taught using the control method.As such, this paper helps to address a gap in the engineering writing education literature, in thatfew studies have investigated the effect of various methods in an experimental fashion. Oneexception is the work of Jensen and Fisher,(1) who showed that the use of student peer reviewwas found to be positively correlated with an improvement in student writing proficiency. Thefindings were based on a comparison of scores on a writing assignment at the beginning of thesemester and a writing assignment at the end of the semester for a control section and a testsection.BackgroundThe test method was guided by advice gleaned from the technical writing and engineeringwriting instruction literature. Two very practical
in the finaldraft to the Engineering 1111 instructor, who then graded the reports. See Figure 1 for theprocess.Figure 1. The Writing Fellow Process Page 14.1383.3The Assignments and Writing Fellow CommentsBoth assignments for the semester were reports based on design experiences. All studentsdid the first assignment, the Aircraft Design Project. The second design experience variedby section. However, the report requirements were the same for all assignments. Theywere designed as Introduction, Methods, Results, Conclusions and Implications, with asection for formatting called ‘Requirements’. The assignments were written around theeight Elements of Critical thinking from the Paul Model
. Start with something and build. If the perfect community partner isnot available, that is okay too. Get started and grow and improve each time you teach.Most of the successful service-learning efforts started small and grew and developed.Finally, look for resources on your campus. If other faculty are not doing service-learning, look to see if your campus has a service-learning center or a the campusvolunteer office. They can be a great help and they will probably be excited to see anengineering professor.You and your students will learn a great deal. Our fellow citizens will be better for yourefforts too.Bibliographic Information (references need to be reordered for final paper) 1. National Academy of Engineering (2004). The Engineer of
engineering education research.While this paper is not suggesting that the rigor of Newtonian thinking be abandoned, it issuggesting that the tendency to apply mechanistic, reductive analysis to complex systems shouldbe addressed. According to Bertalanffy7, a founder of General Systems Theory, it is necessary tomeet the following two conditions in order to effectively apply mechanistic analysis to a system:1) the interactions between the parts are nonexistent or weak and 2) the relations describing thebehavior of a system must be causal (linear, cause and effect). In educational systems, theseconditions are rarely, if ever, met; therefore a systems approach to understanding educationalsystems is going to be proposed in this paper.On the abstract
byproviding an experience which is both fulfilling and enlightening [1]. Many freshmanengineering students are overwhelmed by the workload of the first year engineering curriculum,and are not stimulated by the course material. The majority of freshmen students lack thematurity or experience to understand how the engineering curriculum will be of value to them inthe future. They have not yet been exposed to the variety of opportunities that will be availableto them with an engineering degree, nor do they have an understanding of the skills andknowledge they will need to ultimately be effective and of value in the professional workenvironment. With such an imposing challenge facing them as an engineering student and littleunderstanding of how and where
, averaging four S-L courses each. . Finally, more than two-thirds ofthe students reported that S-L helped keep them in engineering, and female students reportedbeing significantly more responsive to the S-L projects. This program represents perhaps thelargest experiment with S-L in mainstream engineering courses in terms of courses, students, andfaculty. This approach is based on a number of hypotheses, which are posited and “tested” withquantitative and qualitative data. Most of the hypotheses are confirmed with data collected todate from this program and literature results.1. Service LearningAlthough there are many definitions of service-learning (1), we define service-learning as ahands-on learning approach in which students achieve academic
AC 2009-2186: PREPARING ENGINEERS FOR GLOBAL WORKFORCES: ARESEARCH UNIVERSITY’S RESPONSEGisele Ragusa, University of Southern California Page 14.974.1© American Society for Engineering Education, 2009 1 Preparing Engineers for Global Workforce: A Research University’s Response Gisele Ragusa, Ph.D. Associate Professor University of Southern California, Viterbi School of Engineering
FactorsEngineering to implement in their course design.Little research has been performed on the usability of CMS from the perspective of the student.Florida Gulf Coast University addresses some issues of information presentation and interfacedesign.5 WebCT and Blackboard provide access to courses that have been recognized by theGreenhouse Exemplary Course Program (ECP) as models of “best practices in learning, coursedesign, interaction and collaboration, assessment and evaluation, meaningful technology use, andlearner support” at http://www.webct.com/exemplary.8 A snapshot of one of the 2006 winners isincluded in Figure 1 below. The rubric for the ECP does not measure usability of the courses,but rather focuses on the instructional strategies. In reviewing
