given, etc., but rather fromstudents’ hard to observe internal mechanisms. Such mechanisms regulate the extent to whichstudents can comprehend the complexities of a real system and how much of this complexitythey can reflect in a conceptual and calculational model.Self-efficacy is one such mechanism that has been shown to regulate learning, motivation andacademic performance of students. It is defined as personal judgments of one’s capabilities toorganize and execute courses of action to attain designated goals [1]. Individuals have high self-efficacy for a task when they believe they possess the capabilities necessary to successfullyperform the task and low self-efficacy if they believe that they do not have the necessarycapabilities. Hence
’ failure to understand one ormore Statics concepts 3. Litzinger and his colleagues 12 studied four undergraduate students majoring inengineering who had already taken Statics. These students were asked to draw fully dimensionedfree body diagrams (FBD) of the target represented in the problem statement and illustration.The intention of this study was to uncover the sources of errors that students made in theirproblem solutions. From this study it was found that a major source of errors in problem solvingwas the recall and use of conceptually erroneous knowledge in determining the solution 12. Ananalysis of students’ solution of Statics problems reveal patterns of errors that are reflective ofconsistent misconceptions that students hold
and family related policies from women’s perspectives.13 Findings from these studiessuggest that organizations are gendered and the image of the ideal worker reflects that of a whiteman.In work organizations, job-related factors such as, rewards and benefits, advantages andprivileges, decision-making and control, identity and self-esteem and, performance and job-satisfaction are governed by power relations that continue to favor men over women. Hence,gender is not a factor that initiates unequal power relations in organizations; rather it is anintegral part of the organizational structure.1 West and Zimmerman13 describe the processes ofembedding gender into the organizational structures as “doing gender”, and Acker1 posits thatdoing gender
about--"cooperative learning," "collaborative learning," and "active learning"? The proliferation of"learnings" and their attendant partisan camps invites the reawakening of long-standing facultyprejudice against educational fads and "methods." Even so, interest in PBL grows because notonly does research show a higher quality of learning (though not a greater amount if "amount"equates with the number of facts), but problem-based learning simply feels right intuitively. Itseems to reflect the way the mind actually works, not a set of parlor-game procedures formanipulating students into learning 15 .Unfortunately, while there is agreement on the generaldefinition of PBL, implementation has varied widely 3 .The large variation in PBL
, students, andindustry prioritize hands-on ability relative to other desirable traits. Surveys were given toindustrial representatives, faculty, and students asking them to rate hands-on ability among eightother traits. Analysis found that hands-on ability ranked third. Understanding the importance ofhands-on ability would better allow engineering curricula to reflect its prioritization. Hands-onability also has gender associations. Better understanding how industry views this could allowcurriculum to prepare its students to meet this obstacle. It would also allow academia to realizethe gender association and address it within the institution. These changes could allow betterengineering experiences for female engineers as well as
CriteriaStudies were examined to determine whether they met the criteria for inclusion in the study.First, the study examined students enrolled in undergraduate programs enrolled in engineeringdegree programs at accredited postsecondary institutions in North America and Europe. Second,the study examined the effect of educational programs on the cognitive development of studyparticipants. Third, only studies that were carried out in a classroom or program setting wereconsidered, as opposed to those conducted in a more controlled experimental setting. Fourth, theresearch was published or reported after 1996, so that the research would more closely reflect thecurrent environment in which students learn. Fifth, and finally, the research reported
: A framework for modeling the local coherence of a Page 15.269.8 discourse. Computational Linguistics , 21, 203-225.[6] Isbell, M., & Davis, J. (2007). "Organizations are made to tick through talk:" A network comparison of conversation centers, influential words and network centrality. Annual Meeting of the NCA 93rd Annual Convention. Chicago, IL.[7] Jonassen, D. (2000). Computers as mindtools for schools: Engaging critical thinking. New Jersey: Prentice Hall.[8] McLaren, T., Vuong, D., & Grant, K. (2007). Do you know what you don't know? Critical reflection and concept mapping in an information systems
CTC and engagement in undergraduate STEMeducation. With the completion of the conceptual model, the second phase of the study, surveytool development, becomes the focus.AcknowledgementsThe authors would like to gratefully acknowledge the National Science Foundation for theirsupport of this work under the REESE program (grant numbers DRL-0909817, 0910143,0909659, 0909900, and 0909850). Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views ofthe National Science Foundation.References 1. Goodenow, Carol (1993). Classroom belonging among early adolescent students: Relationships to motivation and achievement. Journal of Early Adolescence
existing theoreticalframeworks most relevant to my research questions are 1) the history and pedagogy ofengineering education, which is widely supported through organizations such as ASEE;2) STSE (formerly STS) education and 3) Teacher Identity. The selection of STSE andTeacher Identity have been informed by my own experience conducting research withpre-service and new science teachers, and their use of an STSE approach in their teachingof science. However, acknowledgement of context is critical in educational research, andas I reflected further on these theoretical strands, I realized the inherent challenges inutilizing theory from the K-12 realm in the framing of my post-secondary researchproject
