experiments. Studentsperform laboratory experiments with the help of laboratory instructor as a part of teams whichoften range from two to four members. Such formative assessment is very useful and suitable[3]. However, it may not be sufficient in determining individual student learning of requiredpractical skills as students work in teams and also seek help from laboratory instructor duringthese experiments.In this paper, authors will show through laboratory examination results that good scores forindividual laboratory experiments do not always reflect good results of an individual student’slaboratory practical skills. Laboratory examination helps identify the students struggling withpractical skills. This allows instructor and struggling students
. Thisdata suggests that topics students spent more hands-on time with resulted in better performance.IntroductionAccording to the Bureau of Labor and Statistics, the average person has 10 jobs by the age of 40[1]. This can be seen in Engineering and also reflected in what Engineering graduates are doingfive and ten years post degree[2], [3] . Further, nearly 25% of the Best Performing CEOs startedwith a B.S. in Engineering [4]. Industry continues to ask for more well-rounded competencies ofnew Engineers. The T-shaped engineer combines a depth of engineering technical knowledgewith broad knowledge across domains such as business, communications, entrepreneurship, andethics [2], [5]. Fostering 21st century skills ensures Engineers are equipped to
, the Mixed Circuits LogicControls Lab is using the latest modeling hardware and software, the NI Elvis II workstations withMultisim electrical simulation environment. However, these workstations are prohibitivelyexpensive for home use by students.The course student learning outcomes (SLOs) with their connections to ABET Student Outcomes,as well as grading policies and metrics, are described in [22 and 23]. Students start labs by workingin pairs. When done, students write lab reports consisting of two parts, design descriptions (writtenas a pair) and self-reflections (written individually).Digital Logic Controller Lab Design Problem and Laboratory Environment Changes The Digital Logic Controller Lab consists of two design problems. The
responses regarding working electronics knowledge at end of course.The student data shows that the majority of students somewhat agreed, agreed, or strongly agreedwith the statement they had enough working knowledge to independently create functionalsensors and actuators systems controlled via code on their own. The student responses weregiven a weighted value of 1-strongly disagree, 2-disagree, 3-somewhat disagree, 4-somewhatagree, 5-agree, and 6-strongly agree; the weighted average of the scores was 4.9. This positiveself-reflection learning outcome was especially meaningful given data was taken in a semesterwhere students were fully online given the COVID crisis for this normally hands-on makerspacebased course.Qualitative Analysis of Impact of
and exploring the sensor response for different relevant testparameters such as sensor (probe) size and characteristics such as frequency and type (absolutevs. differential) as well as test material properties (see the example for ET in Figure 1). In thisexercise, the students are first asked to predict the sensor (probe) response (based on what theyhave learned in the lectures and reading materials) and then calculate the response using thesimulation software (Figure 2). Afterwards, the students are asked to analyze the response inlight of their initial predictions and reflect on any mismatch. In this first exercise, the studentsonly study the probe physics and not the probe interaction with a flaw, which will be explored inthe second
described the experiments. For the first year laboratory, we collected twoassignments—one that asked students to propose their acid mine drainage remediation design(considering both cost and effectiveness) and another that asked students to record the data theygenerated (in the face-to-face or simulated experiment), conduct analysis, and propose revisionsbased on their results. In the junior laboratory, we collected students’ analysis of their data(generated in the simulation or face-to-face experiment), the final short laboratory report theywrote about the experiment, and the reflective essays students did at the end of the semesterwhere they were asked, in part, to think about what they learned in this lab. In both cases, wealso collected field
