Socio-Economic Realities of Returned Immigrant Reintegration in Ghana: A Systematic Review
Bibliographic record
Abstract
The purpose of this study was to elaborate on the different dimensions to conduct an online assessment in undergraduate mathematics. Mathematics assessment using digital technologies is unique because it has special symbols and multi-step solutions. The sudden shift to full online learning necessitated Learning Management System-based online assessment as part of learning and for certification. The research design for this study was a qualitative case study. The case considered for this study was a calculus course and the 180 students registered for the course in 2021 at a South African university. The researcher was the instructor for this course. After explaining the details of conducting the online assessment of mathematics, data was collected on students’ experiences in online assessment using questionnaires and interviews. A conveniently chosen sample of 13 students completed the online questionnaire. A further ten students took part in the telephonic interviews and these were selected conveniently again. Data was composed of written responses to the questionnaires and the transcriptions of the interview audio recordings. After sitting both quizzes and assignments on Blackboard, students preferred the assignment format. This was also suitable for mathematics where there is a need to show steps and proofs in the solution process for both formative and summative assessment. Course instructors hence can effectively administer appropriate formative online assessments on the Learning Management System that has the potential to propel the teaching and learning of mathematics. Keywords: Learning Management System; undergraduate mathematics; online assessment; quizzes; assignments; Blackboard; formative assessment
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".