierra, 2005; Weisfeld, 2009

Conference Session

Assessment and Research Tools

Collection

2018 ASEE Annual Conference & Exposition

Authors

Carey Whitehair; Catherine G.P. Berdanier, Pennsylvania State University, University Park

Tagged Divisions

Educational Research and Methods

, or ethnicity. This one-time collection of data resulted in post-dates ranging from2010 to 2017. There was also a limited number of posts by the same users. If these posts weresimply the same post within separate subreddits, one of them was excluded. Otherwise they weregrouped with the previous posts by that user in chronological order.This method of data collection is also easily repeatable and may be extended to other forums.Because the forum is publicly available, it doesn’t require IRB approval. The search can also beextended to look at non-STEM programs, or even for different search criteria unrelated to graduatestudent attrition. B. Overcoming LimitationsAs with all methods, there are some limitations to the unique approach used in

Conference Session

Problem Solving, Adaptive Expertise, and Social Engagement

Collection

2018 ASEE Annual Conference & Exposition

Authors

Oenardi Lawanto, Utah State University; Angela Minichiello P.E., Utah State University; Jacek Uziak, University of Botswana; Andreas Febrian, Utah State University

Tagged Divisions

Educational Research and Methods

students’ knowledge about the task-related discipline(s) [24], [25]. In thisstudy, we only focus on the implicit and explicit aspect of task interpretation. This study views task interpretation as an integral part of self-regulation. Self-regulatedlearning (SRL) is a complex, iterative, and situated goal-directed learning process [5], [8], [26].SRL is comprised by the student, learning environment, and learner’s engagement with theenvironment and is affected by student’s emotion and motivation [7], [9], [26]. Student’sengagement starts with task interpretation. Task interpretation is followed by (a) developing aplan based on the task understanding, (b) enacting the plan, (c) monitoring the progress andapproach, and (d) making any

Conference Session

ERM Technical Session 7: Learning and Research in Makerspaces

Collection

2019 ASEE Annual Conference & Exposition

Authors

Colin Dixon, Concord Consortium; Lee Michael Martin, University of California, Davis

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods

each day.Participants & the Class Portrait ProjectFifteen students, ages 14 to 16, at a public high school participated in the maker club – 7 boys, 7girls, and 1 gender non-binary. The club demographics reflected those of the school as a whole –5 African-American, 3 Latinx, 3 White, and 4 multiracial. Most students were from low tomiddle income families. In this paper, we focus on the work of one group, in which there werethree young women -- Casey, Deonne and B -- and one young man -- B’s brother Isaiah.Three members of the group – Casey, Deonne, and Bi – shared a homeroom, and decided tocreate a light-up Class Portrait. The portrait as initially envisioned would include a photo of allstudents in the class and use LEDs embedded in the

Conference Session

Instrument Development

Collection

2017 ASEE Annual Conference & Exposition

Authors

Sixing Lu, University of Arizona; Loukas Lazos, University of Arizona; Roman Lysecky, University of Arizona

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods

Collaborative Learning Space.The class consists of three main components: (a) reading assignments using the zyBooks onlineinteractive book platform [15], (b) 75 minutes in-class sessions held twice a week, and (c) a3-hour lab held weekly. Students are requested to complete a set of participation and challengequestions before every in-class session. These are automatically graded through the zyBooksplatform. The in-class time is structured as a sequence of active-learning tasks, and lecturing/demonstration periods. The administration of the activities is assisted by preceptors (teachingassistants and undergraduate learning assistants that have previously taken the course). A typicaldistribution of the instructors' and students’ activities during a 75

Conference Session

Faculty Perspectives of Active Learning, Inequity, and Curricular Change

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Steven Santana, Harvey Mudd College

Tagged Topics

Diversity

Tagged Divisions

Educational Research and Methods

. [Accessed: 06- Mar-2021].[4] R. Miller and B. Linder, “Is Design Thinking the New Liberal Arts of Education?,” 2015.[5] A. F. McKenna, “Adaptive Expertise and Knowledge Fluency in Design and Innovation,” in Cambridge Handbook of Engineering Education Research, A. Johri and B. M. Olds, Eds. Cambridge: Cambridge University Press, 2014, pp. 227–242.[6] M. J. Safoutin, “A methodology for empirical measurement of iteration in engineering design processes,” Citeseer, 2003.[7] A. F. McKenna, J. E. Colgate, G. B. Olson, and S. H. Carr, “Exploring Adaptive Expertise as a Target for Engineering Design Education,” in Volume 4c: 3rd Symposium on International Design and Design Education, 2006, vol. 2006, pp

