algebraic equations, allowing for a nuanced understanding of the student'sproficiency levels across various skills within the subject area. A pivotal mathematical model within CDMs is the Deterministic Inputs, Noisy "and" Gate(DINA) model, which assesses mastery or non-mastery statuses across multiple cognitive skillsbased on raw question responses [21], [24]. The DINA model, a latent class model, classifiesstudents into skill mastery profiles based on their responses to exam questions, with each questionhaving a specific relation to one or more skills [21], [24]. The linkage between questions and theircorresponding intended skills are captured in a Q-matrix, a matrix of ones and zeros indicatingwhich questions require a particular skill in
” sessions.In that meeting, they also came up with plans (for communication, conflict management, etc.) forproject management and specify roles (rotational) for themselves.Analysis TechniqueA baseline data was collected at the beginning of the semester to assess students’ pastparticipation in teams and their perception of teamwork. After implementing the interventions, afinal data collection was done to measure student participation and their perception of teamwork.The effect of the interventions (Research Question #1) was measured using the improvement (ordeterioration, calculated by subtracting the baseline score from the final score) in studentparticipation and their perception of teamwork. Student teamwork was measured using the Team-Q survey [21
Maintaining Effective Research Teams. IEEEComputer Society.[7] Bernat, A., Teller, P.J., Gates, A., Delgado, N., & Della-Piana, C.K. (2000, July). Structuringthe student research experience. In Proceedings of the 5th Annual SIGCSE/SIGCUE ITiCSEConference on Innovation and Technology in Computer Science Education, pp. 17-20[8] Gates, A. Q., Hug, S., Thiry, H., Aló, R., Beheshti, M., Fernandez, J., & Adjouadi, M. (2011).The Computing Alliance of Hispanic-Serving Institutions. ACM Transactions on ComputingEducation, 11(3), 1–21. doi:10.1145/2037276.2037280[9] Villa, E. Q., Kephart, K., Gates, A. Q., Thiry, H., & Hug, S. (2013). Affinity Research Groupsin practice: Apprenticing students
was run to examine if LCDLMs offered differentialbenefits or effects based on the gender of participants. Four modes of engagement were assessed:Interactive, constructive, active, and passive scores. Participants were grouped by their gender:male and female. First, we checked preliminary assumptions, and results revealed that data wasnormally distributed, as assessed by inspecting the Normal Q-Q plots. There were no univariateand multivariate outliers, as assessed by boxplot; there were linear relationships, as evaluated byscatterplot, and no multicollinearity; and variance-covariance matrices were homogeneous, asassessed by Box’s test of equality of covariance matrices (p = 0.473); variances werehomogeneous, as assessed by Levene’s Test of
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) introduce the website interface/functionality/moduledesign and what optimization (or testing) techniques did the team use in 2 minutes; 3) brieflydemonstrate the workflow of the website in 1.5 minutes. Each team has a Q&A section wherethey can answer questions from other students and assessors.We invited three evaluators to grade students’ projects, including two females and one male. Theevaluators have had at least 3 years of experience working as full stack/back-end web developers.They were asked to grade the students’ presentations from five aspects: 1) the novelty of the idea;2) the technical depth; 3) the website’s design; 4) the presentation; and 5) the Q&A session. Thefinal grade for each team was 25 points, which was evenly divided
: Theme 3: Theme 4: Engineering Interactions 1 Interactions 2 Active Learning (Problem solving) (Office hours) (Q&A) (Experiential)While the importance of interactions between students and instructors is a critical element ofundergraduate education that is common to all fields and disciplines, the remaining two topicsthat emerged from topic modelling were more specific to engineering. Topic 1 emphasizedstudent preferences for more problem-solving time and practice with TAs. This relates directly tothe theme of problem-solving which is highlighted by the ABET (accreditation board forengineering and technology) student outcome #1: “an ability to identify
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Value Is that what you got? NA Remember What’s the equation? State Understand So would we do the energy equation to find the Q [heat Recognize transfer]? Apply How did you convert to kilowatts? Solve, Sketch Analyze Why can’t we use enthalpy? Differentiate Evaluate Wait, how do we have an adiabatic turbine? Argue, DefendCategorizing questions depends on more than the initial question. A question like, “How did youconvert to kilowatts?” may not appear to be an application level of learning initially. Followingthe question, the study group went
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a problem. And then we'd have five minutes of takeoff and review, and just like have the professor solve the problem for us. So I thought that kept me really engaged in the class, I never fell asleep in that class. (ST5)Qualitative student survey data also reported a number of aspects of teaching practices thatengineering students found effective. For example, instructor availability, easy access to coursematerials, well-presented lectures, and take-home assessments were mentioned in the followingcomments in the student survey. [In response to the question on teaching practices that helped them learn effectively.] Instructor making themselves available after lecture for Q&A: served similar function to
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, culturally relevant/ sustaining workshop designs. Author 1, had to rely onthe high school mentors’ knowledge and input because they are experts and participants in youthculture. Author 2 “To provide a more comfortable and safe environment for participants to share their ideas and thoughts, we told them all their ideas would be [anonymized post-workshop], and we don’t judge any ideas, we just share and learn. To encourage them to express more, we use a storytelling session instead of the traditional Q&A session to learn about participants’ background experiences with AI/ML and their attitude/perspective of teaching AI/ML. That was a successful attempt. Participants shared more
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papers offered practical and transferableideas”, and another made“Great contacts who provided excellent suggestions for the direction ofmy research projects.”Regarding opportunities for professional networking, most participants reported having manyopportunities throughout the conference to connect with others. According to one participant,“Ihad many opportunities for networking. Networking was one of the highlights of the conferencefor me.” Another reported that “I spent a lot of time with people I knew but had only met online inthe past. I also met people during sessions and met up between sessions.” Coffee breaks and timebetween sessions gave participants time for conversations, including the time before and Q&Aafterwards. Participants
EngineeringEducation Research as a field, (2) offering sessions on what makes a good graduate application,(3) offering sessions on identifying advisors, and (4) creating interactive time through breakoutrooms and Q&A sessions. Suggested improvements included offering more time and interactionin the breakout sessions. While organizers could consider extending the event next year to meetthis need, individual programs could also think about how to provide more in-depth interactions.The one measured objective that was not achieved as successfully as others was creatingcommunity. This is not surprising as the current showcase construction did not emphasize thisaspect nor intentionally create space to do so.Data from the student perspective are not sufficient
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