is much more positive than the previous two years. Six students describepositive relatedness behaviors and only two describe predominantly negative behaviors. Asexamples of positive behaviors, Joe appreciates smaller classes and when faculty are passionateabout what they are teaching and Mark reflects on relationships with faculty over time: “When the professors are teaching in their expertise and you can tell they‟re really passionate about what they‟re teaching. They‟re smaller classes, smaller labs. It‟s, it‟s really nice” (Joe, Senior). “I‟ve gotten quite a bit of attention from, from certain professors that you kinda‟ grow with, and you come back for advice, for with. And, I mean if you go to the office, as long as you seek
innovation. Manifestations of this desire to produce more creative engineersand scientists abound. They include, for example, the recent announcement by the KoreanAdvanced Institute of Science and Technology (KAIST) that its new admissions policy willspecifically include creativity as an admissions criterion in up to a fifth of the incoming freshmanclass.3 This drive to produce creative engineers is also reflected in the focus of the Generation IIIEngineering Research Center (ERC) Program of the National Science Foundation. This programis designed to produce “engineering graduates who will be creative U.S. innovators in a globallycompetitive economy”.4 This program explicitly requires that ERC proposals address theeducational requirements needed to
computing capabilities expected during the first years on the job. Eachresponse was assigned a value (1 = not important, 2 = slightly important, 3= average importance,4 = important, 5 = very important) and the mean rating given by all respondents was calculatedfor each question. Responses with a mean value higher than 4.0 and with a standard deviationless the 1.0 indicate a high level of consensus among participants about the importance of thatparticular item, while responses with lower means and higher standard deviations reflect lowerlevels of consensus 14.Results were further analyzed by type of engineering industry, with computer science, electricalengineering, computer engineering, information technology and engineering computer
process: ≠ Blue – Enablers ≠ Pink – Hinderers ≠ Yellow – Student Need Statements ≠ Green – Student Need Factors ≠ White – Pre-defined Student Need Factors (based on the student success theoretical perspectives)Step 2: Elicit Once participants had an understanding of the scope of the meeting, they were guidedthrough a brainstorming exercise by the facilitator. The discussion questions allowed the groupto reflect on their own experiences and provide their perception of those needs that facilitateengineering student success. To ensure that participants clearly understood what is expected ofthem, each discussion question was initially posed to the group to provide an example
Table 5 (Cont’d). Concept & Problem-solving InventoryIV. Assessment InstrumentThe goal of the instrument is to place students at their appropriate levels within the taxonomy.The students take a series of three tests starting with fundamental level problems as indicated inthe inventory. The assessment tool has been designed to be simple and easy to implement. It wasdesigned to focus on the student's conceptual understanding and problem-solving skills. Theassessment instrument provides an essential guide for the instructor to assess the student’sproblem-solving skills that also require covering the conceptual knowledge; this is reflected inthe distribution of the score weights among the competencies. The conceptual competencies ofthe
beliefs. Transitional responses reflect a view that, unlike teacher-centered responses, includes students. These responses demonstrate an affective response towardstudents, as opposed to emerging and reform-based responses, where the student is viewed ashaving a critical voice in classroom decisions and construction of knowledge (Roehrig & Kruse,2005). Table 1 represents the number of times each instructor had a response that was coded ineach of the five categories. The top row for each instructor represents responses from the firstinterview or survey and the bottom row represents responses from the second interview - oneyear later. For this paper, shifts in beliefs have been defined as at least three questions codesmoving in the same
own. Second, we have shared part of the engineering oral presentationrubric we created based on executive input. The full version will be shared at the conference.The resulting tool has high face validity: it clearly reflects real world oral communication. Thetool also has high content validity: it is drawn from engineers already very successful incommunicating in the workplace. Third, we have described the supplemental teachingguidelines that define the rubric items in more detail and provide information on how to helpstudents improve their oral presentation skills. Many engineering faculty would like to includepresentation skills in their courses. Often they and their teaching assistants recognize the neededskills without necessarily
credit for teachers.A recent international review of research on professional learning for educators by LindaDarling-Hammond and colleagues22 report that strategically designed, intensive, and sustainedprofessional learning can have a powerful influence on teacher skills and knowledge andultimately lead to improvements in student learning. Prevost and colleagues23 examined thePLTW teacher professional development training documents, training activities, teacher projects,and teacher self-assessment and self-reflection items. They described it as localized to a two-week intensive program rich with engineering and math concepts that were often implicitlyembedded in the engineering activities. Little, however, was revealed about the impact
reflection, as well as peer’spublic evaluation of each other’s thinking. Further, teachers are in a position to gain authenticknowledge of how students are thinking—which informs subsequent instruction. In particular,the nature of students’ conceptions of foundational engineering constructs is readily accessible tothe teacher, as well as the researcher.Assessment of students’ responses to MEAs can take on two forms. One means for assessingstudent work is to describe the characteristics and nature of the models students create inresponse to an MEA. Carmona2 produced a system for describing responses to MEAs, andHjalmarson3 has adapted the system to describe work in engineering-based MEAs. The resultprovides information that reveals how students are
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