Evidence map›Paper›PMID 34568814›Full record

ArticleFrontiers in artificial intelligence2021

Agent-Supported Peer Collaboration in MOOCs.

Stergios Tegos, Apostolos Mavridis, Stavros Demetriadis

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Stergios TegosSoftware and Interactive Technologies Lab, School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Apostolos MavridisSoftware and Interactive Technologies Lab, School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Stavros DemetriadisSoftware and Interactive Technologies Lab, School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While massive open online courses (MOOCs) can be effective in scaling education, orchestrating collaborative learning activities for large audiences remains a non-trivial task that introduces a series of practical challenges, such as the lack of adequate human support. Even when collaboration takes place, there is uncertainty whether meaningful interactions will occur among learners. This work presents the architecture of a prototype system called PeerTalk. The system was created to enable instructors to easily incorporate real-time collaborative learning activities into their online courses. Furthermore, PeerTalk employs a conversational agent service that aims to scaffold students' online collaboration and provide valuable guidance, which can be configured by the course instructor. In order to investigate the user-acceptance of the system, two evaluation studies took place. The first one involved a group of experts, i.e., MOOC instructors who are expected to use such a system in their course, whereas the second study featured 44 postgraduate students. The study findings were encouraging in terms of the system efficiency and usability levels, laying the foundation for a conversational agent service, which can effectively scale the support of the teaching staff and be easily integrated in MOOC platforms, creating further opportunities for valuable social interaction among learners.

Indexed as

collaborative learningconversational agentinteractionMOOCsupport

Identifiers

PMID34568814
PMCPMC8461054

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.