An Evaluation of a Pilot Study of the Personal Tutoring Programme in Improving Skills Development at the University of Trinidad and Tobago
Bibliographic record
Abstract
Personal tutoring provides professional skills development and support to students as they matriculate through their respective university programmes. Recently, there has been a growing trend of students facing academic difficulties in their first year of undergraduate study at the University of Trinidad and Tobago (UTT) which have impacted on student retention rates. This paper shares the experiences of a pilot study of the Personal Tutoring Programme (PTP) launched in term 2 (January to May) of the 2018 academic year, at the UTT where five hundred and thirty-two (532) students along with forty-eight (48) personal tutors (PTs) engaged in activities that promoted academic and social support. During term 2, PTs met with their assigned tutees at least once per week. The evaluation of the PTP in skills development was based primarily on pass rates of term 2 courses in the Certificate of Applied Engineering (CAE) and Bachelor of Applied Science Common Year I (BASc Common Yr I). These programmes are used as entry requirements to specialized diploma and bachelor level programmes at UTT. Within the BASc Common Yr I, there were marked improvements in the Engineering Thermodynamics course while in the CAE programme, there were marked improvements in the Applied Engineering Mathematics II course with 100% pass rates in two campuses where it was offered. Student feedback was indicative of the positive impact of the PTP which improved students pass rates with engineering courses that traditionally were difficult for them. Overall the PTP contributed to the holistic development of the students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".