mental processing coupled with dialogicinteraction with other learners, where the learner reflects on novel insights and perspectives. Wehave integrated these considerations into our course design. In the next section, we describe thepurpose of this investigation, followed by the course structure.Research PurposeThis paper presents findings from a comparative analysis of the learning outcomes ofengineering students who participated either completely online mode or in a hybrid-mode, whichincluded both online and in-class components. For both learning groups, we utilized the samepedagogy designed to enhance ethical reasoning (the SIRA framework).20 We implemented thispedagogical framework at the graduate-level and assessed student learning and
student learning outcomes using WReSTT-CyLE and gamification.Access to resources (funding and otherwise) is often cited as the primary barrier totechnology adoption and the integration of software testing and programming skills. Therole of negative disposition towards educational technology and digital games, has notbeen fully considered as a contributing factor in the relatively slow pace of technologyadoption in higher education. The example of such slow adoption of technology inhigher education can be exhibited in many programming courses. Moreover, thesenegative dispositions towards technology may hold also reflect more than several decadesof dominant values informed by the naturalist and romantic beliefs about programming,student learning
and texts as resulting from typified behaviors, knowledge, and actions of agiven community of practice: “[T]o write, to engage in any communication is to participate in acommunity; to write well is to understand the conditions of one’s own participation with thatcommunity and determine the success or failure of communication”11. As such, genres reflect thevalue systems of individual organizations (e.g., a stand alone business) and also large scalecommunities (e.g., engineers). Being a proficient writer means becoming intimatelyknowledgeable of the conditions of participation in a given community of practice. According toJames Dubinsky, “our work [as professors] involves more than teaching our students strategies orforms; it also
Georgia, is presently undergoing a major revision to reflect the most current trends inthe job market and the ABET computer science curriculum requirements. Additionally, thecurriculum redesign is needed to increase the program's appeal to students and employers. Theunderlying principle for this redesign is to provide more flexibility for students to take major andfree elective courses and lessen the emphasis on traditional mathematics requirements (such asCalculus II).Currently, the major area in curriculum of computer science at FVSU includes 60 credit hours ofwhich 9 hours are major electives and 6 hours free electives. The revised program will include33 credit hours in core curriculum of computer science, 12 credit hours in major electives
to develop models to reflect the reality. Clear examples can teachstudents how to collect data, develop base model, improve it to advanced models, analyze theobtained results, and think about usability of their simulation results. These learning outcomes canclearly demonstrate valuable educational objectives.This paper, presents an example where a group of students were assigned to develop a simulationmodel for the BGSU Students Union (BTSU) Cafeteria. Managing a university cafeteria oftenexhibits challenges for the food services located in the cafeteria. One challenge regards waitingtimes. This study was focused on reducing the average waiting time of the diners in the queues,while increasing overall efficiency of the food services.The
who have decided topursue a computer science and engineering education.Retention of computer science and engineering students is a major problem at many institutionsof higher education. Retention issues have a big impact on the colleges and universities in avariety of areas such as academic affairs, student services, and even recruitment of newengineering students. Nowadays, graduation rates are published and lower rates reflect poorlyon an institution of higher education. Because of these reasons and more, colleges anduniversities have turned their attentions to finding new ways to retain the students that do enrollin their computer science and engineering programs.In order to increase the retention rate of our Computer Science, Computer
engage students inlearning and allow translation from conceptual knowledge to practice. We propose to use Model-Eliciting Activities (MEAs) to develop students’ representational fluency in the cybersecuritydomain. MEAs are activities that intent to simulate real-word client-driven scenarios. And thesuccess of these MEA activities rely on teamwork and the students’ abilities to apply concepts.Properly constructed and implemented MEAs can increase the use of: (1) student reflection toolsin assessments, and (2) learning technologies. MEAs require students to iteratively build, test andrefine their knowledge by encouraging students to build different forms of representations andconnect and translate among them [3]. These activities focus on
-coding Learning Prior coursework and grades in Math, Physics, Chemistry, as well as specialized topics like Statistics, Drafting, Manufacturing…Team experiences Whether the student has been asked to work in a team, of what size and nature and how they perceive that experience. Student outcomes include robust data set in the form of exams, in-class assignments andhomework. This study is focusing on Computational Thinking aspects of this class, thus allreported grades are filtered to assignments that reflect CT and/or CS topics, unless otherwisestated. An example of topics omitted include questions about the general engineering designprocess
construct elements of a program, and a built-insimulator.4 The simulator allows the user to observe and test the behavior of the programthroughout execution by watching memory elements change in response to the environmentand/or user input/actions. Once the program has been thoroughly tested with the simulator, theIDE is used to download the program to the actual controller.Figure 2 shows the eight key symbols that may be used in a flowchart. As with traditionalflowcharts, the shape of a particular block denotes its function. For example, the two diamond-shaped symbols, compare and decision, reflect branch points in the program logic from whichthere are two exits
, demonstrate thecapability of mobile platform specially the Android platform which bear the testimony thatmobile platform can be made efficient in controlling robot.Preliminariesi. UMLUnified Modeling Language28 widely known as UML is a software engineering tool used formodeling software systems. Fundamentally it is used as a tool for analyzing, designing andimplementing software intensive systems. UML provides a visual representation of the systemwhich reflects the standard and interactive organization or system’s elements. From thebeginning till now, there are several versions of UML have been evolved and UML 2.0 is usedfor the modeling of our system. UML offers two types of system modeling, one is structural orstatic modeling which require the