environment and the desired design outcomes.IntroductionThe importance put on engineering design teaching and learning increased over the last decade.Despite this fact, however, it is still challenging to discern the most appropriate pedagogic settingthat will culminate in long term, deep design learning. Among the reasons for this are: 1) there isno agreement on how design should be taught, or if it can be taught at all, 2) design outcomeassessment is challenging (do we assess the artifact designed, if so how do we define gooddesign), and 3) if good design cannot easily be defined, how do we create the best setting toconvey the conceptual learning behind it, etc. Consequently, there is a need for a framework ofdesign pedagogy that can capture
attributes relate to becoming: 1) Aware of theWorld, 2) Solidly Grounded, 3) Technically Broad, 4) Innovative, 5) Effective in TeamOperations, and 6) Effective in Leadership Positions.Our project team has collected data from engineering student subjects who were enrolledin two different courses, and at various stages of their education. These portfolios werecreated by students intending to major in a wide range of engineering disciplines. One-way ANOVAs and post-hoc tests were utilized to examine differences between theengineering discipline and students’ class standing (i.e., first-year students, sophomores,juniors, and seniors). Overall, our analysis indicates that our rubrics based onAlexander’s Model of Domain Learning (MDL) 2-4 are effective in
higher order cognition in the virtual laboratories. These statements areconsistent with the type of learning that has been previously measured for one of these virtuallaboratories, particularly through a think aloud protocol that has been reported elsewhere.IntroductionThe undergraduate laboratory plays a pivotal role in science and engineering curriculum,especially in the context of developing students’ abilities of scientific inquiry and engineeringdesign. The pedagogical value of the hands-on experience that a laboratory provides isubiquitously endorsed by educators;1 however, in practice the engineering laboratory haslimitations as well. Laboratories are resource intensive, both in terms of acquiring andmaintaining the equipment and in terms
schematics (before they are erased).PowerPoint Dissatisfaction and the Issue of Cognitive LoadIn a survey which was completed and reported on last year, it was noted that our engineeringstudents for the most part, did not favor PowerPoint lectures for technical material. Theypreferred traditional board work instead.1 The authors are now beginning to understand that thispreference may actually be related to the concept of “cognitive loading.” Cognitive loadingrefers to the maximum amount of information that can be stored in short-term memory. Indealing with a lecture or presentation that involves multiple equations, the mind can only recallso many “bits” from a previous slide. Being able to glance back at previous work, or to see anentire design or
American undergraduateengineering students. We used multiple criteria in sampling institutions, including Carnegieclassification; student body composition by ethnicity, gender, and enrollment status; institutionsize; geographical location; type (public or private), and number of transfer students.6, 7, 8At the core of the APPLES instrument are a set of variables that influence undergraduates’persistence in the engineering major, including motivation to study engineering. The surveyprobed six factors affecting motivation: financial, parental influence, social good, mentorinfluence, intrinsic psychological, and intrinsic behavioral. Table 1 summarizes these definitions
effective way to supplement lecturematerial in large courses for all students.1. IntroductionEmergent technologies are transforming higher educational practice, proliferating at a rate farfaster than that of research that analyzes how they are being used, whether they are making adifference to student learning, and whether such difference is equitably distributed among Page 14.1210.3students who vary in academic and social backgrounds. Lecture recordings are one of the newesttechnological innovations to serve teaching and learning. Will their availability emptyclassrooms? Will they lead to passive learning, rather than the active strategies long
. Theseinclude online quizzes before class and a large library of external links that are used forresources. A sample of the first two weeks are displayed in Figure 1. Notice that the plan isdivided into inside and outside class activities, all of which are thoughtfully designed to addmeaning and content to the course. These activities (a) get students ready or prepared forclass, (b) give them opportunities to practice—with prompt feedback via the Wileyplusplatform—doing whatever it is you want them to learn to do, and (c) allow them to reflect ontheir learning. The objective is to produce a sequence of activities that build on each other. Oneparticular activity was the construction of a poster and presentation of a real life failure event,sample of