. In thearea of model validation, students have a purely simulation-based lab where they compare firstand second order models of the device, to determine under what conditions the motor inductancecould be neglected, and are given the Qube’s inductance and asked to justify their choice ofmodel for it. In the next lab, they utilize the device itself, and compare experimental data to thetheoretical performance that they expect, then are asked to reflect on the reasons fordiscrepancies. In a later lab, students develop a controller for the Qube. They are given a set ofspecifications that the controller should achieve; these differ from one term to the next, buttypically involve parameters such as overshoot and settling time. They are then asked
Reality course taught in the Computer Science Department at the same university, oras part of independent research projects involving electrical and computer engineering students.This reflects the strong educational impact of this project, as it allows students to contribute to theeducational experiences of their peers. During phase IV, the VR experiences are played bydifferent types of audiences that fit the player type. The team collects feedback and, if needed,implements changes.The pilot VR Lab, introduced as an additional instructional tool for the E&M course during Fall2019 and Spring 2020, engaged over 100 students in the program, where in addition to the regularlectures, students attended one hour per week in the E&M VR lab
acoustic reverberation times of a room intwo states: either relatively empty or with added materials to absorb sound. The reverb time wascalculated using both the full bandwidth recorded signal and a filtered signal using the standard250 Hz octave band. The learning objectives were to: 1) Read and apply key, excerpted aspectsof a test standard (in this case, ISO 3382-2 [12]); 2) Adapt a test standard to an at-homeenvironment and identify key differences with the test standard; 3) Record acoustic data andperform simple processing using MATLAB to calculate the reverberation time of a room; 4)Design a digital bandpass filter to filter data into the 250 Hz acoustic octave band [13]; 5)Generate conclusions on the acoustic absorptivity and reflectivity
and Physiology I 25 Heart rate Measurement CEGR 324 Structural Analysis and Lab (Sec 1) 9 Stresses and Strains CEGR 324 Structural Analysis and Lab (Sec 2) 6 Stresses and Strains IEGR 305 Engineering Thermodynamics (Sec 1) 10 Specific Heat Capacity IEGR 305 Engineering Thermodynamics (Sec 2) 23 Specific Heat Capacity PHYS 206 University Physics II 23 Sound/Reflection and Refraction of Light TRSS 414 Traffic Engineering 30 SoundMSLQ AnalysisThe Motivated Strategies for
distanceReferences: 1. The Chronicle of Higher Education. “Covid-19 Has Forced Higher Ed to Pivot to Online Learning. Here Are 7 Takeaways So Far”. https://www.chronicle.com/article/covid-19-has-forced-higher- ed-to-pivot-to-online-learning-here-are-7-takeaways-so-far/. 2. David Christian and Danny McCarthy. “Experiential Education during the COVID-19 Pandemic: A Reflective Process.” Journal of Constructivist Psychology, DOI: 10.1080/10720537.2020.1813666 3. Fiseha M. Guangul, et al. “Challenges of remote assessment in higher education in the context of COVID-19: a case study of Middle East College” Educ Assess Eval Account. 2020 Oct 21 : 1–17. DOI: 10.1007/s11092-020-09340-w 4. Arizona State University, “Teach Online
engineering. This activity alsohas similarities to work presented by ASEE colleagues [7]. Students chose an existing part andobserved the process while the course instructor scanned their parts. The scan data was providedto the students who were then challenged to convert this scan data to a solid model and then (ifsuccessful), edit the solid model. This activity was designed to fail; the success of this activitywas very dependent on what the students chose to try to scan. Transparent surfaces cause gaps inthe scan, reflective surfaces skew data, and flat horizontal surfaces are impossible to capture in ascanner that does not have the capabilities to adjust the angle of the laser. Students who capture agood scan struggle to convert their scans to a
learn the material and could complete the experiment without instructor intervention.Henke et al [4] used a hybrid approach where students are able to design control algorithms tocontrol electro-mechanical models in the online lab. In this format, the experiment actually takesplace, and the data reflects interactions between physical devices, not virtual entities. However,these remote web-accessible laboratories are in some respect similar to simulations in that thestudent does not have to be co-located with a particular piece of laboratory apparatus. Nedic et al.[5] developed remotely controlled labs called NetLab that allows multiple students to run anexperiment remotely in real time. Amiguid et al. [6] evaluated 100 web-based remote labs