, and Washington StateUniversity are currently validating the EPS rubric by scoring 19 student discussions recorded andtranscribed during the 2011-12 academic year. This effort has produced a number of bestpractices for annotating transcripts, summarizing data and justifying ratings on rubric scoresheets, arriving at consensus scores between multiple raters, and assuring inter-rater reliability.In this paper, we examine a section from a scored transcript to illustrate the scoring methodologywhich includes rater practices and application of decision rules. Preliminary results are presentedwhich include inter-rater statistics.1. Engineering Professional Skills Assessment OverviewEngineering programs across the nation have struggled to define
forced a change in plans to equip the room with thin-client stations. All thirty stations were served by a single server running thirty images of aMicrosoft Windows 7 operating system 1. The system became operational a few weeks after thestart of fall 2012 classes. Back-to-back room scheduling gave no time for IT staff to service theinevitable station failures resulting from technology failures and students attempting to re-bootand re-arrange cabling of the thin clients.The fall 2012 semester of Linux instruction began in a classroom with no computer access at all,and with best hopes of having thin clients running a sluggish Windows 7 image with nopersistent storage. Enrollment was capped at 59 students, half of whom were freshmen takingtheir
turnover and higher service quality in their workgroups.A theoretical model of self-development following multisource feedback An influential meta-analysis of longitudinal studies published by Smither, London, andReilly50 examined the amount of performance improvement that occurs following multisourcefeedback and the factors that predict such improvement. The authors, who are among the mostprolific and highly-regarded scholars in the field of organizational psychology, reviewedevidence from 24 longitudinal studies and formulated a theoretical model for understandingfactors predicting self-development following the receipt of multisource feedback (see Figure 1).The design of the current project is derived in part from this model, so I have
-led paired thematic analysis is built on the idea that researchers working togethercan provide a richer, more rigorous and more theoretically sound analysis of studentunderstanding of a content area when the analysis is guided by one researcher who is arelative novice in that content area. Both researchers code and analyze the data and meetfrequently to discuss their analyses, but the meetings and general approach are managedby the content novice. The following sub-sections will provide more specific definitionsof the key terms in the phrase “novice-led paired thematic analysis.”1. NoviceAs implied by the name, in this application the “novice” has a lesser level ofunderstanding. There is likely an ideal level of “novice,” or at least some
students’ changing epistemologies. Furthermore,these broad surveys focus on generalized knowledge while leaving issues of domain specificityin epistemology largely unaddressed.Purpose The purpose of this paper is to explore a group of sophomore-level civil engineeringstudents’ personal epistemologies as part of the results from the first year of a larger longitudinal,qualitative study. In this way, we can explicitly track changes in personal epistemology andidentify at what stages in students’ academic careers they take place. In this paper, we willexplicitly examine changes in students’ personal epistemologies over the course of theirsophomore year in the civil engineering program. The primary research questions for this paperare: 1
to the inability to evaluate them withoutanalyzing a complete digital recording of a student’s solution. While this modified processanalysis lacks the ability to assess some skills that were found to be highly correlated to problemsolving success (namely those associated with erasures), it provides a more time efficient methodthat is more feasible to implement with current classroom resources.The resulting abbreviated process analysis assessment tool classifies problem solutions based on Page 23.987.3the following categories: 1) identify problem and system constraints, 2) represent the problem, 3)organize knowledge about the problem, 4
ininstruction, equilibrium serves as a macro-level model of micro-level phenomena that is usefulfor analysis. By highlighting the nature of engineering knowledge as a functional model ofphysical phenomena, students may develop epistemic beliefs that allow for complexity in theirunderstanding of the physical world 20. Figure 1 presents a concept map that displays a possibleway of organizing the concept of equilibrium within an epistemic framework. The figuredescribes various models and representations used to understand the concept of equilibrium with Page 23.1002.5direct connection to the underlying assumptions used when dealing with those models.Figure
books thatfocused on biological sciences were found to contain significant biases especially related togender. These biases may influence how students feel about careers in science, technology,engineering and mathematics (STEM) areas and therefore may impact future workforces inSTEM fields.IntroductionEngineers and scientist utilize the principles and theories of science and mathematics to design,test, and manufacture products that are important to the future of our nation and the world.1 Thepercentage of college students seeking degrees in math, science and engineering disciplines hasbeen declining for the past two decades. This is in part because fewer potential science,technology, engineering, and mathematics (STEM) majors are completing