Conference Session

Assessment II: Learning Gains and Conceptual Understanding

Collection

2016 ASEE Annual Conference & Exposition

Authors

Benjamin James Call, Utah State University; Wade H Goodridge, Utah State University; Thayne L Sweeten Ph.D., Utah State University

Tagged Divisions

Educational Research and Methods

“well-behaved”, inferring requirements like normality.Unfortunately, real-world data is often not normal – particularly real-world, academic,standardized test data14-17. In the data collected for this study, one subset of participants seemedto score especially high on the PSVT:R. This led to the observation by researchers of a potentialceiling effect. “Ceiling effect” is the term used to describe the situation when many participantsobtain a maximum score18. This is a type of censoring – where censored data occurs when thereis a lower bound, a; an upper bound, b; or a situation with bounds a and b19. Kruskal and Tanur19point out that censored data will result in sample means and standard deviations that are poorpredictors of the population mean

Conference Session

Motivation, Attitudes, and Beliefs

Collection

2018 ASEE Annual Conference & Exposition

Authors

Allison Adams, Kansas State University; Amy Rachel Betz, Kansas State University; Emily Dringenberg, Ohio State University

Tagged Divisions

Educational Research and Methods

to deeply understand how these reactions are tied to theirbeliefs about intelligence. Some examples include, “How do you feel when someone else, whoyou do not think is as smart as you, does better on an exam?” and “How do you feel when youdid better than someone you know on an exam, who you think of as considerably smarter than 5you are?” In addition, we will continue to probe the students to clarify what specific wordsand/or terms that they use mean to them. For example, we could clarify what does “low” mean toeach student. Is it below the class average? Failing? Getting a ‘B’?We also found that more follow-up was needed on the breakdown of

Conference Session

Institutional Change

Collection

2018 ASEE Annual Conference & Exposition

Authors

Medha Dalal, Arizona State University; Adam R. Carberry, Arizona State University

Tagged Divisions

Educational Research and Methods

and Betty. Kyle is a Professor of Engineering and Betty is a Professor Emeritawithin Education. Kyle and Betty worked together for 13 years on four funded projects that hasresulted in six journal articles and 44 conference publications. Their research has examinedimproving classroom teaching through the use of real world examples, frequent formativefeedback, professional development of K-12 teachers, and a variety of technological tools. Theresults of their work have innovated the education of engineering at all levels to provide a moreactive and engaging experience for students.Team B: Henry and Janelle. Henry and Janelle work together within an NSF EngineeringResearch Center. Henry is an Assistant Professor of Engineering. He has worked

Conference Session

Data-informed Approaches to Understanding Student Experiences and Outcomes

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

John Chen, California Polytechnic State University, San Luis Obispo; Jenna Michelle Landy, California Polytechnic State University, San Luis Obispo; Matthew Scheidt, Purdue University, West Lafayette; Justin Charles Major, Purdue University, West Lafayette; Julianna Ge, Purdue University, West Lafayette; Camaryn Elizabeth Chambers, California Polytechnic State University, San Luis Obispo; Christina Grigorian; Michelle Kerfs, California Polytechnic State University, San Luis Obispo; Edward J. Berger, Purdue University, West Lafayette; Allison Godwin, Purdue University, West Lafayette; Brian P. Self, California Polytechnic State University, San Luis Obispo; James M. Widmann, California Polytechnic State University, San Luis Obispo

Tagged Divisions

Educational Research and Methods

, associated with academic performance as measured by self-reported, overallgrade-point-average (GPA). We seek to explore this association in more detailed and nuancedways to assess whether (a) cluster membership is truly unassociated with academic performance,or (b) one or more clusters is associated with differential academic performance. If the finding isthe latter, the results would naturally suggest the need for interventions to support those studentswhose profiles may predict poor academic outcomes. Despite this paper’s focus on academicperformance as the measure of success, we acknowledge that achievement or thriving byundergraduate engineering students cannot be simply measured by GPA when many otherfactors are at play. This study is necessary

