Bridging the Digital Divide and Soft Skills: Professional Development for Underrepresented Students at a Minority-Serving Institution
Bibliographic record
Abstract
In response to persistent inequities in employment outcomes, professors must educate college students attending Minority-Serving Institutions to thrive in competitive industries through robust digital skills training, soft skills development, and resume-building skills. The objectives of the study were to explore students' confidence with digital tools, soft skills, and to determine the effectiveness of the workshop in preparing students for resume building and job interviews. Ten undergraduate students participated in a two-week professional workshop. Using a mixed-methods approach, data were collected on the last day of the workshop. Results of the study indicated that all participants found Grammarly to be very effective, while 90% rated Microsoft Excel and LinkedIn to be very effective. The participants rated communication, group work, and problem-solving as very effective (90%), and 80% rated public speaking, and preparation of resume and interviews as very effective. Participants unanimously rated the workshop highly. Themes that emerged from the study were instructor effectiveness, interaction with the professor and peers, and skill development. The students recommended a longer duration for the workshop. These findings suggest the transformative potential of targeted, hands-on interventions in advancing equity and employability for underrepresented college students.
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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 | 0.001 | 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 | 0.000 |
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".