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As robots are entering into educational fields to enhance children’s learning, it becomes relevant to explore different methods of learning in the area of child–robot interaction. In this article, we present an autonomous educational system incorporating a social robot to enhance children’s handwriting skills. The system provides a one-to-one learning scenario based on the learning-by-teaching approach where a tutor-child assess the handwriting skills of a learner-robot. The robot’s writing was generated by an algorithm incorporating human-inspired movements and could reproduce a set of writing errors. We tested the system by conducting two multi-session studies. In the first study, we assigned the robot two contrasting competencies: ‘learning’ and ‘non-learning’. We measured the differences in children’s learning gains and changes in their perceptions of the learner-robot. The second study followed a similar interaction scenario and research questions, but this time the robot performed three learning competencies: ‘continuous-learning’; ‘non-learning’ and ‘personalised-learning’. The findings of these studies show that the children learnt with the robot that exhibits learning competency and children’s learning and perceptions of the robot changed as interactions unfold, confirming the need for longitudinal studies. This research supports that the contrasting learning competencies of social robots can impact children’s learning differently in peer-learning scenarios.

Neurodevelopmental disorders (NDD) are a group of conditions affecting children’s neurodevelopment with consequences on personal, social, and educational functioning. Social robots have been used in the rehabilitation of children with NDD with encouraging results on learning outcomes. This study aims at understanding how a social robot should act to support caregivers during the rehabilitation of children with NDD. Through a Design-Based-Research approach, we investigate this question by considering the point of view of the most concerned and expert people, i.e., children with NDD and their caregivers. We present here the collaborative and iterative design of R2C3, a social robot used to support caregivers and children during rehabilitation sessions in a learning-by-teaching scenario. 27 caregivers and 6 children participated in the iterative design and/or the evaluation of R2C3, that resulted in the development of a Wizard-of-Oz interface and a library containing 120 robot behaviors. We then studied how caregivers used such behaviors during the rehabilitation sessions. We found they mainly used the robot to provide positive reinforcements to children, to elicit their reflection and knowledge toward shared handwriting activities, and to support children’s error acceptance. However, the utilization of Positive Reinforcement by caregivers tends to decrease significantly as the sessions progress.

Teaching the contents of study materials by providing explanations to fellow students can be a beneficial instructional activity. A learning-by-teaching effect can also occur when students provide explanations to a real, remote, or even fictitious audience that cannot be interacted with. It is unclear, however, which underlying mechanisms drive learning by non-interactive teaching effects and why several recent studies did not replicate this effect. This literature review aims to shed light on when and why learning by non-interactive teaching works. First, we review the empirical literature to comment on the different mechanisms that have been proposed to explain why learning by non-interactive teaching may be effective. Second, we discuss the available evidence regarding potential boundary conditions of the non-interactive teaching effect. We then synthesize the available empirical evidence on processes and boundary conditions to provide a preliminary theoretical model of when and why non-interactive teaching is effective. Finally, based on our model of learning by non-interactive teaching, we outline several promising directions for future research and recommendations for educational practice.

Engaging learners in activities is an important instructional method. However, the learning-by-doing approach also poses some risks. By analyzing the differences between various types of learning activities, issues of activity-based learning are revealed and discussed. Activity-based learning can consist of relatively simple patterns of motor activity or may involve additional task-related knowledge, resulting in complex activities. Excessive movement or failure to properly integrate motor activity into a task can lead to problems for motor activity–based learning. Elaborate activities, such as letting learners generate drawings, can quickly evolve to secondary tasks in their own right. Demanding learning tasks can create their own cognitive load, resulting in less working memory capacity being available for engaging with the actual content. If activity-based interventions are to be used, as many redundant aspects as possible need to be avoided while providing additional guidance to learners. In addition, it is necessary to consider how task demands transform once tasks are shifted from the physical to the digital world in order to properly acknowledge potential increases in cognitive load. Taken together, this review connects educational and cognitive perspectives on activity-based learning to arrive at models and recommendations that are of high relevance for the digital transformation of education and learning.

This study was conducted to meta-analytically investigate the influence of teaching vs. no teaching expectancy on the learning effects of teaching after preparatory learning. A meta-analysis of 39 studies revealed that a weighted mean effect size for the effect of teaching after studying with or without teaching expectancy vs. merely studying without teaching expectancy on one’s learning was g = 0.27, 95% CI [0.15, 0.39]. Most importantly, teaching vs. no teaching expectancy significantly moderated the learning effect of teaching: The learning benefit of teaching after studying with teaching expectancy was nearly medium, g = 0.48, 95% CI [0.34, 0.63], whereas that of teaching after studying without teaching expectancy did not significantly differ from zero, g =  − 0.02, 95% CI [− 0.14, 0.11]. This moderator effect was independent of the effects of two possible confounding factors: comparison treatment (the use of a sophisticated or unsophisticated learning strategy) and teaching mode (teaching in written or unwritten mode). An additional meta-analysis of 14 studies also found that the effect of teaching after studying with teaching expectancy vs. merely studying with teaching expectancy on one’s learning was significantly greater than zero, g = 0.38, 95% CI [0.17, 0.60], ruling out the possibility that the effectiveness of learning by teaching after studying with teaching expectancy is entirely attributable to the learning effects of preparing to teach (i.e., merely studying with teaching expectancy). These findings suggest that preparing to teach catalyzes learning by teaching.

