Evidence map›Paper›PMID 35645594›Full record

ArticleEducation and information technologies2022

A mobile learning framework for higher education in resource constrained environments.

Safiya Okai-Ugbaje, Kathie Ardzejewska, Ahmed Imran

Open access · hybridAbstract read
In one paragraph

Article in Education and information technologies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
8.6field-weighted citation impact, top 2% of its field
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

2 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
  2. Article
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

3 authors at 2 institutions in 1 country.

Safiya Okai-UgbajeSchool of Information Technology and Systems, University of Canberra, Bruce, Australia.ORCID 0000-0002-4371-2898
Kathie ArdzejewskaLearning and Teaching Office, The University of Notre Dame, Sydney, Australia.ORCID 0000-0002-2508-6758
Ahmed ImranSchool of Information Technology and Systems, University of Canberra, Bruce, Australia.ORCID 0000-0001-8258-3550
University of Canberra · AUThe University of Notre Dame Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is well documented that learning oppourtunities afforded by mobile technology (m-learning) holds great potential to enhance technology-enhanced learning in countries and communities with low socio-economic conditions where web-based e-learning has failed because of limited infrastructure and resources. Despite the potential for m-learning, its actual uptake has been low. The extant literature in this sphere provides some theoretical insight, with evidence of limited on-the-ground practical studies that often do not progress beyond the pilot phase. Failure to embed sustainable learning opportunities has been attributed to the absence of a contextual framework suitable for the heterogeneous nature of many developing countries. This paper thus presents an m-learning framework that considers the sociocultural and socio-economic contexts of low-income economies. The framework is based on a range of studies conducted over four years, including the outcome of two empirical studies conducted in a Nigerian university. Documenting the research underpinning the design provides practitioners and policymakers with a framework for a potentially sustainable strategy for long-term mainstream m-learning integration in higher education in low-income countries.

Indexed as

Higher educationLow-income countries (LMICs)Mobile learningTechnology-enhanced learning

Identifiers

PMID35645594
PMCPMC9127289
OpenAlexW4282936013

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.