inour survey instruments, the examples we attach to extracurricular activities are almost identicalto the examples provided in the National Survey for Student Engagement (NSSE) when referring Page 23.1085.2to co-curricular activities.In their 2005 study, Pascarella and Terrenzini1 wrote, “If there is a single adjective that describesthe body of research on the impact of college conducted during the decade of the 1990s, it is‘expansive’” (2005, Chapter 1). While that expansiveness has continued into the first decade ofthe twenty-first century, increased attention has been placed on student socioeconomic status,race, gender, and ethnicity, as
funding from two projects. (See Figure 1 for asummary of the survey development process.) The first, supported by the National ScienceFoundation (NSF), works with university-based K-12 STEM education outreach programs bothto support collaboration between the programs and to build program evaluation capacity withinthem.13 The other project, supported by a large, public, state-level foundation, aims to understandthe impact of 14 school-based K-12 STEM education programs.14 The authors conducted aliterature review, searching for instruments measuring student attitudes toward STEM, interest inSTEM careers, and attitudes toward 21st century skills. While the review revealed someinstruments and tools related to the topic, the search did not reveal a
the retained group. To the best of our knowledge, the present study is the firstattempt to study scholar retention at large scale and also the first quantitative effort to measurescholar retention based on bibliometric data within engineering education research.1. IntroductionOver the past two decades, Engineering Education Research (EER) has drawn the attention ofscholars from a variety of disciplines. From 1993 to 2002, it has been reported1 that Journal ofEngineering Education (JEE) authors primarily came from engineering disciplines but no singlediscipline dominated. Also, among all JEE authors, 23.9% of them have no engineering orcomputer science background1, 2. A recent study3 describes the opt-in process of how scholars,especially
of science, technology, mathematics, and engineering (STEM)1. Students involvedin robotics activities and competitions show an increase in attitude toward science2 and possess agreater awareness of engineering careers3. The largest high school robotics competition focusedon inspiring students in STEM areas is the FIRST Robotics Competition. An important part ofthe FIRST program is mentoring. In general, mentoring is believed to lead to high levels ofsuccess in both personal and professional endeavors4. With respect to FIRST, mentoring is givena high level of importance and is attributed with a large part to the program’s success5. This study investigates the role of mentors in eleven different robotics teams participatingin the FIRST
the degree towhich an early someone adopts an innovation. Five categories of innovativeness are (1)innovators (2) early adopters (3) early majority (4) late majority and (5) laggard. Diffusionresearch indicates that people within one category have considerable similarity with each other.Rate of adoption is another concept of the theory related to time. According to the model, theadoption of an innovation within a population is typically normally distributed such that the plotof adoption rates is S-shaped.A social system is another important element associated with the theory of diffusion ofinnovations. The theory assumes that diffusion occurs within a social system which is boundedby shared common objectives. A structure or hierarchy within
target school. While the terms 'hazard,' 'risk,' and'survival' might seem misaligned with the topic studied here, these are the formal methodologicalterms used in survival analysis.2,3 To mitigate this to some degree, we also use the termsattraction and abstention for hazard and survival, respectively. Attraction involves some pull thatdraws students to switch into a new major. Abstention involves something about a department orfield that repels students or keeps them from entering.Using survival analysis to explore late entry we had two research questions: 1) What is the hazard and survival function of attraction into or abstention from engineering and how does it compare to those for social science and STM? 2) Are there any
disciplines.Using a large student-level database, our study adds to the conversation by comparingengineering students with students in other majors regarding changes in demographics andcourse selection behaviors. Meanwhile, we use control groups including both non-residentstudents and states where merit-based scholarships were unavailable. We particularly intend toaddress the extent to which engineering students react to scholarship rules differently acrossinstitutions and states. Therefore, we attempt to answer the following research questions: 1) To what extent do merit-based scholarships affect first-time resident engineering cohort patterns regarding academic preparedness and socioeconomic status? 2) To what extent do merit-based scholarships
