ArticleHeliyon2023
Measuring efficiency and effectiveness of knowledge transfer in e-learning.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With e-learning rapidly gaining popularity, evaluating its effectiveness and efficiency has become a challenge in public education, the public sector, and the corporate sector. Measuring knowledge transfer is crucial in any learning process, but e-learning lacks validated methods for this. Here we examine ways to evaluate that particularly in case of e-learning, conducting a literature review to assess available measurement solutions, developing an evaluation method for knowledge transfer, and validating the method. Using logged data from e-courses, it is possible to assess the knowledge transfer in e-learning. We describe a novel method for classifying effectiveness and efficiency with measured values and measurement instruments. The new measurement method was aligned with a data set of an existing learning management system, and the effectiveness and efficiency of knowledge transfer was analysed using quantitative means, including descriptive statistics, regression modelling, and cluster analysis based on a specific e-learning course. This newly elaborated and validated knowledge transfer measurement technique could be a useful tool for anyone wanting to evaluate e-learning courses and can also serve as a baseline for academics to further develop or implement it on larger empirical datasets.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.