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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».