Integrating ICT in Teacher Education: A Critical Analysis of Digital Tool Usage and Its Effect on Secondary School Students' Performance in Jharkhand
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Notice bibliographique
Résumé
Integrating information and communication technology (ICT) into teacher education is considered vital for improving secondary classroom practice and student outcomes. In Jharkhand, ICT infrastructure in schools has expanded, but concerns remain about how far ICT-related teacher education has led to meaningful digital tool use and measurable gains in student performance. This study examined the extent of ICT integration by secondary school teachers who had received ICT-related training and analysed its perceived effect on students’ academic achievement. A descriptive survey was conducted with 50 secondary school teachers from government and private schools in rural, semi-urban and urban areas of Jharkhand. A researcher-developed questionnaire gathered data on demographics, frequency and type of digital tool use, self-rated level of ICT integration, and teachers’ perceived and estimated impact of ICT on students’ test and examination scores. Data were analysed using frequencies and percentages. Most teachers reported regular use of digital tools: 64% used them at least three to four times per week and 22% once or twice weekly. Presentation software and educational videos were most frequently used, followed by educational apps and learning management systems. Half rated their ICT integration as “moderate” and about one-third as “high”. Seventy-two per cent perceived a positive or strongly positive effect on students’ performance, and around two-thirds estimated average class scores had risen by 6–15%, though about one quarter reported no clear change or were unsure. The findings suggest ICT-oriented teacher education is promoting regular digital tool use and perceived gains in achievement, but practice remains dominated by presentations and videos. Strengthening ongoing professional development in student-centred digital pedagogy and addressing constraints in rural, resource-poor schools is essential for realising ICT’s full potential.
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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 | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 découle