Utilization of Blogging Platforms and Acquisition of Entrepreneurial Skills for Self-Reliance Among Educational Technology Students in University of Calabar
Bibliographic record
Abstract
In this rapidly changing world, the quest for self-reliance has always been the desire of many nations especially in the present face of global economic challenge. This study centres on using blogging platforms for acquiring Entreprneurial skills for Self-reliance among Educational Technology Students in University of Calabar. The study utilized the descriptive survey design. Three research questions were raised and three research hypotheses were stated to guide the study. The population comprised all the 39 final year educational technology students from university of calabar. Thirty-nine final year Educational Technology students were purposively adopted as sample size for the study. Linear regression was used to test the hypotheses at 0.05 level of significance. The model summary table was used to answer research questions. The instrument for data collection was the utilization of blogging platforms for acquiring entrepreneurial skills for self-reliance questionnaire (UBPFESRQ). The instrument was face validated with a reliability coefficient of .80 using Chronbach Alpha Statistics. It was found that the Use of blogger, wordpress and steemit had no significant relationship with acquisition of entrepreneurial skills for self-reliance among educational technology students in University of Calabar. Some recommendations were made to include that emphasis should be laid on entrepreneurial skills and educational technology tools such as design, production and modernization of indigenous resources among students through practices and innovations for achieving self-reliant generation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".