KP-LAB Knowledge Practices Laboratory -- Guidelines and models on implementing design principles of KP-Lab, application scenarios and best practices v2

deliverablesThis deliverable provides an updated and extended description of the KP-Lab co-design framework as well as a summary of main design methods used so far. The purpose of this deliverable is to explicate the underlying rationale as well as systematic structure of the co-design process. The report starts with a brief outline of the theoretical considerations underlying the design-framework as well as a comparison with current approaches in software-engineering. Against this background, the main methodological challenges are sketched. In the main part of the report the actual co-design framework, including its guiding principles, the overall process framework as well as concrete design practices are depicted. The report ends with an outlook on the next steps to be taken. This document replaces Deliverable 2.1 Guidelines and models on implementing design principles in KPLab, application scenarios and best practice, v.1 submitted at M6

Learning to learn together with CSCL tools

In this paper, we identify Learning to Learn Together (L2L2) as a new and important educational goal. Our view of L2L2 is a substantial extension of Learning to Learn (L2L): L2L2 consists of learning to collaborate to successfully face L2L challenges. It is inseparable from L2L, as it emerges when individuals face problems that are too difficult for them. The togetherness becomes a necessity then. We describe the first cycle of a design-based research study aimed at promoting L2L2. We rely on previous research to identify collective reflection, mutual engagement and peer assessment as possible directions for desirable L2L2 practices. We describe a CSCL tool: the Metafora system that we designed to provide affordances for L2L2. Through three cases in which Metafora was used in classrooms, we describe the practices and mini-culture that actually developed. In all contexts, groups of students engaged either in mathematical problem solving or in scientific inquiry and argumentation. These cases show that L2L2 is a tangible educational goal, and that it was partially attained. We show how the experiments we undertook refined our view of L2L2 and may help in improving further educational practice

in press). Using machine learning techniques to analyze and support mediation of student e-discussions

Abstract. Students are starting to use networked visual argumentation tools to discuss, debate, and argue with one another about topics presented by a teacher. However, this development gives rise to an emergent issue for teachers: how do they support students during these e-discussions? The ARGUNAUT system aim

Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions

Students are starting to use networked visual argumentation tools to discuss, debate, and argue with one another about topics presented by a teacher. However, this development gives rise to an emergent issue for teachers: how do they support students during these e-discussions? The ARGUNAUT system aims to provide the teacher (or moderator) with tools that will facilitate effective moderation of several simultaneous e-discussions. Awareness Indicators, provided as part of a moderator’s user interface, help monitor the progress of discussions on several dimensions (e.g., critical reasoning). In this paper we discuss preliminary steps taken in using machine learning techniques to support the Awareness Indicators. Focusing on individual contributions (single objects containing textual content, contributed in the visual workspace by students) and sequences of two linked contributions (two objects, the connection between them, and the students’ textual contributions), we have run a series of machine learning experiments in an attempt to train classifiers to recognize important student actions, such as using critical reasoning and raising and answering questions. The initial results presented in this paper are encouraging, but we are only at the beginning of our analysis