Stretching the limits in help-seeking research

This special section focuses on help seeking in a wide range of learning environments, from classrooms to online forums. Previous research has rather restrictively focused on the identification of personal characteristics that predict whether or not learners seek help under certain conditions. However, help-seeking research has begun to broaden these self-imposed limitations. The papers in this special section represent good examples of this development. Indeed, help seeking in the presented papers is explored through complementary theoretical lenses (e.g., linguistic, instructional), using a wide scope of methodologies (e.g., teacher reports, log files), and in a manner which embraces the support of innovative technologies (e.g., cognitive tutors, web-based environments)

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

Behavior Effect of Hint Selection Penalties and Availability in an Intelligent Tutoring System

Proceedings of: Tenth International Confererence on Intelligent Tutoring Systems: Bridges to Learning (ITS 2010). Pittsburg, USA, June 14-18, 2010.his paper presents empirical results about the behavior effect of two different hinting strategies applied on exercises within an ITS: having some penalty on the scoring for viewing hints or not having any effect on the scoring; and hints directly available or only available as a result to an incorrect attempt. We analyze the students' behavior differences when these hinting techniques changed, taking into account the type and difficulty of the presented exercises.Work partially funded by the Learn3 project TIN2008-05163/TSI within the Spanish “Plan Nacional de I+D+I”, and the Madrid regional community project eMadrid S2009/TIC-1650.Publicad

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

Computer-supported collaborative inquiry learning and classroom scripts

This study examined the influence of classroom-script structure (high vs. low) during computer-supported collaborative inquiry learning on help-seeking processes and learning gains in 54 student pairs in secondary science education. Screen- and audio-capturing videos were analysed according to a model of the help-seeking process. Results show that the structure of the classroom script substantially affects patterns of student help seeking and learning gain in the classroom. Overall, students in the high-structured classroom-script condition sought less help but learnt more than those in the low-structured classroom-script condition

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

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

Participatory design to lower the threshold for intelligent support authoring

One of the fundamental aims of authoring tools is to provide teachers with opportunities to configure, modify and generally appropriate the content and pedagogical strategies of intelligent systems. Despite some progress in the field, there is still a need for tools that have low thresholds in terms of the users’ technical expertise. Here, we demonstrate that designing systems with lower entry barrier can potentially be achieved through co-design activities with non-programmers and carefully observing novices. Following an iterative participatory co-design cycle with teachers who have little or no programming expertise, we reflect on their proposed enhancements. Our investigations focus on Authelo, an authoring tool that has been designed primarily for Exploratory Learning Objects, but we conclude the paper by providing transferable lessons, particularly the strong preference for visual interfaces and high-level pedagogical predicates for authoring analysis and feedback rules

Up and down the number line: modelling collaboration in contrasting school and home environments

This paper is concerned with user modelling issues such as adaptive educational environments, adaptive information retrieval, and support for collaboration. The HomeWork project is examining the use of learner modelling strategies within both school and home environments for young children aged 5 – 7 years. The learning experience within the home context can vary considerably from school especially for very young learners, and this project focuses on the use of modelling which can take into account the informality and potentially contrasting learning styles experienced within the home and school