The Challeges and Benefits in The Implementation of E-learning Teaching in Grade 8 Computer Studies: A Case Study of Six Selected Schools in Khomas Region, Namibia
Bibliographic record
Abstract
In order to equip students with technology and 21st-century abilities like problem-solving, creativity, and collaboration, a new model of education that involves altered curriculum, infrastructure, professional development for teachers, textbooks, and examinations have emerged. The use of information technology can help spread knowledge more widely. This is known as e-learning. The teachers and students can benefit from having current information and knowledge. The goal of this study was to investigate the variables that influence the adoption of e-learning at the selected secondary schools in the Khomas region, Namibia where computer studies is taught in grade 8. A mixed method approach was used for the study. Random sampling was used to select the quantitative phase and purposive sampling was used for the qualitative phase. A self-administered questionnaire was used to collect quantitative data from the learners, and semi-structured interviews were conducted to gather qualitative data from the teachers. The population of the study consisted of all the computer studies teachers and grade 8 learners at the six selected schools, with a sample of 12 teachers and 90 learners. The data was analysed using SPSS and thematic analysis was used for the qualitative data. According to both students and teachers, the key benefits of elearning were convenience of time and place, greater degree of trust in adopting e-learning as a teaching and learning instrument, and ease of contact between teachers and students. However, in the same vein, students and educators complained about challenges such as limited computer and internet access, a lack of technical support, and a lack of enthusiasm to use the e-learning platform. The results indicated that lack of training, poor awareness of programs and poor infrastructure, were the contributing factors to the failure of the e-learning implementation in schools. The study recommends that the Ministry of Education should introduce mandatory e-learning courses into the curriculum for all learners, in order to better prepare the learners for e-learning and increase accessibility to it. When e-learning is introduced in schools, stakeholders’ opinions and suggestions in the field of education must be taken into account, such as teachers and education officers in the Ministry of Education
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".