Evidence map›Paper›PMID 39040274›Full record

ArticleHeliyon2024

The effects of multiple intelligence based reading tasks on EFL students reading skills achievements:

Teshale Alemu Gebremeskel, Mebratu Mulatu Bachore, Elias Woemego Bushisho

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Teshale Alemu GebremeskelDepartment of English Language & Literature, Hawassa University, Ethiopia.
Mebratu Mulatu BachoreHawassa University, Ethiopia.
Elias Woemego BushishoHawassa University, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The main purpose of the study was to investigate the effectiveness of multiple intelligence-based tasks (MIBT) in improving university students reading skills and achievements. It employed a quasi-experimental design that primarily relies on a quantitative approach. Accordingly, a total of 60 students, who were selected purposefully, participated in the study as treatment and comparison groups. The experimental process was carried out with reading tasks that were designed in light of a model for teaching the skills using multiple intelligence-driven tasks. The treatment was provided for 12 weeks, in which three lessons were conducted per week. For similar weeks, the comparison group also attended lessons but followed the conventional approach of teaching reading skills. Quantitative methods were employed to analyze the data collected through tests. Specifically, T-tests were used to generate the output for the findings. Prior to analysis, a data normality check was carried out using Shapiro-Wilk tests, and a p value of .05 was used to determine the level of significance. It was found that multiple intelligence-based reading tasks brought a significant difference in the students' higher and lower levels of reading achievements compared to the conventional methods, with the effect size value ranging from moderate for reading aspects (i.e., reading for main ideas and understanding references) to strong for reading aspects (i.e., reading for details, guessing vocabulary, and inferring meanings from a text). The findings suggest that university teachers should use multiple intelligence-driven reading tasks to raise the comprehension levels of struggling readers in the context of the study.

Indexed as

ComprehensionDifferentiated instructionMultiple intelligencesReading

Identifiers

PMID39040274
PMCPMC11261104

What OpenQuestion holds

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LicenceCC BY-NC
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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.