Paper ID #9277Writing Abstracts of Homework Problem Solutions: Implementation and As-sessment in a Material Balances CourseDr. Kevin D. Dahm, Rowan University Kevin Dahm is a Professor of Chemical Engineering at Rowan University. He received his B.S. from WPI in 1992 and his Ph.D. from MIT in 1998. He co-authored the book ”Interpreting Diffuse Reflectance and Transmittance,” published in 2007, with his father Donald Dahm. His second book, ”Fundamentals of Chemical Engineering Thermodynamics,” a collaboration with Donald Visco of the University of Akron, is expected to be released by January 10, 2014. Kevin has received the
(Eisen; Eisen; Eisen). Figure 1 summarizes the results of the earlier surveys (note 1985 comments on emerging technologies and does not provide data of the type in 1980 and 1989). Figure 1: Historical data (% of responding schools) While comparison of the data in Figure 1 with the data that follow suggests that electives are much more diverse now than in the past, but it may also reflect the greater variety of questions and analysis that can be done with an online multiple choice survey
statistical analysis of their data andconsideration of relevant theory. The course is structured in such a way that students mustdetermine which statistical techniques are appropriate for processing their experimental data. Thecourse is also designed to meet the Writing Intensive requirements of our university, through acombination of individual lab reports, reflections on their ability to write in a technical context,and brief essays on engineering ethics and laboratory safety.Specific course logistics, including the sequence of activities, learning objectives, andconnections to student outcomes in junior- and senior-level courses, are considered here. Directassessment of student performance against specific learning objectives from the past three
multiple perspectives(flexible representations), which facilitate a better understanding of the topic under discussion.This flexibility will be reflected in the students' ability to demonstrate the relationships betweensame elements in different ways along different conceptual contexts or in the ability to formdifferent representations of a same situation depending on the task20. Flexible representationshave three levels of learning: image level, which refers to the initial holistic image of a conceptor a phenomenon; schema level, where people outline images as a result of the search for Page 24.40.3regularities in their experiences; and a
Leaders: A Case Study of the AIChE Concept WarehouseAbstractPropagation is a widespread goal for educational innovations. If an innovation is effective in oneenvironment, developers usually desire to share it with other instructors and institutions to have alarger impact and improve education more broadly. Additionally, funding agencies like theNational Science Foundation require a “broader impact” component in all grant proposals. Oneaspect commonly missing when an innovation is shared is a reflective, evidence-baseddescription of the process as the innovation moves from the home institution to other institutionswith different faculty, different students and a different culture. E.M. Rogers put forth a theory,Diffusion of
. However, the curriculum at mosttraditional Western universities does not necessarily reflect these new dynamics.” The majorityof chemical engineering programs today do not leave room within their curriculum for studentsto be able to adequately explore the concept of chemical product design and how novel ideas canbecome the basis for new businesses. In fact, out of the 158 ABET accredited chemicalengineering programs in the US, only 25 offer chemical product design classes. This state ofaffairs presents a stark contrast with mechanical, industrial, and even bioengineering programs,where product design has been a routine part of the curriculum for decades.In response to this need, the chemical engineering program at the University of Pittsburgh
individuals to teams or assess an individual’s fitness for a particular careerpath.5,6,7,8,9,10 These studies often produce conflicting results surrounding the benefits of teamdiversity or homogeneity of personality type,5,8 which limits the possible impact of the researchon engineering pedagogy. While some MBTI types may be statistically more likely to be theleader of a team or pursue a particular career, any type can excel in any position or field giventhe proper self reflection and knowledge of MBTI type. The value of this team training aspect ofMBTI is often overlooked or mentioned as an afterthought.5,10 Further, because of this aspect ofMBTI type, some studies discard the MBTI instrument in favor of other, more prescriptiveinstruments.6,7Rather
. Page 24.625.6 Figure 2. Introduction to Chemical, Food, and Environmental Engineering Design course structure“Concepts” introduce students to the engineering design process, problem-solving techniques,working in teams, engineering as a profession, and planning for success that students then applyin “Laboratory” on two actual design projects. Students were organized into multidisciplinaryteams of three to four members; the group had a total of thirty-eight students (15 male).The “Concepts” section uses quizzes given in nearly every session to ascertain whether studentshave understood the material in their pre-class reading assignments. In addition, we encouragestudents to write brief reflective journal entries to further solidify and
, the 2011-2012 data does not include data gathered regarding the students' major, thus theseare not reported. Secondly, the material/energy balance class under review has students majoringin both Chemical Engineering and Bioengineering.In 2011, analyses of students’ Peer Learning scores revealed a significant Gender interaction,reflecting the tendency for women’s scores to decrease and men’s scores to increase from pre-test to post-test. Students' Classroom Connectedness scores increased significantly from pre-testto post test. Accounting for Gender and Race Classroom Connectedness increased for Caucasianmales and females and Asian males.In 2012, the results were mixed with interactions based upon the specific demographic variables(Gender
Page 24.220.7preliminary standard operating procedure that the students can use to assist in theirfamiliarization with the apparatus and which they must adapt and modify to reflect theirknowledge and experience with the MIMO apparatus.The use of this hands-on experience is an important tool for students to better understand processcontrol concepts and to improve their general troubleshooting skills. The benefits are numerousand relate and to the fact that the student performs a complete study and tuning of a processsystem, in addition to using “state-of-the-art” software tools to assist in the analysis (e.g., Loop-Pro, Excel, LabView, etc.). Specifically, the student is engaged in familiarization, calibration,characterization, and the set-up
enroll in a unit operations laboratory. This factor may or may not have influences student’sinterview results from the study, but it was consistent for both the group that received hands-onand the group that received lecture, so it is assumed the two groups are equal with respect toadditional hands-on learning.The interview protocol was updated to better reflect the course content that was covered in 2013.One question from the 2012 protocol was omitted on applying the ME balance to a piping systemand another question on continuity was added. The results from the continuity question will bereported elsewhere. The 2013 interview protocol can be viewed in the appendix of this paperwith questions emphasizing different usages of the ME balance and
the National Science Foundation under thegrant TUES 1245482. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the authors and do not necessarily reflect the views of the NationalScience Foundation.References1. Ma, J., and J. Nickerson. 2006. Hands-on, simulated, and remote laboratories: A comparative literature review. ACM Computing Surveys, 38(3), 1-24.2. Wieman C. and K. Perkins. 2005. Transforming physics education. Physics Today,58(11), 36-41.3. Perkins, K., Adams, W., Dubson, M., Finkelstein, N., Reid, S., Wieman, C., & LeMaster, R. 2006. PhET: Interactive simulations for teaching and learning physics. The Physics Teacher, 44, 18.4. Finkelstein, N.D., W.K. Adams, C.J