Conference Session

Innovative Pedagogies Afforded Through Technology and Remote Learning

Collection

2021 ASEE Virtual Annual Conference Content Access

Authors

Manjina Shrestha, Georgia Institute of Technology

Tagged Divisions

Educational Research and Methods

of AR for class practical. Source:(Bazarov, Kholodilin, Nesterov, & Sokhina, 2017), (B) An orthographic projection of a 3D model. Source: (Abekani 2018)[26] developed an AR app to help engineering students of electrical and technological specialtiesperform lab exercises. The app helps the faculty provide explanations conveniently at a differentphase of the lab and an economic substitution of lab assistant (Figure 1). The app provides anoverlay of 3D models in the context of the equipment figure but does not provide any interactionand is mainly suitable only for visual information related to the context of the environment.A quick search on the Google Play store presents only a handful of AR apps in engineering

Conference Session

Student Learning, Problem Solving, and Critical Thinking 1

Collection

2014 ASEE Annual Conference & Exposition

Authors

K. Jo Min, Iowa State University; John Jackman, Iowa State University; Jason C.K. Chan

Tagged Divisions

Educational Research and Methods

., Pintrich and de Groot15), the relationship betweenvisual models and enhanced self-efficacy needs to be further investigated.MethodologyWe conducted a randomized study as follows. A problem solving session for inventorycontrol theory was designed for junior level undergraduate industrial engineering majors. Wealso conducted pre- and post- self-efficacy surveys on students’ abilities regarding thespecific domain knowledge aspects of inventory control theory.ParticipantsStudents in the class were divided randomly into 2 groups, A and B. In Group A, 44 studentscompleted the problems and in Group B, 42 students completed the problems. Both groupshad originally been designed for 45 students each, but last-minute sickness, etc., led to lessthan 100

Conference Session

Classroom Practice I: Active and Collaborative Learning

Collection

2016 ASEE Annual Conference & Exposition

Authors

Dawn Laux, Purdue University, West Lafayette; Andrew Jackson, Purdue University, West Lafayette; Nathan Mentzer, Purdue University, West Lafayette

Tagged Divisions

Educational Research and Methods

Higher Education, 45, 115-138.[11] Laux, D., Luse, A., & Mennecke, B. (2016). Collaboration, connectedness, and community: An examination of the factors influencing student persistence in virtual communities. Computers in Human Behavior, 57, 452-464.[12] Nielsen, J. (1993) Usability Engineering. San Francisco: Morgan Kaufmann[13] Brooke, J. (1996). SUS: A quick and dirty usability scale. In: P.W. Jordan, B. Thomas, B.A. Weerdmeester & I.L. McClelland (Eds.), Usability Evaluation in Industry. London: Taylor & Francis.[14] Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An Empirical Evaluation of the System Usability Scale. International Journal of Human-Computer Interaction, 24(6), 574-594.[15] Kortum, P. T., &

Conference Session

ERM Technical Session 6: Technology-enhanced Instruction and Assessment

Collection

2019 ASEE Annual Conference & Exposition

Authors

Jim Morgan P.E., Charles Sturt University; Euan Lindsay, Charles Sturt University; Colm Howlin, Realizeit; Maartje E. D. Van den Bogaard, Delft University of Technology

Tagged Divisions

Educational Research and Methods

the same branch 𝐵 > 1, 𝐹 > 1 (F->G,J)  NewBranch - moving to some other topic on a different branch, requiring a pathway through T1 𝐵 > 1, 𝐹 > 1 (A->B,E,G,J)We can combine the above classifications to create our distance dimension. This is visualized inFigure 5. Note that due to their definitions the Same, Next and Previous classifications can neverhave total distance traveled of more than one, so labels such as NextMid or SameFar cannotexist. Our distance dimension has 12 categories. Figure 5: How distance backward and distance forward contribute to total distance traveled categories.The breakdown of activities on our distance dimension and the percentage of activities based ondistance traveled, and

Conference Session

Postgraduate Pathways and Experiences

Collection

2020 ASEE Virtual Annual Conference Content Access

Authors

Russell Korte, George Washington University; Saniya Leblanc, George Washington University

Tagged Divisions

Educational Research and Methods

. Further analysis and modeling of the data areforthcoming, and will provide details of the competencies developed among the newcomers andhow they were developed. We anticipate that articulating the competency models of professionaland technical competence developed in this learning ecology will provide a deeper understandingof what newly hired engineers learn and how they learn as they develop into their careers.References[1] R. Korte, “Learning to practice engineering in business: The experiences of newly hired engineers beginning new jobs,” in The Engineering-Business Nexus: Higher Aims or Triumphant Markets? S. Christensen, B. Delahousse, C. Didier, M. Meganck, & M. Murphy (Eds), Cham, Switzerland: Springer, 2019, pp. 341