to the development of the Mini-Math Jam – a shorter, one-week version of Math Jam that is offered a week prior to the beginning of the fall semester, andduring the winter break. The Mini-Math Jam has also been successful in helping studentsimprove their placement scores, and preparing them for subsequent math courses they take.1. IntroductionCommunity colleges serve as the gateway to higher education for large numbers of students inthe U.S., especially minority and low-income students. Yet for many students, the communitycollege gateway does not lead to success. According to a study of community colleges inCalifornia, only one in four students wanting to transfer or earn a degree/certificate did sowithin six years.1 The completion rates for
approximately 8 hours a week (4 hours of regularclass time, a 1-hour weekly meeting with the course instructor, a 2-hour SI session and 1 hourpreparation time).Student SuccessThe two college reports which follow indicate that the SI sections did help students succeed inCollege Algebra & Trigonometry and other math classes. In the Achieving the Dream report, thedata focuses exclusively on the pilot SI section for College Algebra & Trigonometry held in thespring of 2009. NECC Achieving the Dream Report – A Preliminary Look at Comparing Outcomes for Students who received Supplemental Instruction to those who did not – College Algebra & Trigonometry1 During the Spring 2009 semester
courses.Matlab Component DescriptionThe advanced engineering mathematics course in which this Matlab component was includedcovers the standard topics of ordinary differential equations (ODEs), Laplace transforms, andcomplex analysis (including complex numbers, functions of a complex variable, and integrationin the complex plane). The component consisted of three self-study modules, two onlinequizzes, five sets of teaching assistant (TA) office hours (each 2 hours long), and a set of videotutorials.Matlab Self-Study ModulesThe three self-study modules are described in greater detail in Table 1 below, and as an examplethe third module is presented in Appendix A. These modules were designed to allow the studentto work through them on their own time in the
mathematics to undergraduate engineering students 1-3. The main objective ofthe paper is to provide some lessons learned in developing and conducting a graduateengineering mathematics course from the perspective of an instructor. These lessonsdiscussed in the paper may be insightful and useful to other faculty members trying todevelop such a course or teaching a similar course.Course Description The author designed and developed the course, MEEN 5304 AdvancedEngineering Analysis, in 2001 and started offering the course in fall 2002. The coursecontents were chosen according to the contents and requirements of other graduate Page 22.1371.3courses offered
” perspective [5]. Theconceptualization of transfer shifts away from the expert’s viewpoint to an actor’s or learner’sviewpoint. In this approach, the goal is to understand the “relations of similarities created” by thelearner and how they are supported by the environment. The focus is not on whether the righttype of transfer is obtained but rather on determining what kind of similarities the students see.Another modern approach to transfer has been proposed by Bransford & Schwartz [1]. Transferstudies in their view have relied too much on “sequestered problem-solving”, in which a studentis explained a problem then asked “cold” to solve a similar problem thus giving negative results.They promote an approach using “preparation for future learning
with manyuniversity-level educators, and, in turn, stimulate education researchers to re-evaluate thepotential of current STEM initiatives to reverse the declining trend in STEM education in theU.S.A.1. IntroductionThe needs related to science, technology, engineering and mathematics (STEM) education in theUSA are many. A well-recognized need is for more K-12 students to pursue STEM disciplines atthe university level. It is the acknowledgement of this need that is central to the various STEMinitiatives at the National Science Foundation (NSF), as well as other funding agencies. Thereare a wide variety of reasons responsible for the increased lack of interest in STEM subjectsamong younger (K-12) students. Based on the proposals that were funded
, curriculum theorists, instructional researchers, and specialists in testing and assessment led by Lorin Anderson, one of Bloom‟s former students, published an updated version of Bloom’s Taxonomy. The revised version modified terminology, structure, and emphasis of the original taxonomy (see Figure 1) to provide “ „a clear
publication. In addition to teaching regular math courses, I also like to create and teach innovative courses such as ”Mathematical symmetry of Southern Spain” and ”Mathematics and Architecture of the Incas in Peru”, which I have taught as study abroad courses several times.Michael P. Hennessey, University of St. Thomas Michael P. Hennessey (Mike) joined the full-time faculty as an Assistant Professor fall semester 2000. He is an expert in machine design, computer-aided-engineering, and in the kinematics, dynamics, and control of mechanical systems, along with related areas of applied mathematics. Presently, he has published 41 technical papers (published or accepted), in journals (9), conferences (31), or magazines (1). In
