Evidence map›Paper›PMID 31511813›Full record

ReviewCureus2019

Using Micro-learning on Mobile Applications to Increase Knowledge Retention and Work Performance: A Review of Literature.

Mrigank S Shail

Abstract readReview
In one paragraph

Review in Cureus, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Design, Implementation and Evaluation of Educational Aid Game for Differential Diagnosis of Oral Lesions Through Mobile Learning.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026
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  13. Feasibility and potential effectiveness of the IdentifEYE training programmes to address mental health problems in adults with vision impairment.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2024
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  14. Article
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  16. Microlearning in Diverse Contexts: A Bibliometric Analysis.TechTrends : for leaders in education & training · 2023
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  18. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Mrigank S ShailMedicine, Xavier University School of Medicine, Toronto, CAN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Micro-learning is an educational teaching method used to train users on multiple platforms. This article will provide a brief introduction to the concepts of short-term and long-term memory, and explain how micro-learning can be used to increase retention in learners. Micro-lessons can aid in negating the Ebbinghaus forgetting curve and can use reintroduction to keep retention at significantly higher levels. This process also speeds up the learning process overall because students avoid the phenomenon of mental fatigue. The article cites studies suggesting mental fatigue can cause serious cognitive decline in individual performance. By breaking complex courses into manageable smaller lessons, micro-learning preserves the neurotransmitter cascade for steady neurochemical performance. By using mobile devices, students can pause and continue their micro-lessons with ease. The mobile application also gives them the opportunity to continually check on their performance, and adjust their learning accordingly. Micro-learning on mobile devices also keeps engagement levels high because it utilizes different forms of media to keep users captivated.

Indexed as

ebbinghaus curvelong-term memorymemorymental fatiguemicro-learningneuroplasticityneuroscienceprimacy effectrecency effectshort term memory

Identifiers

PMID31511813
PMCPMC6716752

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.