Enhancing Accessibility and Sustainability of HSCI 595, a Tanzanian Experiential Learning Course
Bibliographic record
Abstract
Introduction: Experiential learning is recognized as a crucial component of undergraduate education, yet personal and external constraints can limit inclusive participation. Current literature lacks adequate evidence to guide the optimization of experiential courses for equitable access and financial sustainability. The proposed research investigates strategic enhancements to the HSCI 595 Tanzanian experiential course, with the aim of improving accessibility for diverse student populations while ensuring its long-term financial sustainability. The study focuses on three primary objectives: 1) identifying financial barriers to participation, 2) exploring students’ sociocultural and emotional readiness for international engagement, and 3) assessing perceived facilitators that could improve program inclusivity. Methods: Data were gathered through pre- and post-surveys, capturing both anticipated challenges and actual experiences. The pre-survey elicited participants’ initial concerns regarding finances, cultural adaptation, and personal preparedness, while the post-survey examined how these factors evolved over the course of the program. In addition to closed-ended items about funding sources and socioemotional barriers, open-ended questions solicited reflections on resource gaps, unforeseen expenses, and supportive measures. Participants were also encouraged to identify any physical or mental accessibility-related challenges, and to discuss how these needs were addressed, including the resources or modifications employed in the program. Implications: Although final results are forthcoming, preliminary indications suggest that strategic financial aid, sociocultural orientation, peer support, and institutional facilitators could enhance student participation and accessibility to experiential programs. Through tailoring pre-departure resources to address personal hesitations and logistical complexities, institutions can strengthen student preparedness, and self-efficacy. Furthermore, the long-term viability of such initiatives may depend on sustainable funding models and ongoing evaluation of participant feedback. The findings from this work will guide actionable recommendations to refine the HSCI 595 Tanzanian experiential course, serving as a potential model for other universities seeking to advance equitable and financially secure international learning opportunities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".