Effects of Distance Learning on African-American Students in Engineering Technology Courses During COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Until 2019, many students enrolled in online courses for advantages such as flexibility and financial benefits. Research shows that online students made up 32% of the total enrollment in 2013. The number continued to grow for many majors; however, previous research does not investigate online learning for laboratory-based engineering courses and its effect on minority students. When the US declared COVID-19 as a pandemic in the spring of 2020, many universities in Florida suspended their in-person classes and shifted to online modality. This sudden shift happened in the middle of the semester, affecting students' educational experience and academic performance. This paper investigates the effects of distance learning on the academic performance of African American minority students' population for lecture and laboratory courses in the Electronic Engineering Technology (EET) and Construction Engineering Technology (CET) programs at Florida A&M University. This paper compares students' success in two courses (one lecture and one laboratory) from each major taught over two different modalities: distance learning and in-person learning over three academic terms. The courses selected are Introduction to Robotics and Introduction to Robotics laboratory for EET and Strength of Materials and laboratory for CET. A total of 49 students (22 from EET and 27 from CET) academic performances were measured in those two courses. The effects of student background variables (race, financial background, ease of using, and availability of the internet) and course-related variables (difficulty level of the course, available course-related resources on Canvas, lab-based vs. lecture-based course) on student success were explored through student surveys. To measure students' performance, the academic grades they received in the courses were used. To assess student satisfaction with each course, students had to take surveys. The results indicated that, for lecture-based courses, the performance remained almost similar for both modalities; for laboratory courses, student performance and satisfaction were low for the distance learning modality. Both results indicated that students needed at least some personal interaction for laboratory-based courses to understand and perform the labs. These results provided the Engineering Technology program insights into how laboratory experiments can be more effectively delivered to minority students in distance learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".