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Although there is extensive literature documenting hands-on learning experiences in engineering classrooms, there is a lack of consensus regarding how student learning during these activities compares to learning during online video demonstrations. Further, little work has been done to directly compare student learning for similarly-designed hands-on learning experiences focused on different engineering subjects. As the use of hands-on activities in engineering continues to grow, understanding how to optimize student learning during these activities is critical. To address this, we collected conceptual assessment data from 763 students at 15 four-year institutions. Students completed activities with one of two highly visual low-cost desktop learning modules (LCDLMs), one focused on fluid mechanics and the other on heat transfer principles, using two different implementation formats: either hands-on or video demonstration. Conceptual assessment results showed that assessment scores significantly increased after all LCDLM activities and that gains were statistically similar for hands-on and video demonstrations, suggesting both implementation formats support an impactful student learning experience. However, a significant difference was observed in effectiveness based on the type of LCDLM used. Score increases of 31.2% and 24% were recorded on our post-activity assessment for hands-on and virtual implementations of the fluid mechanics LCDLM compared to pre-activity assessment scores, respectively, while significantly smaller 8.2% and 9.2% increases were observed for hands-on and virtual implementations of the heat transfer LCDLM. In this paper, we consider existing literature to ascertain the reasons for similar effectiveness of hands-on and video demonstrations and for the differing effectiveness of the fluid mechanics and heat transfer LCDLMs. We discuss the practical implications of our findings with respect to designing hands-on or video demonstration activities. 

Hands-on experiments using the Low-Cost Desktop Learning Modules (LCDLMs) have been implemented in dozens of classrooms to supplement student learning of heat transfer and fluid mechanics concepts with students of varying prior knowledge. The prior knowledge of students who encounter these LCDLMs in the classroom may impact the degree to which students learn from these interactive pedagogies. This paper reports on the differences in student cognitive learning between groups with low and high prior knowledge of the concepts that are tested. Student conceptual test results for venturi, hydraulic loss, and double pipe heat exchanger LCDLMs are analyzed by grouping the student data into two bins based on pre-test score, one for students scoring below 50% and another for those scoring above and comparing the improvement from pretest to posttest between the two groups. The analysis includes data from all implementations of each LCDLM for the 2020-2021 school year. Results from each of the three LCDLMs were analyzed separately to compare student performance on different fluid mechanics or heat exchanger concepts. Then, the overall pre- and posttest scores for all three LCDLMs were analyzed to examine how this interactive pedagogy impacts cognitive gains. Results showed statistically significant differences in improvement between low prior knowledge groups and high prior knowledge groups. Additional findings showed statistically significant results suggesting that the gaps in performance between low prior knowledge and high prior knowledge groups on pre-tests for the LCDLMs were decreased on the posttest. Findings showed that students with lower prior knowledge show a greater overall improvement in cognitive gains than those with higher prior knowledge on all three low-cost desktop learning modules. 

Our team has developed Low-Cost Desktop Learning Modules (LCDLMS) as tools to study transport phenomena aimed at providing hands-on learning experiences. With an implementation design embedded in the community of inquiry framework, we disseminate units to professors across the country and train them on how to facilitate teacher presence in the classroom with the LC-DLMs. Professors are briefed on how create a homogenous learning environment for students based on best-practices using the LC-DLMs. By collecting student cognitive gain data using pre/posttests before and after students encounter the LC-DLMs, we aim to isolate the variable of the professor on the implementation with LC-DLMs. Because of the onset of COVID-19, we have modalities for both hands-on and virtual implementation data. An ANOVA whereby modality was grouped and professor effect was the independent variable had significance on the score difference in pre/posttest scores (p<0.0001) and on posttest score only (p=0.0004). When we divide out modality between hands-on and virtual, an ANOVA with an Ftest using modality as the independent variable and professor effect as the nesting variable also show significance on the score difference between pre and posttests (p-value=0.0236 for handson, and p-value=0.0004 for virtual) and on the posttest score only (p-value=0.0314 for hands-on, and p-value<0.0001 for virtual). These results indicate that in all modalities professor had an effect on student cognitive gains with respect to differences in pre/posttest score and posttest score only. Future will focus on qualitative analysis of features of classrooms yield high cognitive gains in undergraduate engineering students.