Using Student Video Presentations to Develop Communication Skills
Bibliographic record
Abstract
Abstract Communication skills are one of several professional skills that are required for engineering graduates that pose difficulties for engineering educators. The issues around these skills include what to teach, how to teach them, and how to assess students’ abilities. As part of a curriculum reform project that is a component of a larger department change effort, three required classes of the third year curriculum of an Electrical and Computer Engineering program have added a knowledge integration component that occurs approximately every five weeks. During these integration efforts, students are required to integrate knowledge from the three courses to evaluate the design of a cell phone. The goal of these efforts is to have the students connect the knowledge across the three courses using a practical real-world device. Additionally, students are required to produce short video presentations to demonstrate their abilities in integrating the knowledge and the ability to communicate this via a video presentation. The video presentation assignment includes several components. To provide scaffolding for the student efforts, a couple of high-quality example video presentations are made available. These videos were developed by graduate students involved with the knowledge integration project. After the graduate students developed initial versions of their videos, they were critiqued by several faculty and then finalized and made available to the students. A time limit of 7.5 minutes was given for each video. After producing the videos, students were required to perform an anonymous peer review of three classmates’ presentations. Additionally, one graduate student, not responsible for an example video, then also performed an assessment of the videos. The students were also required to provide guided self-reflections on their communication skills after they had completed their videos and performed and received peer reviews. Herein we present results of the assessment data collected for this project. There are a couple of goals related to the assessment of the videos. First, a comparison is made between the assessments of the GTA and peer assessments with the students’ self-reflections –looking for areas of consistency. Then a second evaluation was performed where a random selection of videos were evaluated by members of an industry advisory board to look for similarities and differences between their evaluation and the in-house, or academic, evaluations. The methodology for this work includes collecting the text-based evaluations from each constituents. These texts were then coded for emergent themes that are compared across the various constituents. The results of this work demonstrate the efficacy of combining peer reviews with self-reflections in the development of students’ communication skills.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".