Paper ID #31526The CAHSI INCLUDES Alliance: Realizing Collective ImpactDr. Elsa Q. Villa, University of Texas at El Paso Elsa Q. Villa, Ph.D., is a research assistant professor at The University of Texas at El Paso (UTEP) in the College of Education, and is Director of the Center for Education Research and Policy Studies (CERPS). Dr. Villa received her doctoral degree in curriculum and instruction from New Mexico State University; she received a Master of Science degree in Computer Science and a Master of Arts in Education from UTEP. She has led and co-led numerous grants from corporate foundations and state and
assistance for understanding theories, astheir role is instrumental. Such descriptions must be carefully qualified with the warning thatethics is not actually computable; no mechanical procedure exists for making ethicaldecisions.Normative Theories as Subroutine HeadingsEthical theories sketched as function headings outlne high-level decision procedures suitable forcarrying out by a human agent, with input and output parameters (given as types). An example ofa function heading is the description of Consequentialism (“right and wrong depend onoutcomes”) as a subroutine that produces action-scores as output, given as input both anethical quandary Q and some knowledge of how the world works (to enable the computation ofoutcomes):action-scores
equation is defined as the order of the highest derivative appearing in the equation and ODE can be of any order. A general form of a first-order ODE can be written in the form dy/dt + p(t)y + q(t) + s = 0 where p(t) and q(t) are functions of t. This equation can be rewritten as shown below d/dt(y) +y p(t) = - q(t) - s where s is zero. A classical integrating factor method can be used for solving this linear differential equation of first order. The integrating factor is e∫p dt . Euler Method Graphical methods produce plots of solutions to first order differential equations of the form y’ = f(x,y), where the derivative appears on the left side of the equation. If an initial condition of the form y(x0) = y0 is also specified, then the only solution
reduced, allowing for more detailed and interesting findings.3.2.5 Tests for AssumptionsIn order to understand the validity of our data set and the subsequent analysis, a few assumptionsmust first be tested which the factorial ANOVA takes into account. The assumption of normalitywas tested via examination of the residuals. Review of the Shapiro–Wilk (S-W) test fornormality and skewness and kurtosis statistics suggest whether or not normality was a reasonableassumption. The boxplot of the residuals is analyzed for a relatively normal distributional shape.The Q–Q plot and histogram are also examined for normality. The assumption of homogeneityof variance (homoscedasticity) is tested by conducting Levene’s test.Random assignment of individuals to
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opportunityfor schema construction (germane load) is possible. Note that although our model currently showsonly 4 LESs, we expect that this number will increase over time as more pedagogical approachesare integrated.The main objective of the proposed model is through research find the values of a, b, c, d, e, q, r, s,and t in the equations F2FA (aCL + bGA + cPBL + dSI + eLS) and OA(qCL + rGA + sPBL + tSI)that maximizes student learning and engagement. Where F2FA and OA are the face-to-face andonline activities respectively. CL - collaborative learning, GA - gamification, PBL - problem-basedlearning and SI - social interaction are the LESs, and LS - lecture style the traditional approach.You may think of a, b, c, d, e, q, r, s, and t as the percentage of
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enrollmentstrategy have higher educational outcomes compared to students with mixed and part-timeenrollment strategies. In another study, Chen et al. [20] use HMM to categorize students based ontheir learning styles and investigate the relationship between learning style states and learningefficiency in massive open online courses (MOOCS).Hidden Markov Model, like any other Markov Models has a set of states Q = {q1 , q2 , . . . , qN }.However, in HMM, the states’ status are hidden, and the model aims to estimate the states given asequence of observations O = o1 , o2 , . . . , oT with the length of T , where each observation isdrawn from the set of M possible observations V = {v1 , v2 , . . . , vM }. Each Hidden MarkovModel has three parameters: (1) π0 , is
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their internship. The majority of students’ plans centered around their behaviorback at school; we’ll mention one theme that emerged concerning their future job search plans. Many students spoke of how participating in their internships increased their awareness of specializedsub-disciplines within CSE. This new knowledge informed their elective course choices, with studentsmentioning interest in upper-division networking, artificial intelligence and machine learning, databases,web development, security, advanced math and statistics, and more. Some students also planned totake non-CSE classes to round out their education, for example in management, public speaking, or design. TABLE I Q
packed with team building andprofessional development activities. Activities include Skype and in-person alumni talks, industrysite visits and Q&A with engineers, faculty research talks, and a University career center visitwhere staff discusses the college application process, various majors in STEM, alumniconnections and inspirational stories. These features of the camp align with the dimensions ofrelatedness and autonomy in SDT.The code camp was led by a female faculty/lecturer that has extensive experience with teachinglower-level Computer Science courses at the University level. The code camp instruction teamalso included undergraduate student teacher assistants (TAs); two female TAs and one male TA,all of whom were Computer Science
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