Educational environment perception and cognitive load among physical therapy students during e-learning
Zahid Mehmood; Zubair Ahmad; Asad Ullah; Anam Aftab; Junaid Akram; Abdul Haseeb Bhutta; Abdul Wahab · 2023 · The Rehabilitation Journal
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
Background: Understanding students' perception of the educational environment and their cognitive load in this context is crucial for optimizing the effectiveness of e-learning platforms. Objective: To compare educational environment perception and cognitive load in under-graduates Doctor of Physical Therapy (DPT) and post-graduate Master in Science of Physical Therapy (MSPT) students having online learning experiences. Methodology: In this comparative cross-sectional study, data was collected through non-probability convenient sampling from n=274 under-graduates DPT (n=225) and post-graduates
Abstract by Zahid Mehmood; Zubair Ahmad; Asad Ullah; Anam Aftab; Junaid Akram; Abdul Haseeb Bhutta; Abdul Wahab, The Rehabilitation Journal (2023) — 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
t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data
Negative / Null Result ReportMeteorological Causes of the Secular Variations in Observed Extreme Precipitation Events for the Conterminous United States
Negative / Null Result ReportThe Next Generation of Sepsis Clinical Trial Designs
Negative / Null Result ReportAnalysis of DNA Methylation in Young People: Limited Evidence for an Association Between Victimization Stress and Epigenetic Variation in Blood
Negative / Null Result ReportStudy preregistration: an early example and analysis.
Negative / Null Result ReportInsights Into LSTM Fully Convolutional Networks for Time Series Classification
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: DOAJ · DOI 10.52567/trj.v7i02.224