, David K. Probst Department of Physics and Engineering Physics Southeast Missouri State University Cape Girardeau, MO 63701 AbstractMany concepts in physics and engineering courses cannot be understood easily. Althoughpowerful computers with advanced software can generate fancy animations, students still cannotgrasp these concepts without spending time reflecting on them. In the past, homework was thetool used by instructors to challenge students and enforce their learning. Unfortunately, nowmany students can bypass this challenge and directly go to the solution manual for answers,which is widely available from the
learners receive and process information. The FSLM incorporates someelements of the Myers-Briggs model and the Kolb’s model. The main reasoning for its selection inthe DLMS evaluation is that it focuses on aspects of learning that are significant in engineeringeducation.The FSLM consists of four dimensions, each with two contrasting learning styles: Processing(Active/Reflective); Perception (Sensing/Intuitive); Input (Visual/Verbal); and Understanding(Sequential/Global). The details of the dimensions can be found in Ref.6. In order to determine anindividual’s specific learning style, Felder and Soloman13developed the Index of Learning Style(ILS) survey. Each of the 44 questions within the survey is designed to place the learner’spreference within
parallelarrangements is used to demonstrate the underlying resistance addition rules. Although thisserves as a good hands on experiment to test the principles of resistance, it often leaves studentswith very few possible combinations to build in the lab, and does not reflect the innatecomplexity of even the most basic of modern circuits. Moreover, typically students aredisconnected from the theory when using rudimentary laboratory equipment to make fairlysimple measurements. Since it has been demonstrated that a more engaged and active approachto physics education has a more lasting effect on the retention of material [2], it was our goal to Page
reflects the physicist’s way ofunderstanding the world, so we should teach physics that way.The importance of nurturing a scientific curiosity and motivating young students’ understandingof science has been addressed for many years1 and that call invites everyone2. As Barak Obamarecently reinforced: “we want to make sure that those who historically have not participated inthe sciences as robustly -girls, members of minority groups here in this country- that they areencouraged as well”3. In this call, physics and mathematicians become the main filters of young Page 26.353.2students’ career decisions. We want them to select a program because it has
of x, (b) Calculate 𝑍!" at 𝜆! /8 away from the load, (c)Calculate Γ! , (d) Calculate VSWR and (e) Calculate the transmitted power and reflected power as apercentage of incident power 𝑃!"Solution: (a) 𝑍! = 0, 𝑍! = 50 Ω. !! !!! Γ! = = -1 = 𝑒 !!"# => Γ! = 1 50 Ω 𝑍! !! !!! Φ = 180 ! !/! Applying this for 𝑉(𝑥) , we get ( 𝑉(𝑥) = 𝑉! (1 + Γ! )! − 4 𝑠𝑖𝑛! (𝛽𝑥
impacted research, but also the classes that are using Buddy.The items in Tables One and Two reflect considerable effort on the part of faculty, students, andthe co-authors of this paper. It should not appear as though these results were not “hard-won.” Inthe conclusions of this paper we list some of the issues that have arisen in this deployment andoperation of the Buddy cluster in hopes that others can at least be aware of pitfalls.Conclusions and DiscussionAt UCO an NSF MRI grant was competed for and won for a HPC cluster, Buddy, to enable andenhance the research computing environment at UCO, which had not had any such facilitiesbefore. The competition for Buddy took several tries with
active learning approach2,3,4;• promoting a better interpretation of physics and its application in practical situations5promoting activities where students can understand how physics works instead of just doingcalculations;• developing skills and competencies for a professional life as an Engineer6, such as gainingan understanding of different cultures, foreign language skills, oral and written expression,time management, and teamwork, amongst others.The pedagogical features of the developed project were as follows:• development of scientific thinking and reflection using physical problems. Page 26.147.3• application of real problems with increasing
points on the Posttest. Qualitative observations were that as reflected in Table 2, students worked more on homework and in a more much more timely fashion than observed in the past. The oneonone interactions helped better deal with issues in problemsolving, including the issue of how students approached problems. This appears to be indicated in the improvement in the Final Exam scores. In addition, the interactions with the instructor enhances student performance on the teambased projects compared to previous semesters and other courses. After using a flipped methodology in several courses and looking at all evidence: quantitative and qualitative, the lead author thinks that the students’ ability to learn
normally covered in the standard senior design curriculum. The paperconcludes with a reflection on what constitutes “appropriate technology” and how developmentengineers need to consider the relative benefits of locally produced or locally assembled productsin maximizing societal impact.Project BackgroundA. Mali Sorghum ProjectThe ‘Mali Sorghum Project’ is a joint project between the University of St. Thomas (UST) and theInternational Crops Research Institute for the Semi-Arid Tropics (ICRISAT).5 A sorghum hybridwas developed by ICRISAT through a decade long participatory collaboration between subsistencefarmers in Mali and scientists from ICRISAT. The hybrid yields acceptable amounts of sorghumgrain, the primary product of traditional sorghum
both (i) incorrectanswers and (ii) correct answers supported only by explicitly worked out computations. Sinceour data come from a final exam, we expected that many students would do explicit calculationseven if they thought of a quick, heuristic answer, in order to get “full credit” or to be sure of theiranswers. Therefore, we coded answers as reflecting mathematical sense-making if any part of astudent’s solution included mathematical sense-making, whether or not the student also did acalculation. The details of the sense-making coding on each problem are described in the nextsub-section.Our preliminary coding scheme was generated by three of the authors by looking at a smallsubset of the student responses (N=25). Two authors then coded 45
author took several lab courses, followed theinstructions and was assigned good grades. He spent little to no time reflecting on each labafterwards, instead going on to focus on the next problem set, paper or upcoming exam. Whilethe labs were often designed to demonstrate theory that was introduced in lecture, there weremany situations in which important underlying assumptions were not mentioned. Now, as amathematics professor teaching courses with applications, such as differential equations, discretemathematics, and linear optimization, the author’s interest in applied topics has been rekindled.It is apparent that his learning in undergraduate lab courses and the supporting lecture courseswas not sufficiently deep and did not include the
activities to develop students’ reasoning skills and therefore, increase engineeringstudents’ physics learning.IntroductionScientific reasoning refers to “cognitive abilities such as critical thinking and reasoning” (Bao etal, 2009, p. 586) or “skills involved in inquiry, experimentation, evidence evaluation, andinference that are done in the service of conceptual change or scientific understanding”(Zimmerman, 2007). It is needed in problem solving situations and requires methods of scientificinquiry such as the cycle of analysis, testing, reflection and revision, in order to construct adeeper understanding of the situation. Scientific thinking is “purposeful thinking that has theobjective of enhancing the seeker’s knowledge” (Kuhn, 2010, p. 2).To