Digital learning in schools: Which skills do teachers need, and who should bring their own devices?
Anne Lohr; Michael Sailer; Matthias Stadler; Frank Fischer · 2024 · Teaching and Teacher Education
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
We investigated factors that are potentially associated with teaching and learning with digital technology, by replicating and extending Sailer, Murböck, and Fischer's (2021) study with a representative sample of 407 German secondary school teachers. In line with the replicated study, teachers' technology-related teaching skills were crucial for different forms of students' active learning, whereas the digital technology equipment available in a school was less important. School support was positively related to successful digital teaching and learning at schools. The success of Bring-Your-Own
Abstract by Anne Lohr; Michael Sailer; Matthias Stadler; Frank Fischer, Teaching and Teacher Education (2024) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Leakage and the reproducibility crisis in machine-learning-based science
Negative / Null Result ReportDefining and detecting quantum speedup
Negative / Null Result ReportService robots in hotels: understanding the service quality perceptions of human-robot interaction
Negative / Null Result ReportBoosting methods for multi-class imbalanced data classification: an experimental review
Negative / Null Result ReportFINANCIAL DEVELOPMENT AND ECONOMIC GROWTH: A META‐ANALYSIS
Negative / Null Result ReportThe impact of site-specific digital histology signatures on deep learning model accuracy and bias
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1016/j.tate.2024.104788
