Constraints in the use of ICT in teaching – Learning processes in secondary schools In Rongai sub county Kajiado count, Kenya
Notice bibliographique
Résumé
Information communication technology (ICT) is a major drive in most world economies. It has been used in almost all the sectors of the economy. In developed countries like United States and Canada it has been incorporated in the education sectors as a tool for administration, management and in curriculum for both teaching and learning processes in most developing countries like Kenya, hence the study was geared towards secondary schools in Rongai Sub County, seeking to establish constraints in the use of ICT in teaching and learning processes in the area. It also sought to find out the level of ICT infrastructure establishment enhancing learning and teaching, to find out the extent to which teachers and students are endowed with ICT skills for used in teaching and learning process. The findings of the study will contribute information to the policy makers that could help them to formulate their teacher training programmes involving ICTs for education. The study sampled schools using purposive sampling technique using the criteria of the type of schools (boarding, day, mixed, boys or girls). Descriptive survey design was also used since it is concerned with gathering of facts. From the sampled schools an equal number of students, teachers and the principal were selected. Data was collected using questionnaires, interviews and observations. A pre-setting of research tools was carried out in one of the institutions. Data collected was analyzed descriptively using chi square and pearsons’ product moment correlation. Descriptive statistics was also used. The major findings showed that there were no adequate ICT facilities in most schools making it impossible to incorporate ICT in teaching and learning processes. Where ICT facilities were available there was no proper utilization of the facilities partly because of lack of staff. Most of the student seemed to engage in entertainment whenever they access computers mrather than using them for academic benefits. Where facilities were available there was educational programmes nor the internet. It was also found out that most teachers lacked basic computer training hence they need to address this problem. Based on this finding the study recommended that the government should assist schools to have electricity, train more staff in ICT and post them in schools, and also facilitate the provision of more computers in all the schools.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 source (Gemma direct ou Codex distillé), 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 ».