Metafora: A Web-based Platform for Learning to Learn Together in Science and Mathematics

This paper presents Metafora, both a platform for integrated tools as well as an emerging pedagogy for supporting Learning to Learn Together in science and mathematics education. Our goal is to design technology that brings education to a higher level; a level where students not only learn a subject matter, but also gain a set of critical skills needed to engage in and self-regulate collaborative learning experiences in science and math education. We first discuss the core skills we hope students will gain as they learn to learn together. We then present our design and implementation that can achieve this goal; a platform and pedagogy we have developed to support the learning of these skills. Finally, we present an example use of our system based on results from pilot studies that demonstrates interaction with the platform, and potential benefits and limitations of the tools in promoting the associated skills

Learning Design Engines as Remote Control to Learning Support Environments

Commentary on: Chapter5: An Architecture for Learning Design Engines. (Vogten, Koper, Martens and Tattersall, 2005) Abstract: Chapter 5 of the Learning Design book describes the operational model of a learning design engine based on the concept of finite automata with output alphabet. We rely on this event concept to include pre-existing learning tools in flexibe and rich learning designs. We sketch an approach for the integration of complex learning environments in learning designs. Interactive learning support environments, such as argumentation or modelling tools are pre-existent and have a high potential when integrated in learning designs. We propose an approach that aims at a clear separation of the learning design engine, the specification of the learning flow (as LD documents) and learning environments. According to its current state, the engine controls the learning environment with events (such as "start a new phase"), defined as a vocabulary for a set of environments, that are mapped to the environments' existing functionality (such as "create new workspace"). Thus the engine remotely controls the learning tools while the tools can initiate state transitions in the engine on specific events in the tool. Editors: Colin Tattersall and Rob Koper

Evaluating relevance of educational resources of social and Semantic Web

The social web paradigm has modified the way people behave on the Web. Amongst the many consequences of this change the amount of online resources directly produced and shared by users has increased considerably. In this scenario the importance of methods to evaluate the educational relevance of the resources raises up. In this poster we propose an approach based on recent advancements of Linked Open Data. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40814-4_8

Diagnosefähigkeiten digital erlernen – das Projekt FALEDIA

Internationale Vergleichsuntersuchungen weisen wiederkehrend auf ausbaufähige und im Trend weitgehend stagnierende Leistungen von Schüler:innen im Mathematikunterricht hin (Selter et al., 2020). Als eine mögliche Ursache wird hierfür das Ausbleiben diagnosegeleiteter Förderung ausgemacht (Prediger, 2009). Zur Steigerung von Diagnosefähigkeiten von (angehenden) Lehrkräften für die Primarstufe im Fach Mathematik wird im Projekt FALEDIA eine digitale Lernplattform entwickelt und erforscht

Entwicklung, Erprobung und Erforschung einer digitalen, fallbasierten Lernplattform zur Steigerung der Diagnosefähigkeit für die Lehrerbildung Mathematik Primarstufe

Im Verbundprojekt FALEDIA wird eine digitale, fallbasierte Lernplattform zur Steigerung der Diagnosefähigkeiten für die Lehrerinnen- und Lehrerbildung gemeinsam von der Technischen Universität Dortmund, der Westfälischen Wilhelms-Universität Münster und der Fachhochschule Dortmund entwickelt, erprobt und erforscht. Im Beitrag wird zur Vorstellung des Projekts zunächst auf die zentralen Bereiche der Diagnosefähigkeiten, des fallbasierten Lernens und der digitalen Lernplattformen eingegangen. Daraufhin erfolgt eine Vorstellung der FALEDIA-Plattform und des Forschungsdesign. Erste Ergebnisse des ersten Zyklus werden vorgestellt, sodass die Ableitung der Überarbeitung der FALEDIA-Plattform erörtert werden kann.

