Stealth Assessment of Self-Regulative Behaviors within a Game-Based Environment

abstract: Students' ability to regulate and control their behaviors during learning has been shown to be a critical skill for academic success. However, researchers often struggle with ways to capture the nuances of this ability, often solely relying on self-report measures. This thesis proposal employs a novel approach to investigating variations in students' ability to self-regulate by using process data from the game-based Intelligent Tutoring System (ITS) iSTART-ME. This approach affords a nuanced examination of how students' regulate their interactions with game-based features at both a coarse-grained and fine-grain levels and the ultimate impact that those behaviors have on in-system performance and learning outcomes (i.e., self-explanation quality). This thesis is comprised of two submitted manuscripts that examined how a group of 40 high school students chose to engage with game-based features and how those interactions influenced their target skill performance. Findings suggest that in-system log data has the potential to provide stealth assessments of students' self-regulation while learning.Dissertation/ThesisM.A. Psychology 201

Child-Robot Interactions for Second Language Tutoring to Preschool Children

In this digital age social robots will increasingly be used for educational purposes, such as second language tutoring. In this perspective article, we propose a number of design features to develop a child-friendly social robot that can effectively support children in second language learning, and we discuss some technical challenges for developing these. The features we propose include choices to develop the robot such that it can act as a peer to motivate the child during second language learning and build trust at the same time, while still being more knowledgeable than the child and scaffolding that knowledge in adult-like manner. We also believe that the first impressions children have about robots are crucial for them to build trust and common ground, which would support child-robot interactions in the long term. We therefore propose a strategy to introduce the robot in a safe way to toddlers. Other features relate to the ability to adapt to individual children's language proficiency, respond contingently, both temporally and semantically, establish joint attention, use meaningful gestures, provide effective feedback and monitor children's learning progress. Technical challenges we observe include automatic speech recognition (ASR) for children, reliable object recognition to facilitate semantic contingency and establishing joint attention, and developing human-like gestures with a robot that does not have the same morphology humans have. We briefly discuss an experiment in which we investigate how children respond to different forms of feedback the robot can give

Child-Robot Interactions for Second Language Tutoring to Preschool Children

In this digital age social robots will increasingly be used for educational purposes, such as second language tutoring. In this perspective article, we propose a number of design features to develop a child-friendly social robot that can effectively support children in second language learning, and we discuss some technical challenges for developing these. The features we propose include choices to develop the robot such that it can act as a peer to motivate the child during second language learning and build trust at the same time, while still being more knowledgeable than the child and scaffolding that knowledge in adult-like manner. We also believe that the first impressions children have about robots are crucial for them to build trust and common ground, which would support child-robot interactions in the long term. We therefore propose a strategy to introduce the robot in a safe way to toddlers. Other features relate to the ability to adapt to individual children's language proficiency, respond contingently, both temporally and semantically, establish joint attention, use meaningful gestures, provide effective feedback and monitor children's learning progress. Technical challenges we observe include automatic speech recognition (ASR) for children, reliable object recognition to facilitate semantic contingency and establishing joint attention, and developing human-like gestures with a robot that does not have the same morphology humans have. We briefly discuss an experiment in which we investigate how children respond to different forms of feedback the robot can give

Gendered Socialization with an Embodied Agent: Creating a Social and Affable Mathematics Learning Environment for Middle-Grade Females

This study examined whether or not embodied-agent-based learning would help middle-grade females have more positive mathematics learning experiences. The study used an explanatory mixed-methods research design. First, a classroom-based experiment was conducted with one hundred and twenty 9th-graders learning introductory algebra (53% male and 47% female; 51% Caucasian and 49% Latino). The results revealed that learner gender was a significant factor in the learners’ evaluations of their agent (η2 = .07), the learners’ task-specific attitudes (η2 = .05), and their task-specific self-efficacy (η2 = .06). In-depth interviews were then conducted with 22 students selected from the experiment participants. The interviews revealed that Latina and Caucasian females built a different type of relationship with their agent and reported more positive learning experiences as compared to Caucasian males. The females’ favorable view of the agent-based learning was largely influenced by their everyday classroom experiences, implying that students’ learning experience in real and virtual spaces was interconnected

The sequence matters: A systematic literature review of using sequence analysis in Learning Analytics

Describing and analysing sequences of learner actions is becoming more popular in learning analytics. Nevertheless, the authors found a variety of definitions of what a learning sequence is, of which data is used for the analysis, and which methods are implemented, as well as of the purpose and educational interventions designed with them. In this literature review, the authors aim to generate an overview of these concepts to develop a decision framework for using sequence analysis in educational research. After analysing 44 articles, the conclusions enable us to highlight different learning tasks and educational settings where sequences are analysed, identify data mapping models for different types of sequence actions, differentiate methods based on purpose and scope, and identify possible educational interventions based on the outcomes of sequence analysis.Comment: Submitted to the Journal of Learning Analytic

A tutorial on machine learning for interactive pedagogical systems

This paper provides a short introduction to the field of machine learning for interactive pedagogical systems. Departing from different examples encountered in interactive pedagogical systems—such as intelligent tutoring systems or serious games—we go over several representative families of methods in machine learning, introducing key concepts in this field. We discuss common challenges in machine learning and how current methods address such challenges. Conversely, by anchoring our presentation on actual interactive pedagogical systems, highlight how machine learning can benefit the development of such systems

AI as a Methodology for Supporting Educational Praxis and Teacher Metacognition

Evidence-based practice (EBP) is of critical importance in education where emphasis is placed on the need to equip educators with an ability to independently generate and reflect on evidence of their practices in situ – a process also known as praxis. This paper examines existing research related to teachers’ metacognitive skills and, using two exemplar projects, it discusses the utility and relevance of AI methods of knowledge representation and knowledge elicitation as methodologies for supporting EBP. Research related to technology-enhanced communities of practice as a means for teachers to share and compare their knowledge with others is also examined. Suggestions for the key considerations in supporting teachers’ metacognition in praxis are made based on the review of literature and discussion of the specific projects, with the aim to highlight potential future research directions for AIEd. A proposal is made that a crucial part of AIEd’s future resides in its curating the role of AI as a methodology for supporting teacher training and continuous professional development, especially as relates to their developing metacognitive skills in relation to their practices