, facilitating the assessment of social integration and assisting the analysis of the departurepuzzle’s factors and informing policy making processes in education. Page 23.1211.2IntroductionEducators of engineers are facing the declining interest of potential students for the field 1, a lackof diversity of those who study engineering 2, and the need to assure that programs effectivelyprepare the graduates for the current engineering challenges 3,4. These conditions motivateeducators to be interested in the understanding of outcomes of engineering programs, and inparticular, persistence of engineering students and its relations with factors that can be
writtendocumentation. On average about 40% of the total coaching episodes related to professionalskills. Most of these episodes were nested within the context of core disciplinary content andconcepts. The types of feedback given to students included affirmative and corrective feedbackwith specific techniques of elaboration and revoicing. In addition, some discussion was found tobe neutral. When student teams were given directive feedback regarding their written workproducts, this feedback was taken up by the teams almost immediately.IntroductionWhile few studies have actually examined “everyday” engineering practice, professional skills(e.g., teamwork and communication) are believed to be a critical aspect of an engineer’s job 1, 2.Providing students with
AdvisingThere were nearly 500,000 undergraduate engineering students in baccalaureate programs in theUS in Fall 2011 1. It is expected that fewer than half of them will have earned engineeringdegrees by 2016 2. This low graduation rate is costly to institutions and has serious implicationsfor our ability to compete in the global economy. Furthermore, matriculation as well asgraduation rates are lower for the country’s growing minority population, particularly AfricanAmerican and Latino students 3. To compound such issues, public universities, often the post-secondary destination for students who are the first in their family to attend college, areundergoing drastic budget cuts, tuition increases, and loss of staff and full-time faculty. Thisreduction
students strengths to enhance their skillsto succeed in the performance of troubleshooting process in industry. 1. Definition of troubleshooting problem-solvingIn 1991, Perez [23] described troubleshooting as a task that deals with problem-solving skills thatare specific to a domain such as computer programming, engineering, biology, medicine, orpsychology. Further, he described the task of troubleshooting is to locate the problem ormalfunction in a system that is not working properly and then to repair or replace the faulty partor component. The level of details at which the troubleshooter must identify the source of themalfunctions depends on her or his role and the characteristics of the troubleshooting situation,e.g., the complexity of the
constructs. Ultimately, this instrument will be useful to the engineeringeducation community because of its potential to concisely measure all of the expectancy-valueconstructs (task values and expectancy of success) in engineering students. Motivation can becompared to other data such as persistence rates and measurements of career goals to betterunderstand the decisions that students make about their engineering education and career bymeasuring such connections.Introduction Research continues to show that engineering loses talented capable individuals to othermajors and careers.1-5 Out-migration is highest after the first year but research shows that peopleleave engineering majors throughout the undergraduate cycle, and even as practicing
measuring successful innovation. Thiswork further explores the use of language using specialized linguistic analysis software as ameans of generating quantitative data to make qualitative characterizations. Procedures foranalyzing a written report were developed and tested using four sets of draft and final reportsfrom a graduate-level, project-based mechanical engineering core design and innovation course.The procedure reported involved capturing the frequency and distribution of terms and nounphrases containing keywords by comparison to 1) a reference corpus of generalized AmericanEnglish, 2) other established, standard technical reference corpus and 3) documentation fromother projects in the same set from the same mechanical engineering course
by promoting reflection and self-explanation of themathematical procedures.IntroductionStatics, the study of objects and systems in equilibrium and the forces that act upon them, is afoundational subject present in most engineering curricula, but many students struggle to learnand succeed in statics courses. Statics is a “threshold concept” in engineering in that mastery ofthis area can serve as a “conceptual gateway” that opens up “previously inaccessible way(s) ofthinking about something” [1]. However, many statics courses have a high failure rate, and manystudents who pass still have difficulty conceptualizing important topics and may have trouble infollow-up courses [2-4]. As students develop from novices to experts in threshold topics
developer of CATS Page 23.1352.3designed three items for each of nine concepts: (a) Drawing forces on separate bodies, (b)Newton’s 3rd Law, (c) Static Equivalence, (d) Roller joint, (e) Pin-in-slot joint, (f) Loads atsurfaces with negligible friction, (g) Representing loads at connections, (h) Limits on frictionforce, and (i) Equilibrium. Subsequent research has identified a set of ten cognitive attributes orskills for which mastery is required to select a correct response among distractors2. Table 1presents a list of these skills and their descriptions.Table 1. Cognitive Attributes (Skills) identified for CATS1Cognitive Name
