. If it fails, one will go back to the first step and build a new model [8].The diagnosis section consists of the estimation of Q-matrices and using these Q-matrices toprovide insight into the dependency between the variables of BoT and the TC. In this paper, weused the GDINA function from the CDM package [9], [10] to retrieve the delta matrices that areessential to the estimation of the Q-matrices. The initial Q-matrix given to the GDINA functionis always 1J x K. Both the Lasso and the Truncated L1 penalty (TLP) terms were used as tuningparameters to retrieve the delta matrices which were then converted to Q-matrices following asimilar expectation–maximization (EM) algorithm in [11]. We also used our experience to comeup with one expert
significant predictors is as follows:Motivation = 2.866 + 0.229(Enjoyment) - 0.101(Anxiety) - 0.153(Shame)Residuals were examined to assess the assumptions of the regression model. The scatter plot ofobserved vs. predicted values and the plot of residuals vs. predicted values indicated that theassumptions of linearity and homoscedasticity were reasonably met. The histogram and Q-Q plotof the residuals suggested that the residuals were approximately normally distributed, with someminor deviations at the tails. The Durbin-Watson statistic was 2.04, indicating no significantautocorrelation in the residuals. These checks support the appropriateness of the regression modelfor the data.From the figure 3, we can interpret that the Observed vs. Predicted
datasets (pre-standard course, post-standard course, pre-honors course, post-honors course) and conductedstatistical tests to determine any differences within our datasets. significance testing, we analyzedfor any pre/post differences within and across course types via these statistical tests andconducted tests for normality via the Shapiro-Wilk Test and Q-Q plots. These normality testswere used to establish if parametric or nonparametric testing was appropriate. Nonparametrictests were used given normality testing indicated non-normal data for each variable(stakeholders, value categories, value created) in all four datasets. Thus, we applied theWilcoxon Signed Rank Test for pre/post significance testing within courses and the Mann-Whitney U Test
and examined Q-Q plots, so non-parametric tests for significant differencesbetween the sections were performed using Kruskal-Wallis to analyze for significant differencesbetween the medians of each section. Subsequently, a post-hoc Dunn’s test with Bonferronicorrections was conducted to analyze which sections were significantly different. The sample sizefor each of the sections were as follows: Section A had a sample size of 33 (n = 33), Section B had asample size of 38 (n = 38), and Section C had a sample size of 50 (n=50).Table 2. Descriptive statistics of student engagement scores (n = 121) Measurement Total Emotional Physical Cognitive Mean 67.61 23.13
. Through the lunch, students became acquainted with their discipline-specific facultymentors. These faculty mentors consist of one representative from each engineering disciplinewho eventually would become the SSP students’ academic advisors and have regular check-inswith their students. The lunches offered a casual, non-threatening environment to help buildconnections between students and their mentors. Faculty mentors utilized the lunches to provideshort Q&A sessions with the group describing their discipline along with academic andprofessional opportunities.Throughout the year, social events were planned to provide students with an opportunity to relaxand connect with each other and the grant team. A winter quarter kick-off party was held
difference at =0.05. Several questions were found to be statistically significant, asshown in Table 4 Table 4: Comparing mean values between post-fall and post-spring Q Post Fall 2022 Post Spring 2023 Mean t df p M (SD) M (SD) Difference 16 1.660 (1.205) 2.009 (0.956) -0.349 -2.257 177 .025 34 1.032 (1.589) 1.636 (1.239) -0.604 -2.973 175 .003 37 1.602 (1.595) 2.103 (1.140) -0.501 -2.519 164 .013Discussion & Future WorkAdditional work is needed to better understand the results of the EDVES survey. Initial
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City 8 3 5 4.13 0.83 Tech.p. I would recommend this workshop to other students interested in understanding what 8 3 5 4.50 0.76 majoring in engineering would be like at City Tech. q. I would recommend this workshop to other students interested in understanding what the 8 3 5 4.25 0.89 different engineering majors are like at City Tech. r. I am confident that I can identify the engineering 8 3 5 4.25 0.71 major that best fits my desired
generative AI (many wrote about multiple types of experiences), 5 students mentioned theirexperience using generative AI for schoolwork. Examples included using DALL-E to generateimages when making a zine for a senior English project and generating practice problems forclasses. 4 talked about learning with or about generative AI in out-of-school settings. Forexample, one student had attended a summer program hosted by a university where they codedan AI using Q-learning and Monte Carlo tree search. 3 students used generative AI for theirhobby by setting generative AI up as their opponent for video games or creating stories aboutcharacters for role-playing games. 10 students stated their experiences testing out generative AIon their own or with their
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words, why did we not skip the process of flocculation and go directly to filtering? 7. What additional steps would you take to make the water potable?ENGR 1203Module 6: Life Cycle AssessmentUse the following carbon footprint database to find the kg CO2e/kg for each ingredient.https://apps.carboncloud.com/climatehub/search?q=pizza&market=USA&sort=market%3AdescDeliverables 1. One table showing total kg CO2e/kg for each meal (three rows, one column) 2. Three pie charts, one for each meal, showing what proportion of the total impact is caused by each ingredient 3. One bar graph, showing how the total kg CO2e/kg breaks out by "phase" (agriculture, transportation, etc.) 4. Discussion
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