focusimprovement of their aural comprehension skills.IntroductionThe history of education is filled with innovation in approaches for enhancing student mastery ofmaterial while also allowing more efficient delivery of instruction. New technologies in theclassroom are often attractive to faculty members because they can be used to foster learning inways that are not possible through a traditional lecture style format. As instructors, faculty havemoved from the not so distant past of writing on a black board, to writing in multiple colors on awhite board, to using overhead projectors with preprepared slides, to using overhead projectorswith PowerPoint[1], to using television sets in classrooms to watch videos in person or fromdistant classrooms[2, 3], to
selected findings from the extensive APS researchand to offer audience participants an opportunity to interact with these findings and providefeedback to the CAEE research team.The expected audience for this session would be engineering education researchers, engineeringeducators, faculty development practitioners, engineering curriculum developers, and policymakers. The session is designed to engage attendees in developing ways of thinking about thesefindings that can inform engineering education program planning and classroom practice.Overview of the Session ≠ Part 1 (40 min.): The first portion of the session will provide a brief overview of CAEE and APS with a focus on selected findings centered on the APS research questions. ≠ Part 2
truth is putting on itsshoes. Well, my shoes are laced up, and I’m trying to atone for my sins! Bibliographic Information1. (Removed from draft for author anonymity.)2. Handout from “Training the Trainer” workshop, University of Wisconsin-Eau Claire, 1970. Author unknown.3. Thalheimer, Will, “People Remember 10%, 20% … Oh, Really?” Work-Learning Research, Inc. (May 1, 2006). Available at http://www.work-learning/chigraph.htm.4. Dale, E., Audio-visual Methods in Teaching, p. 107. New York: Dryden (1969).5. Molenda, M. H., personal communications with Will Thalheimer, February and March, 2003.6. Treichler, D. G., “Are You Missing the Boat in Training Aids?” Film and Audio-Visual Communication, 1, 14
enabling technologies have evolved or new technologies haveemerged and spawned new teaching methods of interest.In this study we aim to examine a specific technology-enabled teaching method, namelyenhanced podcasting, which we define as capturing a lecture, both audio and written notes, fortime-delayed playback by students on a computer. This asynchronous learning technology hasalso been referred to as ‘screencasting’1 or ‘lecture capture’2 and a variety of enabling hardwareand software tools are available, as described in the references. This technology is not intendedto replace live, face-to-face instruction but is advocated as a viable replacement for whenstudents miss class, as opportunities for class review, or when the instructor has to miss
iteration.Model-Eliciting Activities (MEAs)Model-Eliciting Activities (MEAs) are client-driven, open-ended problems that are constructedusing six principles for designing MEAs6 that have been modified for engineering contexts7,2.The intention is to construct realistic engineering problems that (1) require student teams todevelop mathematical models for clients and (2) provide a natural window on students’ thinkingabout the mathematics in the problem context. That is, the problems are “model-eliciting” and“thought-revealing”6. Students’ solutions to these problems are generalizable mathematicalmodels – meaning the models are shareable, modifiable, and reusable tools6. To develop ageneralizable mathematical model for a client, students must draw on and
never become engineers, but all Americans -young and old - can benefit by having a better understanding of the role engineers play in thecreation of technologies” 1. The relationship between understanding engineering andtechnological literacy is of special urgency during the high school years, since “technologicallyliterate people should also know something about the engineering design process” 2. Developingstudents’ understanding of engineering design is aligned with the Standards for TechnologicalLiteracy Standard 9 3. The focus of this study is on development of teachers’ understanding ofengineering design in preparation for infusing engineering design into their high schoolclassrooms, as evidenced by their lesson plans. The National
requires reliable and high quality instructor feedbackand assessment to substantially boost the quality of student learning and work products. Model-Eliciting Activities (MEAs) are open-ended, realistic, client-driven problems set in engineeringcontexts requiring teams of students to create a generalizable (shareable, reusable, modifiable)mathematical model for solving the client’s problem. Two significant challenges are associatedwith the assessment of student team solutions to MEAs: (1) evaluation reliability among multipleinstructors and (2) fidelity to what is valued in engineering practice. In this paper, we describethe dimensions of a new assessment tool used by graduate teaching assistants to assess studentteam work on MEAs in a required
