Hands-on Effect on MotivationAll nine experiments on motivation reported a positive hands-on effect size ranging from0.19 to 0.90. Using the random effect model, the overall mean effect size was moderate andstatistically significant (d = 0.52, p = 0.05), indicating a positive hands-on effect onmotivation. The heterogeneity statistic was highly significant, Q = 15.76, df = 8, p = 0.05.This result shows that the hands-on learning effect had a significant positive impact onstudent motivation. Figure 2 shows the forest plot of the hands-on effect on motivation.Figure 2 Hands-on effect on Motivation (Forest Plot)The Hands-on Effect on Self-EfficacyThirteen out of the 15 experiments on self-efficacy reports a positive hands-on effect sizeranging from
likelihood estimator. Initial descriptive statisticalanalysis was conducted and used to test normality of data. Dependent variables and hypothesizedcovariates showed significant p-values (p>.05) on Shapiro-Wilk test of normality (refer to Table3). This indicated violations of normality in the data; however, large samples are sensitive toviolations of normality (Azen & Walker, 2011; Pituch & Stevens, 2016). As a result, visualinspections of histograms and normality Q-Q plots indicated acceptable normality in the data[36]-[37]. Table 3 Test of normality of data for each survey construct Shapiro-Wilk
their early experiences leading to the Bridge program. The secondinterview explored their experiences in the Bridge program and their aspirations for their co-op.Interviews were professionally transcribed and pseudonymized.Data were analyzed using a narrative approach that includes multiple readings [19]. Themultistep process included reading for: familiarization with the transcripts, identifying contentsuch as individuals mentioned and major storylines, detecting identity of the participant andothers, and uses of CCW and funds of knowledge. After the readings, a narrative case waswritten for each participant.Quality was considered internally and externally. Internally, we used the Q 3 framework [20],[21] as a reflexive tool to guide each phase
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definitely think that I could have made my presentation moreinteractive and engaging. I think for next time, I will try to incorporate several different activitiesinto the lesson instead of just one at the end.” Similarly, “I think I could have had more specificinstructions” or “I could also improve by providing relatable examples so that the audience canbetter understand the topic” indicated that students recognized that they needed to go more in-depth or explain their key concepts better (18%).Many made mention that discussion and Q&A sessions were successful as assessments (17%), “Ifeel that I had a well thought out topic that connected well back to content of this class. Thepresentation that I prepared was engaging and easy to follow, with
. • Schedule synchronous class times to open a few minutes before and stay open a few minutes after class time and use the time to have conversations with students.Figure 4. Summary of recommendationsReferences[1] K. A. Douglas, A. C. Johnston, J. Martin, and T. Short, “Instructor Decisions andStudents’ Perceived Support in Engineering Project-Based Courses During the COVID-19Pandemic.”[2] R. M. Felder and R. Brent, Teaching and learning STEM: A practical guide. Jossey-Bass, 2016.[3] X. Huang and E.-L. Hsiao, “Synchronous and asynchronous communication in an online environment,” Q. Rev. Distance Educ., vol. 13, no. 1, pp. 15–30, 2012.[4] C. Rapanta, L. Botturi, P. Goodyear
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metric from the “Topical Content Post-Tests”will then be used to test the learning effectiveness by performing another two-way ANOVA between thepost-test survey results and the topical post-test results. The learning engagement will be measured based Table 1: RIMMS 2015 Questions Plus Two Rewrites (RIMMS++) RIMMS ARCS Prin- Original RIMMS Ques- RIMMS++ Question Q ciples tion [22] R7 Confidence - As I worked with these huser As I worked with this industry standard Learning re- instructionsi, I was confident tool or practice, I was confident that I quirements that I could learn how to work could learn how to
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to students to connect in teams any time. Another engagement strategy this instructor listed was to have optional sessions like office hours on Friday for discussion and for Q&A. However, they did not continue this due to lack of interest. Table 1 below shows a summary of the engagement strategies that faculty listed as being used in the Hybrid in-person and Hybrid remote learning environments. There was no difference in the strategies listed by faculty based on the gender, years of teaching, and number of online classes taught. TABLE 1 List of Primary Engagement Strategies in ENED 1100 Hybrid LEs Strategies In-Person RemoteDiscussions
deviation, no leverage values greater than 0.2, and values for ' 'Cook's distance above1. Also, the assumption of normality was reasonable, as evident by the inspection of the Q-Qplot.We hypothesized that (1) high impact engagement practices will predict academic achievementgoals; (2) course motivation will predict academic achievement goals; (3) confidence atcompleting a degree will predict academic achievement goals. The regression results indicatedthat the model explained 23.9% of the variance and that the model statistically predictedacademic achievement goals, F(3, 489) = 52.45, p = .001, adjusted R2 = .24. This result indicatesa linear relationship in the population, and the multiple regression model is a good fit for thedata. All three
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asked questions to thateffect, the audience and client were able to probe deeper into the assumptions and analysismethodology for the second team.Mentioned previously, during the proposal presentations, students in the class played the role ofthe client’s engineering team. They were tasked with assessing the proposal pitch from atechnical perspective and reporting to the client on how effective the product was described,assessing the technical merits of the proposed heat transfer analysis, and rating the ability of theteam to complete their stated deliverables. Completing this assessment necessitated the reviewersto ask questions and probe deeper into the presented proposal. An interesting phenomenonemerged between the in class Q&A and the
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for the next week.Table 6: Mapping social and emotional learning concepts to focus group questions (Q) anddaily debriefing self-assessments (SA). Concept Focus Group Question Self-awareness Q3, Q4, Q5, Q6, Q7, SA1, SA2, SA3 Social awareness Q1, Q5, SA1 Self-management SA4 Relationship management Q4, Q6, Q1, SA2, SA3 3.4 Qualitative Data AnalysisThematic analysis, using techniques described in [15], of undergraduate student feedback wasconducted to pull out the common themes mentioned by the 12 undergraduate participants.Because of the small participant number, reflexive thematic analysis was used in