Group C 3.47 1.19 Group D 3.24 1.33Team online discussion makes me reflect on the course content Group A 2.88 1.24in a deeper level. Group B 2.72 1.06 Group C 2.75 1.32 Group D 2.91 1.42I frequently respond to the post from my group members through Group A 3.53 1.45online discussion. Group B 3.28 1.11
significant proportion ofstudents to fail in exams, which consisted mostly of simulating the execution of thealgorithms for a given input. Usually, students made two types of errors: simple involuntarymistakes and errors that reflect a lack of understanding of the algorithm. After applying themethodology, the former were less common, and the latter were infrequent. We started usingthe methodology in the II semester of 2010 (in our college, I semester goes from March toJune and II semester from August to November). Tables 1 and 2 show the average grades forTest 1 applied during the II semester of 2009-2010 and the I semester of 2010-2011 (beforeand after applying the methodology, in each case). This test is about analysis of algorithmsand sorting
mentor (11variables) on the post-survey is 4.35 (out of 5) with std = 0.97. An inspection of the Q-Qplots and histogram graphs for the remaining five variables (v2, v4, v5, v8, and v12) forwhich the confidence interval were not computed (variables not normally distributed) showone or two outliers. These outliers could be a reflection of the type of research project andthe student’s academic level.Table 2 (Evaluation 1): CISE REU Survey Constructs Differences df Std. Error 95% confidence interval Mean SmdConstructs
form. Thequestions are also re-designed in order to attempt to maximize activation related to cryptographyconcepts by maximizing the effort subjects exert to answer the question. We expect that thesechanges to the fMRI methods will add to our understanding of where cryptography concepts areprocessed in the brain.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under Grant No.1500046. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.ReferencesAlvarez, J. A., & Emory, E. (2006). Executive function and the frontal lobes: a meta-analyticreview. Neuropsychology
, this imperfection inmeasuring can convincingly reflect the real overhead in a real system.5.1 Hash FunctionsIn the first set of experiments, we measured the H(VM) with several major CryptographicHashing Functions [12]. We chose different hash function to be able to compare them and chosethe best match for our proposed Architecture. Results of H(VM)’s Execution time are shown inthe Table 2. As seen from the results, the CPU processing time of hashing is basically linear tothe size of the VM templates. SHA-384 and SHA-512 has similar processing time due to the factthe construction of the hashing are very similar. An interesting to note was SHA-256 processtime is actually longer time than SHA-384 and SHA-512. This is because SHA-384 and SHA-512
. These software enabled devices allowed students to take notes,draw charts and diagrams. Afterwards the results were measured through student feedback. The Page 26.1592.3results of the experiment were satisfactory as more than 85% of the students thought that usingthe tablets contributed positively towards their active learning experience. Instructors observedthat the level of interaction and enthusiasm increased greatly among students. While it is difficultto provide accurate result values that reflect the amount of increase in student performance in theclassroom, the high level of attendance, which was observed, was certainly a good indicator
. Despite thebenefits of an approach teaching problem-solving skills first, the transition from pre-programming problem-solving courses to courses in which students should master a full-fledgeprogramming language remains a challenge 18, 22. This is reflected in the number of students(44%) who did not have an acceptable performance in either concepts or skills (Figures 4 and 5).Even though those students had passed a previous problem solving course, they find thetransition to a learning environment that uses a full-fledge programming language like Javadifficult.According to Mayer 17, in addition to the cognitive and metacognitive aspects of problemsolving, other aspects like motivation and engagement are also important determinants of studentsuccess
information, funding sources, government entity, etc.) of information they seek. This preference will drive the use of the corresponding web site seed along with the presentation of the associated keywords to the user as defined by the attribute property also in the ontology. And third, the refined list of keywords, instead of all the keywords under a given topic, is used by the application for the searches. Figure 9 contains a sample mockup of the modified search application including the Page 26.1358.14 Figure 9 - Sample Search Application Mock-up user’s ability to set the search aspect. Note the categories reflect
to succeed.AcknowledgementsThis work was supported by a National Science Foundation grant (#1203206) to the NationalCenter for Women and Information Technology, and a National Science Foundation grant(#062444) for Project PRiSE. Any opinions, findings, conclusions, or recommendationsexpressed in this paper are the authors’ and do not necessarily reflect the views of the National Page 26.328.16Science Foundation.References1. The White House. (2011). Women and girls in science, technology, engineering, and math (STEM). Retrieved from http://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp-women-girls-stem-november2011.pdf2
results do not prove the superiority ofthe CBI compared to other traditional methodologies, the CBI approach did offer our students theframework and skills to bridge the gap between traditionally disparate sciences. The courseevaluations filled by students, and the reflective summary by the involved faculty, show manypositive improvements in attitude, independence, attendance, learning engagements, immersion,and mood. We also measured significant improvements in programming and problem solving,especially as it related to mathematics and physics, as well as in decision making.Some of the skills that CBI targeted were interpersonal skills, oral and verbal communications,and presentations.Acknowledgment:Part of this work was conducted while
Proceedings of the 45th ACM Technical Symposium on Computer Science Education (pp. 355-360). ACM.15 Exter, M., & Turnage, N. (2012). Exploring experienced professionals’ reflections on computing education. ACM Transactions on Computing Education (TOCE), 12(3), 12.16 Lethbridge, T. C. (2000). What knowledge is important to a software professional? Computer, 33(5), 44-50.17 Andriole, S. J. and Roberts, E. (2008). Technology curriculum for the early 21st century. Retrieved from http://cacm.acm.org/magazines/2008/7/5359-point-counterpoint- technology-curriculum-for-the-early-21st-century/fulltext 21Formal