thermal systems and engineering education. Page 14.438.1© American Society for Engineering Education, 2009 Determining the Importance of Hands-On Ability for EngineersKeywords: hands-on, attributes, industryIntroductionTwo challenges facing engineering educators today are: (1) to provide a curriculum that preparesgraduates for the work of the twenty-first century; (2) to recruit more students to the field ofengineering. A number of reports cite the shortcomings of current curricula1-4. For example, thetraditional engineering curriculum does not prepare graduates to adapt quickly to new jobrequirements or to work effectively
most.MethodologyDeveloping a model to predict between a STEM outcome vs. another outcome for a givenstudent involves using data to discriminate between the two potential results. A valuable tool inassessing the accuracy of the discrimination is Receiver Operating Characteristics (ROC) curve14analysis. The prediction accuracy is a tradeoff between sensitivity and specificity. Sensitivity isthe probability of correctly identifying a STEM student while specificity is the probability ofcorrectly identifying a student having an outcome other than STEM. Such “Not-STEM” studentswere considered to be part of the “All Else” category. Classifying a student outcome as STEMvs. All Else is based upon the value of a prediction threshold. Consider a threshold valuebetween [0, 1
LISREL. CFA results show there is a positive correlation between theteam effectiveness measured by the two scales, thus we concluded that our team effectivenessinstrument proved to be valid through the cross-validation process.BackgroundThe Accreditation Board for Engineering and Technology (ABET) [1] with Engineering Criteria2000 started a movement to advance the current curriculum and pedagogy of engineeringeducation. According to ABET guidelines, students graduating from engineering programsshould not only have strong traditional engineering knowledge in fundamental areas such asmathematics and science, but should also be able to work effectively in a multidisciplinaryenvironment in multicultural teams.Campion, Medsker, and Higgs [2] define
overall structure of the subjects would include a loosely structured design “process”that included the steps: 1- consider user needs, budget and scope, 2- set constraints, 3-gather information on problem, 4-develop ideas for solution, 4-choose best solution givenconstraints, 5-develop prototypes if possible. The subjects would also need to stress thedevelopment of teamwork, as well as written and oral communication abilities. Page 14.933.5Subjects were developed that included civil engineering projects in New Orleans,transportation systems, how to slow rainforest deforestation by developing technologyideas in farming or logging for local populations, toy
,thus reducing student frustration. This article presents the results of the student portion of thefirst SPIRIT summer workshop.The research questions addressed here are: 1. Did the SPIRIT summer workshop improve participating high school students’ attitudes with respect to IT careers? 2. Did the SPIRIT summer workshop improve participating high school students’ knowledge of how to program using the Alice software? 3. What changes can be made to the SPIRIT summer workshops to further improve students’ attitudes with respect to IT careers and their knowledge of the Alice software?Similar workshops will be offered over the next three years and the findings presented here willbe used to improve their design and implementation
attend IEP were statistically significant, 2 (gender) by 2 (IEP attendance)analysis of variance (ANOVA) was applied within each of the different TOEFL score groups.Under the assumption that improved English language proficiency, and thus enrollment in IEP,could be relatively more or less important to engineering students compared to students in othermajors, an analysis of major-specific GPA was conducted for students in the TOEFL score range520-539. This more narrowly-focused score range was selected for two reasons: (1) to separateeffects of IEP participation from TOEFL score effects (as described above) while at the sametime providing an adequately-sized (see Table 2) within each subject area, and (2) the TOEFLscore cutoff point of 520 is
(i.e., cognitive level), noting that “abilities are seen as unipolar, whereascognitive styles are typically conceived to be bipolar”23. That is, abilities range from none to alarge amount with a socially preferred end, while cognitive styles range from one extreme to acontrasting extreme, with no socially preferred end (see Figure 1). Level (High) Style (Pole 1) Style (Pole 2) Level (Low) Page 14.613.4 Figure 1: Independence of cognitive level and cognitive style16Both cognitive level and
communication skills and teamwork for the global context: “In the new century the partiesthat engineering ties together will increasingly involve interdisciplinary teams, globally diverseteam members, public officials, and a global customer base.”2Similar points are made in influential volumes such as Educating Engineers: Designing for theFuture of the Field,3 and Educating the Engineer of 2020.4 Redish and Smith also consider Page 14.840.2global awareness and multicultural communication skills in their useful framework forengineering undergraduate education (Figure 1):5Figure 1. Purdue’s future engineer (From Redish and Smith, 2008).A significant