ProcessingAbstract Image edge detection is an integral component of image processing to enhancethe clarity of edges and the type of edges. Issues regarding edge techniques wereintroduced in my 2008 paper on Transforms, Filters and Edge Detectors.15 The currentpaper provides a deeper analysis regarding image edge detection using matrices; partialderivatives; convolutions; and the software MATLAB 7.9.0 and the MATLAB ImageProcessing Toolbox 6.4. Edge detection has applications in all areas of research,including medical research6,16. For example, a patient can be diagnosed with ananeurysm by studying the shape of the edges in an angiogram. An angiogram is thevisual view of the blood vessels (see Figure 1-Vascular Web image). The previouspaper15 studied
-related courses werebetter than their counterparts in reformed Calculus I classes. Furthermore, 44% of reformedCalculus I students changed to traditional Calculus II programs and only 18% of traditionalCalculus I students shifted to reformed Calculus II. Baxter, Majumdar, and Smith[1] alsosurveyed reformed and traditional calculus students’achievement in the Math-ACT andfound that traditional Calculus I students’average grade was slightly higher than that in thereformed Calculus I, but only 52% of traditional Calculus I students passed the exam,significantly lower than reformed Calculus I students’passing rate of 64%. As for succeedingperformance, reformed Calculus I students surpassed the traditional students in Physics I andCalculus II, yet
efficient in increasing studentengagement and supporting teachers’ instructional needs. The key strategy of the project is todevelop innovative math learning modules and use them to enhance students’ performance. Byapplying cutting-edge computer graphics and virtual reality technologies, these modules can: (1)make mathematics learning interesting while still retaining the underlying contents; (2) makeabstract and non-intuitive mathematics concepts “visible” and “touchable”, and thereby, easy tounderstand; and (3) bridge mathematics and engineering and motivate students to pursueengineering careers.The goal of the project is to ensure that students, especially freshmen and sophomores, canbenefit from the instructional strategies and develop a solid
the students finish all the topics in their pie they are given a comprehensive assessment todetermine if they have retained all the items in their pie. The progress assessments mostly givequestions that the students have currently worked on, and some that they are ready to learn. Thecomprehensive assessments give questions on any topic in the pie from the most basic materialto the last item that they learned. If the student earns a 92% or better on this assessment they aremoved to the next course. The 92% is based on percent mastery of the entire course, not 92% ofthe questions correct on the assessment. If they do not get a 92% they relearn the topics they gotwrong, and try the comprehensive assessment again. Figure 1 shows an assessment
students believe arethe issues which have the most impact and the interventions which would be most useful. Thispaper contributes to that area of the literature by presenting the results of a survey of 87engineering majors who took pre calculus. All had taken pre calculus within the past foursemesters and only 11% of the respondents had received a D or F grade. Specifically, the surveyexplored the research questions in Table 1. Table 1: Summary of Survey Research Questions 1. Do students believe they were placed in pre calculus appropriately? a. Is this substantiated by the correlation of the test score and the grade? 2. What is the role of high school preparation and how should this influence the course
describe theauthors’ approach to adding original derivation assignments to the curriculum of engineering andtechnology courses in order to ensure the genesis of this creative skill set at the undergraduatelevel. The goal is to develop in undergraduate students learning patterns that will facilitate theability to write for any system, a set of equations that describes the system. II. INTRODUCTIONMathematical modeling entails finding a series of steps that define all the relationships in asystem. An example of a system is an energy system, a power system, an electronic circuit, amanufacturing process or a cancer cell. Each of these systems is an ongoing subject formathematical modeling.1-4 Students can use a
theirspecific expectations for student mathematical knowledge and skills.After receiving sample problems from five faculty members, the questions were analyzed todevelop a set of learning outcomes that would reflect the knowledge and skills required to solvethe problems. There was significant overlap among the problems, with respect to the knowledgeand skills expected. The resulting set of mathematics topics for which engineering facultymembers expected student mastery are listed in Table 1. Table 1. First-year Mathematics Topics Determined by Engineering Faculty Members Projection Vector Components (2-D) Derivative (using Chain Rule) Second Derivative