Affect-Aware Adaptive Tutoring Based on Human-Automation Etiquette Strategies.

Objective: We investigated adapting the interaction style of intelligent tutoring system (ITS) feedback based on human-automation etiquette strategies.Background: Most ITSs adapt the content difficulty level, adapt the feedback timing, or provide extra content when they detect cognitive or affective...

Descripción completa

Detalles Bibliográficos
Publicado en:Human Factors Vol. 60; no. 4; pp. 510 - 527
Autores principales: Yang, Euijung, Dorneich, Michael C.
Formato: research Journal Article
Publicado: Sage Publications Inc. Jun2018
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129668370&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 129668370
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        00187208
        HMF
      jtl: Human Factors
      issn: 00187208
      maglogo: Y
    pubinfo:
      dt: Jun2018
      vid: 60
      iid: 4
      pid: 344
      pub: Sage Publications Inc.
      place: Thousand Oaks, California
    artinfo:
      ui:
        129668370
        129668370
        NLM29589967
        129668370
        10.1177/0018720818765266
        NLM29589967
        129668370
      ppf: 510
      ppct: 17
      formats:
      tig:
        atl: Affect-Aware Adaptive Tutoring Based on Human-Automation Etiquette Strategies.
      aug:
        au:
          Yang, Euijung
          Dorneich, Michael C.
        affil: Iowa State University, Ames
      sug:
        subj:
          Educational Technology
          Automation
          Affect
          Feedback
          Technology
          User-Computer Interface
          Adult
          Human
          Male
          Female
          Young Adult
          Validation Studies
          Comparative Studies
          Evaluation Research
          Multicenter Studies
          Scales
          Adult: 19-44 years
          Male
          Female
      ab: Objective: We investigated adapting the interaction style of intelligent tutoring system (ITS) feedback based on human-automation etiquette strategies.Background: Most ITSs adapt the content difficulty level, adapt the feedback timing, or provide extra content when they detect cognitive or affective decrements. Our previous work demonstrated that changing the interaction style via different feedback etiquette strategies has differential effects on students' motivation, confidence, satisfaction, and performance. The best etiquette strategy was also determined by user frustration.Method: Based on these findings, a rule set was developed that systemically selected the proper etiquette strategy to address one of four learning factors (motivation, confidence, satisfaction, and performance) under two different levels of user frustration. We explored whether etiquette strategy selection based on this rule set (systematic) or random changes in etiquette strategy for a given level of frustration affected the four learning factors. Participants solved mathematics problems under different frustration conditions with feedback that adapted dynamic changes in etiquette strategies either systematically or randomly.Results: The results demonstrated that feedback with etiquette strategies chosen systematically via the rule set could selectively target and improve motivation, confidence, satisfaction, and performance more than changing etiquette strategies randomly. The systematic adaptation was effective no matter the level of frustration for the participant.Conclusion: If computer tutors can vary the interaction style to effectively mitigate negative emotions, then ITS designers would have one more mechanism in which to design affect-aware adaptations that provide the proper responses in situations where human emotions affect the ability to learn.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N