creation of a symbolic/mathematical (S)model for further analysis. Learning to solve problems in this particular way is a major goal forengineering education. The research presented in this paper focuses specifically on the text todiagram translation and the particularized representations utilized within a course onconservation principles. Previous research on student-generated diagrams revealed that, at thebeginning of the course, students are not able to construct useful diagrams that follow theconservation laws. This result led to the general question of whether students can recognizeuseful, correct diagrams; more specifically: 1) Given a set of diagrams, are students able todistinguish between effective and ineffective diagrams? and 2) How do
presented method is used compare the curriculum and courserelated options and decisions to evaluate the curriculum. An analysis is performed on thedecision making process to determine the extent to which changes in weight assignment affectthe final conclusion. It is found that by using this methodology, subjectivity may be minimizedand rational decisions may be made during the conflict resolution phase of curriculum or coursedesign.1. IntroductionMany higher education programs perform curricular revision or course redesign on a regular orsemi-regular basis with concerns of producing employment-ready graduates. These efforts aretypically undertaken at least in part as a result of constituent input. Constituents of an academicprogram typically
following terms represent concepts central to achieving this purpose. Professionalformation is the development of one’s professional identity as influenced by one’s personal valuesystems, the value systems of the culture of the profession (e.g., epistemologies, norms,particular symbols, and persona), and one’s developing conception of her/his professional rolesand responsibilities as she/he is transformed from a layperson into an engineer. This occurs, inpart, by socialization through classes, internships, design projects, and friendships 1. Self-awareness is a state of self-directed attention and represents the extent to which one hasidentified and can articulate the personal values, professional values, and assumptions regardingprofessional roles
became useful in designing the final instrument was a Q-matrix that weupdated throughout the redesign. A Q-matrix15, 16 is similar to a table of specifications17 exceptthat it is a matrix of concepts (horizontal) and items (vertical). A Q-matrix can be used torepresent the mapping between items and FKs. We had two different versions of Q-matrices, oneat the item level and one at the item response level (e.g., “A”, “B”, “C”, etc.; our items weremultiple-choice). Table 1 shows a portion of one of our item level Q-matrices. In this table, wehave four items, four concepts (“FK.c#”), and four misconceptions (“FK.m#”). The cells arecoded dichotomously: a “1” indicates that solving the item requires proficiency with thatconcept. An item can be coded
analysis uncovers whether team memberscorrectly perceive the relationships among their teammates. These initial findings openopportunities for future work on the role social network analysis can play in the analysis ofcollaborative learning.1. IntroductionReal world engineering design problems are frequently solved by teams; therefore, as educators,we are required, both by ABET and common sense, to give students the skills and attitudes thatenable them to work effectively in teams. One of the key skills is the ability to engage incollaborative learning with team members. In the process of acquiring the knowledge necessaryto solve the design problem, collaborative learning gives students the opportunity to both learnfrom and to teach their peers
toacquire conceptual understanding of the topics taught. Consequently, a course’s assessmentshould at least in part evaluate this conceptual understanding. 1 To achieve this, there are multipleassessment methods that could be used, as for example essays or oral exams. However, many ofthese methods require a very high time investment on the part of the instructor, which is, in manycases, simply not possible. For large classes, multiple-choice tests are among the most efficienttypes of assessment. Although much care has to be taken in their development, machine-basedscoring of multiple-choice tests can significantly reduce an instructor’s work load, freeing uptime for more face to face interaction with students. However, one main point of criticism
accumulation processes. Three categories of conceptualunderstanding are included in the RACI: (1) first order calculus, (2) mass flow, in particularwater flow, and (3) heat transfer.Pilot testing of the RACI took place in a sophomore civil and environmental engineering course.Results from pilot testing indicated the presence of persistent misconceptions among the studentsin all three categories of understanding. Student performance on the RACI went from 56% to59% after instruction. Internal consistency reliability was assessed using Cronbach’s Alpha;values were 0.77 for the entire instrument and ranged from 0.64 to 0.76 for the three conceptcategories of the RACI.Introduction Mass and energy balances are fundamental process models adopted by