Answering confucius: The reason why we complicate

Learning is a level-progressing process. In any field of study, one must master basic concepts to understand more complex ones. Thus, it is important that during the learning process learners are presented and challenged with knowledge which they are able to comprehend (not a level below, not a level too high). In this work we focus on language learners. By gradually improving (complicating) texts, readers are challenged to learn new vocabulary. To achieve such goals, in this paper we propose and evaluate the 'complicator' that translates given sentences to a chosen level of higher degree of difficulty. The 'complicator' is based on natural language processing and information retrieval approaches that perform lexical replacements. 30 native English speakers participated in a user study evaluating our methods on an expert-tailored dataset of children books. Results show that our tool can be of great utility for language learners who are willing to improve their vocabulary. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40814-4_45.TERENCEEC/FP

Specifying computer-supported collaboration scripts

Collaboration scripts are activity programs which aim to foster collaborative learning by structuring interaction between learners. Computer-supported collaboration scripts generally suffer from the problem of being restrained to a specific learning platform and learning context. A standardization of collaboration scripts first requires a specification of collaboration scripts that integrates multiple perspectives from computer science, education and psychology. So far, only few and limited attempts at such specifications have been made. This paper aims to consolidate and expand these approaches in light of recent findings and to propose a generic framework for the specification of collaboration scripts. The framework enables a description of collaboration scripts using a small number of components (participants, activities, roles, resources and groups) and mechanisms (task distribution, group formation and sequencing)

Personalised depression forecasting using mobile sensor data and ecological momentary assessment

Introduction Digital health interventions are an effective way to treat depression, but it is still largely unclear how patients’ individual symptoms evolve dynamically during such treatments. Data-driven forecasts of depressive symptoms would allow to greatly improve the personalisation of treatments. In current forecasting approaches, models are often trained on an entire population, resulting in a general model that works overall, but does not translate well to each individual in clinically heterogeneous, real-world populations. Model fairness across patient subgroups is also frequently overlooked. Personalised models tailored to the individual patient may therefore be promising. Methods We investigate different personalisation strategies using transfer learning, subgroup models, as well as subject-dependent standardisation on a newly-collected, longitudinal dataset of depression patients undergoing treatment with a digital intervention (N=65 patients recruited). Both passive mobile sensor data as well as ecological momentary assessments were available for modelling. We evaluated the models’ ability to predict symptoms of depression (Patient Health Questionnaire-2; PHQ-2) at the end of each day, and to forecast symptoms of the next day. Results In our experiments, we achieve a best mean-absolute-error (MAE) of 0.801 (25% improvement) for predicting PHQ-2 values at the end of the day with subject-dependent standardisation compared to a non-personalised baseline (MAE=1.062). For one day ahead-forecasting, we can improve the baseline of 1.539 by 12% to a MAE of 1.349 using a transfer learning approach with shared common layers. In addition, personalisation leads to fairer models at group-level. Discussion Our results suggest that personalisation using subject-dependent standardisation and transfer learning can improve predictions and forecasts, respectively, of depressive symptoms in participants of a digital depression intervention. We discuss technical and clinical limitations of this approach, avenues for future investigations, and how personalised machine learning architectures may be implemented to improve existing digital interventions for depression

QoS-Enabled B2B Integration

Business-To-Business Integration (B2Bi) is a key mechanism for enterprises to gain competitive advantage. However, developing B2Bi applications is far from trivial. Inter alia, agreement among integration partners about the business documents and the control flow of business document exchanges as well as applying suitable communication technologies for overcoming heterogeneous IT landscapes are major challenges. At the same time, choreography languages such as ebXML BPSS (ebBP), orchestration languages such as WS-BPEL and Web Services are promising to provide the foundations for seamless interactions among business partners. Automatically translating choreography agreements of integration partners into partner-specific orchestrations is an obvious idea for ensuring conformance of orchestration models to choreography models. Moreover, the application of such model-driven development methods facilitates productivity and cost-effectiveness whereas applying a service oriented architecture (SOA) based on WS-BPEL and Web Services leverages standardization and decoupling. By now, the realization of QoS attributes has not yet received the necessary attention that makes such approaches suitable for B2Bi. In this report, we describe a proof-of-concept implementation of the translation of ebBP choreographies into WS-BPEL orchestrations that respects B2Bi-relevant QoS attributes