Students’ Understanding of Their Student Model. In

Abstract. Open Learner Models (OLM) are believed to facilitate students' metacognitive activities in learning. Inspectable student models are a simple but very common form of OLM that grant students opportunities to get feedback on their knowledge and reflect on it. This paper uses individualized surveys and interviews with high school students who have at least three years experience learning with the Cognitive Tutor regarding the inspectable student model in the Tutor. We also interviewed a teacher. We found that: i) students pay close attention to the OLM and report that seeing it change encourages them to learn; ii) there is a significant discrepancy between the students' self-assessment and the system's assessment; iii) students generally rely on the OLM to make judgments of their learning progress without much active reflection. We discuss potential revisions to the student model based on the findings, which aim to enhance students' reflection on and self-assessment of their own learning

Adaptive Rückmeldungen im intelligenten Tutorensystem LARGO

The Intelligent Tutoring System LARGO is designed to help law students learn argumentation skills. The approach implemented in LARGO uses transcripts of oral arguments as learning resources: Students annotate them and create graphical representations of the argument flow. The system encourages students to reflect upon arguments proposed by the attorneys and helps students detect possible weaknesses in their analysis of the dispute. Technically, graph grammar and collaborative filtering algorithms are employed to detect these weaknesses. This article describes how “usage contexts” are determined and used to create adaptive feedback in LARGO. On the basis of a controlled study with the system that took place with law students at the University of Pittsburgh, we discuss to what extent the automatically calculated usage contexts can predict student’s learning gains

How Teachers Conceptualise Shared Control With an AI Co-Orchestration Tool: A Multiyear Teacher-Centred Design Process

Artificial intelligence (AI) can enhance teachers\u27 capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un-planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers\u27 voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co-design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co-orchestration tool, called Pair-Up. We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co-orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI-based tutoring systems. We leveraged qualitative content analysis to examine teachers\u27 views about sharing control with Pair-Up, and we describe high-level insights about the human-AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human-centred learning analytics can be applied to the design of human-AI technologies and share reflections for human-AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co-orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems

Designing Hybrid Human-AI Orchestration Tools for Individual and Collaborative Activities: A Technology Probe Study

Combining individual and collaborative learning is common, but dynamic combinations (which happen as-the-need arises, rather than in pre-planned ways, and may happen on an individual basis) are rare. This work reports findings from a technology probe study exploring alternative designs for classroom co-orchestration support for dynamically transitioning between individual and collaborative learning. The study involved 1) a technology-probe classroom study in an authentic, AI-supported classroom to understand teachers\u27 and students\u27 needs for co-orchestration support over dynamic transitions; and 2) workshops and interviews with students and teachers to get informed feedback about their lived experiences. 118 students and three teachers from a middle school in the US experienced a pairing policy – student, teacher and, AI-controlled pairing policy – (i.e., identifying students needing help and potential helpers) for switching from individual to a peer tutoring activity. This work aims to answer the following questions: 1) How did students and teachers react to these pairing policies?; and 2) What are students\u27 and teachers\u27 desires for sharing control over the orchestration of dynamic transitions? Findings suggest the need for a form of hybrid control between students, teachers, and AI systems over transitions, as well as for adaptivity and adaptability for different classroom characteristics, teachers, and students\u27 prior knowledge

Pedagogical Agents for Fostering Question-Asking Skills in Children

Question asking is an important tool for constructing academic knowledge, and a self-reinforcing driver of curiosity. However, research has found that question asking is infrequent in the classroom and children's questions are often superficial, lacking deep reasoning. In this work, we developed a pedagogical agent that encourages children to ask divergent-thinking questions, a more complex form of questions that is associated with curiosity. We conducted a study with 95 fifth grade students, who interacted with an agent that encourages either convergent-thinking or divergent-thinking questions. Results showed that both interventions increased the number of divergent-thinking questions and the fluency of question asking, while they did not significantly alter children's perception of curiosity despite their high intrinsic motivation scores. In addition, children's curiosity trait has a mediating effect on question asking under the divergent-thinking agent, suggesting that question-asking interventions must be personalized to each student based on their tendency to be curious.Comment: Accepted at CHI 202

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453