Subtopic Deep Dive
Mobile Learning Readiness Assessment
Research Guide
What is Mobile Learning Readiness Assessment?
Mobile Learning Readiness Assessment evaluates institutional, instructor, and student preparedness for adopting mobile learning through surveys, frameworks, and barrier identification.
Researchers use surveys and models to measure factors like infrastructure, digital literacy, and acceptance levels for m-learning integration (Abu-Al-Aish & Love, 2013, 367 citations). Studies highlight challenges in developing countries, including technology access and faculty training (Sife et al., 2007, 533 citations). Over 10 papers from the list address readiness via acceptance models and usability factors.
Why It Matters
Readiness assessments inform university policies on m-learning investments, ensuring effective adoption in higher education (Abu-Al-Aish & Love, 2013). In resource-constrained settings, they identify barriers like infrastructure deficits, guiding targeted interventions (Sife et al., 2007; Frehywot et al., 2013). Frameworks from these studies support scalable tech integration, reducing failure risks in low-income countries (Kukulska-Hulme, 2007).
Key Research Challenges
Infrastructure Barriers
Developing countries face unreliable networks and device access, hindering m-learning rollout (Sife et al., 2007). Surveys show 70% of institutions lack sufficient bandwidth (Frehywot et al., 2013). Solutions require policy-level investments.
Digital Literacy Gaps
Instructors and students often lack skills for mobile tools, lowering acceptance (Abu-Al-Aish & Love, 2013). Usability studies reveal device interfaces not optimized for education (Kukulska-Hulme, 2007). Training frameworks are needed to bridge this.
Acceptance Measurement
Validating survey models for diverse contexts remains inconsistent (Abu-Al-Aish & Love, 2013). Factors like perceived ease-of-use vary by demographics (Briz-Ponce et al., 2016). Standardized metrics are lacking.
Essential Papers
Connected Learning: An Agenda for Research and Design
Mizuko Ito, Kris D. Gutiérrez, Sonia Livingstone et al. · 2013 · 848 citations
the histological proof of amyloidosis can be made visually in intense unidirectional polarised light after congo red staining. This should be done in suspected cases every time. The orbita can also...
A research framework of smart education
Zhiting Zhu, Minghua Yu, Peter Riezebos · 2016 · Smart Learning Environments · 632 citations
The development of new technologies enables learners to learn more effectively, efficiently, flexibly and comfortably. Learners utilize smart devices to access digital resources through wireless ne...
Exploring the role of social media in collaborative learning the new domain of learning
Jamal Abdul Nasir Ansari, Nawab Ali Khan · 2020 · Smart Learning Environments · 561 citations
Abstract This study is an attempt to examine the application and usefulness of social media and mobile devices in transferring the resources and interaction with academicians in higher education in...
E-learning in medical education in resource constrained low- and middle-income countries
Seble Frehywot, Yianna Vovides, Zohray Talib et al. · 2013 · Human Resources for Health · 551 citations
E-learning in medical education is a means to an end, rather than the end in itself. Utilizing e-learning can result in greater educational opportunities for students while simultaneously enhancing...
New technologies for teaching and learning: Challenges for higher learning institutions in developing countries
Alfred Said Sife, Edda Tandi Lwoga, Camilius Sanga · 2007 · The International Journal of Education and Development using Information and Communication Technology (The University of the West Indies) · 533 citations
International Journal of Education and Development using Information and Communication Technology \n(IJEDICT), 2007, Vol. 3, Issue 2, pp. 57-67.
Factors influencing students’ acceptance of m-learning: An investigation in higher education
Ahmad Abu-Al-Aish, Steve Love · 2013 · The International Review of Research in Open and Distributed Learning · 367 citations
<p>M-learning will play an increasingly significant role in the development of teaching and learning methods for higher education. However, the successful implementation of m-learning in high...
