ReviewHUMAN-ROBOT INTERACTION

Social robots for education: A review

See allHide authors and affiliations

Science Robotics  15 Aug 2018:
Vol. 3, Issue 21, eaat5954
DOI: 10.1126/scirobotics.aat5954

Figures

  • Fig. 1 An overview of data from the meta-analysis.

    (A) Type of learning outcome studied. (B) Role of the robot in the interaction. (C) Number of learners per robot in studies. (D) Division between children and adults (≥18 years old). (E) Age distribution for children. (F) Age distribution for adults.

    CREDIT: ADAPTED BY A. KITTERMAN/SCIENCE ROBOTICS
  • Fig. 2 Diversity of robots in education.

    (A) Types of robots used in the studies. (B) Nations where the research studies were run.

    CREDIT: ADAPTED BY A. KITTERMAN/SCIENCE ROBOTICS
  • Fig. 3 Histograms of effect sizes (Cohen’s d) for all cognitive and affective outcomes of robot tutors in the meta-analysis.

    These combine comparisons between robots and alternative educational technologies but also comparisons between different implementations of the robot and its tutoring behavior. In the large majority of results, adding a robot or adding supportive behavior to the robot improves outcomes.

    CREDIT: ADAPTED BY A. KITTERMAN/SCIENCE ROBOTICS
  • Fig. 4 Illustrative examples of social robots for learning.

    (A) iCat robot teaching young children to play chess (76). (B) Nao robot supporting a child to improve her handwriting (13). (C) Keepon robot tutoring an adult in a puzzle game (27). (D) Pepper robot providing motivation during English classes for Japanese children (74).

Tables

  • Table 1 Common measures for determining cognitive and affective outcomes in robots for learning.
    CognitiveLearning gain, measured as difference between pre- and posttest score
    Administer posttest either immediately after exposure to robot or with delay
    Correct for varying initial knowledge, e.g., using normalized learning gain (77)
    Difference in completion time of test
    Number of attempts needed for correct response
    AffectivePersistence, measured as number of attempts made or time spent with robot
    Number of interactions with the system, such as utterances or responses
    Coding emotional expressions of the learner, can be automated using face analysis software (47)
    Godspeed questionnaire, measuring the user’s perception of robots (78)
    Tripod survey, measuring the learner’s perspective on teaching, environment, and engagement (79)
    Immediacy, measuring psychological availability of the robot teacher (3, 10)
    Evolution of time between answers, e.g., to indicate fatigue (31)
    Coding of video recordings of participants responses
    Coding or automated recording of eye gaze behavior (to code attention, for example)
    Subjective rating of the robot’s teaching and the learning experience (15)
    Foreign language anxiety questionnaire (80)
    KindSAR interactivity index, quantitative measure of children’s interactions with a robot (81)
    Basic empathy scale, self-report of empathy (82)
    Free-form feedback or interviews

Stay Connected to Science Robotics

Navigate This Article