Evidence map›Paper›PMID 35098109›Full record

ArticleFrontiers in artificial intelligence2021

Keep Calm and Do Not Carry-Forward: Toward Sensor-Data Driven AI Agent to Enhance Human Learning.

Kshitij Sharma, Serena Lee-Cultura, Michail Giannakos

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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.

Kshitij SharmaDepartment of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
Serena Lee-CulturaDepartment of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
Michail GiannakosDepartment of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of Multimodal Data (MMD) and embodied learning systems (such as Motion Based Educational Games, MBEG), can help learning researchers to better understand the synergy between students' interactions and their learning experiences. Unfolding the dynamics behind this important synergy can lead to the design of intelligent agents which leverage students' movements and support their learning. However, real-time use of student-generated MMD derived from their interactions with embodied learning systems (MBEG in our case) is challenging and remains under-explored due to its complexity (e.g., handle sensor-data and enable an AI agent to use them). To bridge this gap, we conducted an

Indexed as

AI agenteducational technologieslearning analyticsmotion-based gamesmulti-modal datasensors

Identifiers

PMID35098109
PMCPMC8790027

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.