We often understand something only after we’ve had to teach or explain it to someone else. Learning-by-teaching (LBT) systems exploit this phenomenon by playing the role of tutee . BELLA, our sixth-grade mathematics LBT systems, departs from other LTB systems in several ways: (1) It was built not from scratch but by very slightly extending the ontology and knowledge base of an existing large AI system, Cyc. (2) The “teachable agent”—Elle—begins not with a tabula rasa but rather with an understanding of the domain content which is close to the human student’s. (3) Most importantly, Elle never actually learns anything directly from the human tutor! Instead, there is a super-agent (Cyc) which already knows the domain content extremely well. BELLA builds up a mental model of the human student by observing them interact with Elle. It uses that Socratically to decide what Elle’s current mental model should be (what concepts and skills Elle should already know, and what sorts of mistakes it should make) so as to best help the user to overcome their current confusions. All changes to the Elle model are made by BELLA, not by the user—the only learning going on is BELLA learning more about the user—but from the user’s point of view it often appears as though Elle were attending to them and learning from them. Our main hypothesis is that this may prove to be a particularly powerful and effective illusion to maintain.

Teaching other students in a face-to-face manner has been shown to effectively foster both one’s own and their learning. This study experimentally investigated whether and how tutors and tutees academically benefit from three phases of face-to-face teaching: preparing-to-teach, initial-explanation, and interaction phases. Japanese undergraduates ( n = 80) acted as tutors or tutees in peer tutoring. After studying with the expectation of teaching face-to-face or taking a test (the preparing-to-teach phase), tutor participants provided tutee participants with initial instructional explanations, without asking or answering questions (the initial-explanation phase), and then engaged in a question-and-answer period (the interaction phase). Tutor and tutee participants learned better by providing and receiving higher-quality explanations in the initial-explanation and interaction phases. Face-to-face teaching vs. test expectancy had no effects on the quality of tutor participants’ explanations or their learning outcomes. The results suggest that both the initial-explanation and interaction phases contribute to learning by teaching face-to-face, whereas the preparing-to-teach phase does not.

This paper presents an overview of 10 years of research with the Betty’s Brain computer-based learning environment. We discuss the theoretical basis for Betty’s Brain and the learning-by-teaching paradigm. We also highlight our key research findings, and discuss how these findings have shaped subsequent research. Throughout the course of this research, our goal has been to help students become effective and independent science learners. In general, our results have demonstrated that the learning by teaching paradigm implemented as a computer based learning environment (specifically the Betty’s Brain system) provides a social framework that engages students and helps them learn. However, students also face difficulties when going about the complex tasks of learning, constructing, and analyzing their learned science models. We have developed approaches for identifying and supporting students who have difficulties in the environment, and we are actively working toward adding more adaptive scaffolding functionality to support student learning.

The effect of metacognitive scaffolding for learning by teaching was investigated and compared against learning by being tutored. Three versions of an online learning environment for learning algebra equations were created: (1) APLUS that allows students to interactively teach a synthetic peer with a goal to have the synthetic peer pass the quiz while the system provides students with metacognitive scaffolding on how to teach. (2) A plus T utor that provides cognitive tutoring (i.e., immediate feedback and just-in-time hint) and metacognitive scaffolding on how to learn. And, (3) C og T utor + that provides traditional cognitive tutoring on mastery learning. Two school studies were conducted with a total of 444 6th through 8th grade students. 208 students completed the study and were included in the analysis. The results show that (i) students’ proficiency in solving equations increased after using our interventions for 4 days, but there was no difference in the effectiveness across three interventions, and (ii) learning by teaching with metacognitive scaffolding facilitated learning equally across various levels of students’ prior competency.

Collaborative learning by teaching (CLBT) is a pedagogical approach that combines collaborative learning and learning by teaching pedagogy, which can be applied to university classrooms with the support of information and communication technology (ICT). This pedagogy not only emphasizes the independent learning of group members, but also emphasizes the process of collaborative learning and cooperative teaching among group members. For current Chinese college students, even with the support of online learning resources and ICT, CLBT is a relatively difficult task for group members, which needs to be combined with some regulatory strategies. Socially shared learning regulation (SSRL) has attracted widespread attention from educators and researchers as an effective collaborative learning regulation strategy, but so far, there is few studies on the effects of SSRL in CLBT environment, especially on psychological variables. This study explored the effects of SSRL on the computational thinking, learning motivation, engagement, and academic achievement of university students in CLBT by conducting a semester-long quasi-experiment in a data mining course with 72 third-year undergraduates at a Chinese public university. The experimental class adopted SSRL in CLBT with 41 students (33 males, eight females), while the control class only implemented CLBT with 31 students (26 males, five females). The independent sample t-test results showed that the students in the experimental class significantly improved their intrinsic motivation, engagement (Deep processing strategy and affective engagement), and computational thinking (Algorithmic thinking, critical thinking and problem solving) than those in the control class. In addition, the student’s academic achievements in the experimental class were significantly higher than the students in the control class. This study enriches the practical cases of educators and researchers and makes recommendations for future research, such as combining project-based learning approaches with CLBT or investigating the relationship among these psychological variables and academic performance.