Computer-Aided-Instruction (CAI) as an Innovative Method for Optimizing the Quality of Social Studies Lecturers in Nigerian Tertiary Institutions for Quality Teacher Education in Nigeria
Bibliographic record
Abstract
This study focused on Computer-Aided-Instruction (CAI) as an innovative method for optimizing the quality of Social Studies lecturers in Nigerian tertiary institutions for quality teacher education in Nigeria. To achieve the purpose of this study, three research questions were posed to guide the study. The study adopted descriptive survey research design. The population of the study consisted of all the one hundred and sixty-two (162) Social studies education lecturers in public universities and colleges of education in South-East, Nigeria. A sample of 108 social studies lecturers was drawn for the study, using cluster sampling technique. Relevant data for the study were collected using a “Questionnaire on Effectiveness of CAI in Optimizing the Quality of Social Studies Lecturers in Nigerian Tertiary Institutions”. Data collected were analysed using mean and standard deviation. The findings of the study indicated that: many social studies lecturers are not acquainted with the requisite knowledge and skills for teaching social studies; a good number of social studies lecturers are not acquainted with CAI as an innovative trend for accessing information; and that CAI helps in optimizing the quality of social studies lecturers in Nigerian tertiary institutions. Following the findings of this study, conclusion was drawn and recommendations were made to include that professionally qualified social studies lecturers should be recruited to teach social studies in Nigerian tertiary institutions, social studies lecturers ought to update their computer competences among others.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".