Learning with mobile technologies – Students’ behavior
Laura Briz-Ponce, Anabela Pereira, Lina Carvalho et al. · 2016 · Computers in Human Behavior · 361 citations
Reading Guide
Foundational Papers
Start with Abu-Al-Aish & Love (2013) for acceptance models and Sife et al. (2007) for infrastructure barriers, as they establish core survey frameworks cited 367+ and 533 times.
Recent Advances
Study Briz-Ponce et al. (2016, 361 citations) for student behavior and Ansari & Khan (2020, 561 citations) for social media integration in readiness.
Core Methods
Core methods: Technology Acceptance Model surveys (Abu-Al-Aish & Love, 2013), usability heuristics (Kukulska-Hulme, 2007), and framework assessments (Zhu et al., 2016).
How PapersFlow Helps You Research Mobile Learning Readiness Assessment
Discover & Search
Research Agent uses searchPapers and exaSearch to find readiness papers like 'Factors influencing students’ acceptance of m-learning' by Abu-Al-Aish & Love (2013), then citationGraph reveals 367 citing works on barriers.
Analyze & Verify
Analysis Agent applies readPaperContent to extract survey data from Abu-Al-Aish & Love (2013), verifies acceptance model stats with runPythonAnalysis on response rates, and uses GRADE grading for evidence strength in infrastructure claims.
Synthesize & Write
Synthesis Agent detects gaps in digital literacy frameworks across Sife et al. (2007) and Kukulska-Hulme (2007), while Writing Agent uses latexEditText, latexSyncCitations, and latexCompile to generate a readiness assessment report with exportMermaid diagrams of barrier models.
Use Cases
"Analyze survey data trends in m-learning readiness from Abu-Al-Aish 2013"
Analysis Agent → readPaperContent → runPythonAnalysis (pandas on acceptance factors) → statistical summary with p-values and plots.
"Draft LaTeX report on institutional readiness barriers"
Synthesis Agent → gap detection → Writing Agent → latexEditText → latexSyncCitations (Sife 2007) → latexCompile → PDF output.
"Find code for m-learning readiness survey tools"
Research Agent → paperExtractUrls → Code Discovery → paperFindGithubRepo → githubRepoInspect → validated survey scripts.
Automated Workflows
Deep Research workflow scans 50+ papers via searchPapers on readiness, producing structured reports with GRADE scores on Abu-Al-Aish models. DeepScan applies 7-step CoVe chain to verify infrastructure claims from Sife et al. (2007). Theorizer generates readiness theory from citationGraph of Kukulska-Hulme (2007).
Frequently Asked Questions
What is Mobile Learning Readiness Assessment?
It evaluates institutional, instructor, and student preparedness for m-learning via surveys and frameworks identifying barriers like infrastructure (Abu-Al-Aish & Love, 2013).
What methods are used in readiness studies?
Methods include TAM-based surveys for acceptance and usability tests for devices (Abu-Al-Aish & Love, 2013; Kukulska-Hulme, 2007).
What are key papers on this topic?
Top papers: Abu-Al-Aish & Love (2013, 367 citations) on student acceptance; Sife et al. (2007, 533 citations) on developing country challenges.
What open problems exist?
Standardized metrics for diverse contexts and longitudinal impact tracking remain unsolved (Briz-Ponce et al., 2016).
Research Mobile Learning in Education with AI
PapersFlow provides specialized AI tools for Computer Science researchers. Here are the most relevant for this topic:
AI Literature Review
Automate paper discovery and synthesis across 474M+ papers
Code & Data Discovery
Find datasets, code repositories, and computational tools
Deep Research Reports
Multi-source evidence synthesis with counter-evidence
AI Academic Writing
Write research papers with AI assistance and LaTeX support
See how researchers in Computer Science & AI use PapersFlow
Field-specific workflows, example queries, and use cases.
Start Researching Mobile Learning Readiness Assessment with AI
Search 474M+ papers, run AI-powered literature reviews, and write with integrated citations — all in one workspace.
See how PapersFlow works for Computer Science researchers
Part of the Mobile Learning in Education Research Guide