Evidence map›Paper›PMID 36470617›Full record

ArticleTrends in neuroscience and education2022

Navigating learners towards technology-enhanced learning during post COVID-19 semesters.

Chinaza Solomon Ironsi

Abstract read
In one paragraph

Article in Trends in neuroscience and education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  3. 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.

Chinaza Solomon IronsiDeaprtment of English Language Teaching, Near East University, Cyprus. Electronic address: Solomon.chinaza@neu.edu.tr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeİn line with the objective of this special issue to unveil the challenges, opportunities, experiences, and perspectives of learners while they engage with technology during the global pandemic especially in a broader context, this study decides to examine their perceptions on the use of technology-based learning environments during the post covid semesters. This is on the premise that understanding the learners perceptions on using technology for learning after the pandemic unpacks numerous success and challenges which is essential in making propositions that would enhance learners' digital experience and while steering educational policy to the appropriate direction.

designTo achieve this, a mixed-method research design was adopted for this study and 36 participants were purposively selected. Semi-structured interview questions and a 5 point Likert-type questionnaire were used to collect information from the participants on their perceptions on using technology-based instruction during the post-pandemic semesters.

findingsBased on the data that was collected and analyzed, the study unveiled that though technology was perceived to enhance learning among the learners, it significantly creates boredom when used for educational purposes. Other novel findings of this study were instrumental in making valid empirical conclusions on this topic. ORIGINALITY: This study adds to the corpus of studies showing learners' experiences and perspectives on the use of technology for learning during the post-pandemic semester. The study is a starting point for more discussions on the need for developing learner-friendly technologies for better learning experiences among learners.

Indexed as

COVID-19Education, Medical, UndergraduateHumansLearningTechnologyEducationLearnersPost-COVID-19TechnologyTechnology-based instruction

Identifiers

PMID36470617
PMCPMC9476329

What OpenQuestion holds

Textmetadata
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.