Evidence map›Paper›PMID 35656095›Full record

ArticleFrontiers in artificial intelligence2022

Affective Response Categories-Toward Personalized Reactions in Affect-Adaptive Tutoring Systems.

Alina Schmitz-Hübsch, Sophie-Marie Stasch, Ron Becker, Sven Fuchs, Maria Wirzberger

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2022. 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

5 authors.

Alina Schmitz-HübschFraunhofer Institute for Communication, Information Processing and Ergonomics FKIE, Wachtberg, Germany.
Sophie-Marie StaschUniversity of the Bundeswehr München, Neubiberg, Germany.
Ron BeckerFraunhofer Institute for Communication, Information Processing and Ergonomics FKIE, Wachtberg, Germany.
Sven FuchsFraunhofer Institute for Communication, Information Processing and Ergonomics FKIE, Wachtberg, Germany.
Maria WirzbergerUniversity of Stuttgart, Stuttgart, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Affect-adaptive tutoring systems detect the current emotional state of the learner and are capable of adequately responding by adapting the learning experience. Adaptations could be employed to manipulate the emotional state in a direction favorable to the learning process; for example, contextual help can be offered to mitigate frustration, or lesson plans can be accelerated to avoid boredom. Safety-critical situations, in which wrong decisions and behaviors can have fatal consequences, may particularly benefit from affect-adaptive tutoring systems, because accounting for affecting responses during training may help develop coping strategies and improve resilience. Effective adaptation, however, can only be accomplished when knowing which emotions benefit high learning performance in such systems. The results of preliminary studies indicate interindividual differences in the relationship between emotion and performance that require consideration by an affect-adaptive system. To that end, this article introduces the concept of Affective Response Categories (ARCs) that can be used to categorize learners based on their emotion-performance relationship. In an experimental study,

Indexed as

affect-adaptive systemsaffective user statehierarchical cluster analysisintelligent tutoring systemssafety-critical systems

Identifiers

PMID35656095
PMCPMC9152461

What OpenQuestion holds

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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.