Evidence map›Paper›PMID 36619454›Full record

ArticleHeliyon2022

The linguistic differences in concept conveying in English and Chinese xMOOC forums.

Tai Wang, Hercy N H Cheng, Zhiqiang Cai

Abstract read
In one paragraph

Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Tai WangFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, 430079, China.
Hercy N H ChengCenter for General Education, Taipei Medical University, Taipei, 110301, Taiwan.
Zhiqiang CaiWisconsin Center for Education Research, School of Education, University of Wisconsin-Madison, Madison, Wisconsin, 53706, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent studies have found that comments from teaching assistants may encourage interactions in edX-like Massive Open Online Course (xMOOC) forums. However, how concepts from these interactions are conveyed to other xMOOC participants has not received much attention. Therefore, this study focuses on a unidirectional teaching assistant-student xMOOC interaction (TS interaction), a content-related pair including one question from a student and one immediate answer from a teaching assistant. The authors particularly investigate the linguistic features (i.e., concept connectivity, concept concreteness, readability and semantic overlap) of concept conveying in TS interactions with many responses (mTS) and with few responses (fTS). In addition, a language factor (English and Chinese) is also considered. Additionally, the interaction transcripts from science lectures (SL) and political briefings (PB) were used as control groups as two opposite cases of concept conveying. At the concept level, the concept conveying in transcripts were modelled as a graph, and measured by common indicators in graph theory. At the overall level, the concept conveying in transcripts were measured by regular linguistic measuring tools. The results show that interactions with mTS and fTS demonstrate different concept conveying tendencies toward SL and PB in terms of linguistic features in both languages. The results suggest that in both languages, teaching assistants may use mixed concept-conveying strategies to stimulate more follow-up responses in xMOOC forums. These conclusions drawn from TS interactions can be even partially generalized in a larger student-student (SS) interaction dataset.

Indexed as

Concept conveyingLinguistic featuresMOOCTeaching assistant-student interaction

Identifiers

PMID36619454
PMCPMC9816989

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.