ArticleHeliyon2023
Enhancing students' English language learning via M-learning: Integrating technology acceptance model and S-O-R model.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled 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.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Unraveling EFL students' technology self-efficacy, emotions and behavioral intentions in leveraging large language models: the serial mediation of perceptions and attitudes.BMC psychology · 2026Article
- The effects of responsiveness, perceived warmth, and anthropomorphism on university students' use of conversational AI for learning support: a chain mediation analysis based on S-O-R framework.Frontiers in psychology · 2026Article
- Beyond technology: the dominance of affective factors in explaining speaking ability among Chinese learners using an online platform.Frontiers in psychology · 2026Article
- Development of student intent-based educational chatbot system with adaptive and attentive DTCN on symmetric convolution approach.MethodsX · 2025Article
- Application and effectiveness of blended learning in medical imaging via the technology acceptance model.BMC medical education · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the impact of COVID-19, many university students may not be able to learn English in the physical classroom in a traditional way. Students' English learning effectiveness and outcome were threatened when English learning was forced to turn online. Thus, a variety of technological media and platforms to improve their learning outcomes are in need. Mobile learning (M-learning) that involves interacting with other devices through mobile devices and wireless networks can also be a solution to improve students' online English learning effectiveness. In order to explore the learning behaviors and attitudes of university students when learning English with M-learning, this study integrated technology acceptance model and Stimulus Organism Response model including the concepts of perceived convenience, curiosity and self-efficacy in addition to the original technology acceptance model to verify university students' usage cognition and attitude toward English M-learning. This study disseminated surveys to 10 targeted universities/colleges and collected 1432 valid surveys. This study implemented Smart-PLS 4.0 to examine structural model and verify the hypotheses. Results indicated that perceived convenience have positive impact on perceived ease of use, perceived usefulness and attitude toward using; there is a significant and positive relationship among perceived ease of use, perceived usefulness, attitude toward using and intention to using; curiosity and self-efficacy have positive impact on intention to using. Based on the findings, this study further provides abundant theoretical insights and practical significance on language learning.
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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.