Evidence map›Paper›PMID 37123892›Full record

ArticleHeliyon2023

The role of pre-class and in-class behaviors in predicting learning performance and experience in flipped classrooms.

Tianjiao Chen, Heng Luo, Peiyu Wang, Xin Yin, Jiaxin Yang

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
25.0field-weighted citation impact, top 1% 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

5 citing papers in PubMed, 26 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Tianjiao ChenFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei 430079, China.
Heng LuoFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei 430079, China.
Peiyu WangFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei 430079, China.
Xin YinFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei 430079, China.
Jiaxin YangFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei 430079, China.
Central China Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The flipped classroom is a well-recognized instructional approach that improves learning performance and experience through two sequential phases: pre-class preparation and in-class collaboration. Despite the large differences between the two phases, few studies have explored their separate effects on flipped learning. This study conducted a hierarchical linear regression to investigate how the two sequential phases and their featured behaviors can predict learning performance and experience in a flipped classroom at the undergraduate level. The results indicated that students' pre-class discussion positively predicted their assignment scores to a moderate degree, while the in-class collaboration was negatively correlated with test scores. However, the two phases had a limited impact on students' flipped learning experience. The results also highlight the influence of students' initial interest and prior achievement on their flipped learning experience. The findings can extend our understanding of the flipped classroom approach and inform its design and implementation in higher education contexts.

Indexed as

Flipped classroomIn-class collaborationLearning experienceLearning performancePre-class discussion

Identifiers

PMID37123892
PMCPMC10130212
OpenAlexW4365136874

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.