Adaptive Robotic Tutors that Support Self-Regulated Learning: A Longer-Term Investigation with Primary School Children

Self Cite

This work explores a group learning scenario with an autonomous empathic robot. We address two research questions: (1) Can an autonomous robot designed with empathic competencies foster collaborative learning in a group context? (2) Can an empathic robot sustain positive educational outcomes in long-term collaborative learning interactions with groups of students? To answer these questions, we developed an autonomous robot with empathic competencies that is able to interact with a group of students in a learning activity about sustainable development. Two studies were conducted. The first study compares learning outcomes in children across 3 conditions: learning with an empathic robot; learning with a robot without empathic capabilities; and learning without a robot. The results show that the autonomous robot with empathy fosters meaningful discussions about sustainability, which is a learning outcome in sustainability education. The second study features groups of students who interact with the robot in a school classroom for two months. The long-term educational interaction did not seem to provide significant learning gains, although there was a change in game-actions to achieve more sustainability during game-play. This result reflects the need to perform more long-term research in the field of educational robots for group learning.

Section: Robots In Educationmentioning

confidence: 99%

Empathic Robot for Group Learning

Alves‐Oliveira

Sequeira

Melo

et al. 2019

Self Cite

“…Opening up such an internal knowledge base of a system to the user is commonly referred to as an open learner model [11] or system transparency [58,59,61]. Open learner models often take the form of a series of skill meters [10,38,39,57] and have been shown to help students to better regulate their efforts [10] or to improve their problem selection [63]. Transparency about system states has further been demonstrated to enhance trust in the system as a whole by making its behavior more understandable [58,59,61] and, therefore, reducing uncertainty in the user.…”

Section: Scaffolding and Transparency Through Robot's Explanationmentioning

confidence: 99%

Adapt, Explain, Engage—A Study on How Social Robots Can Scaffold Second-language Learning of Children

Schodde

Hoffmann

Stange

et al. 2019

Social robots are increasingly applied to support children's learning, but how a robot can foster (or may hinder) learning is still not fully clear. One technique used by teachers is scaffolding, temporarily assisting learners to achieve new skills or levels of understanding they would not reach on their own. We ask if and how a social robot can be utilized to scaffold second-language learning of children at kindergarten age (4-7 years). Specifically, we explore an adapt-and-explain scaffolding strategy in which a robot acts as a peer-like tutor who dynamically adapts its behavior or the learning tasks to the cognitive and affective state of the child, and provides verbal explanations of these adaptations. An evaluation study with 40 children shows that children benefit from the learning adaptation and that the explanations have a positive effect especially for slower learners. Further, in 76% of all cases the robot managed to "re-engage" children who started to disengage from the learning interaction, helping them to achieve an overall higher learning gain. These findings demonstrate that a social robot equipped with suitable scaffolding mechanisms can increase engagement and learning, especially when being adaptive to the individual behavior and states of a child learner. CCS Concepts: • Human-centered computing → Empirical studies in HCI; • Applied computing → Interactive learning environments; • Computing methodologies → Cognitive robotics; Reasoning about belief and knowledge;

“…Several studies involving personalized robots in learning have also explored methods to sustain engagement for students, for example, by leveraging attention data from wearable sensors [50] or by employing multi-activity switching over the course of several interactions [11]. More recent work has gone farther by investigating a robot tutor that provides personalized feedback and encourages students to build self-regulated learning skills over multiple sessions, demonstrating that students who interacted with the personalized robot that provided scaffolding were able to more successfully enhance their self-regulated learning skills [25]. These studies address the effects of various supportive mechanisms within a learning interaction, whereas our work aims to understand the effect of content-based personalization with a robot tutor over multiple sessions on learning outcomes.…”

Section: Personalized Robot Tutoringmentioning

confidence: 99%

The Effect of Personalization in Longer-Term Robot Tutoring

Leyzberg

Ramachandran

Scassellati

2018

The benefits of personalized social robots must be evaluated in real-world educational contexts over periods of time longer than a single session to understand their full potential to impact learning outcomes. In this work, we describe a personalization system designed for longer-term personalization that orders curriculum based on an adaptive Hidden Markov Model (HMM) that evaluates students' skill proficiencies. We present a study investigating the effectiveness of this system in a five-session interaction with a robot tutor, taking place over the course of 2 weeks. Our system is evaluated in the context of native Spanish-speaking firstgraders interacting with a social robot tutor while completing an English Language Learning educational task. Participants either received lessons: (1) ordered by our adaptive HMM personalization system which selects a lesson based on a skill that the individual participant needs more practice with ("personalized condition") or (2) ordered randomly from among the lessons the participant had not yet seen ("non-personalized condition"). We found that participants who received personalized lessons from the robot tutor outperformed participants who received non-personalized lessons on a post-test by 2.0 standard deviations on average, corresponding to a mean learning gain in the 98th percentile.