Language Technologies for Lifelong Learning

Brief overview of language technologies for lifelong learning and the work of the LTfLL project

Translating Learning into Numbers: A Generic Framework for Learning Analytics

With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning and Knowledge Analytics as well as the consequences for learning and teaching are still far from being understood. In this paper, we explore the key dimensions of Learning Analytics (LA), the critical problem zones, and some potential dangers to the beneficial exploitation of educational data. We propose and discuss a generic design framework that can act as a useful guide for setting up Learning Analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. Furthermore, the presented article intends to inform about soft barriers and limitations of Learning Analytics. We identify the required skills and competences that make meaningful use of Learning Analytics data possible to overcome gaps in interpretation literacy among educational stakeholders. We also discuss privacy and ethical issues and suggest ways in which these issues can be addressed through policy guidelines and best practice examples

Dimensions of Mobile Augmented Reality for Learning: A First Inventory

Specht, M., Ternier, S., & Greller, W. (2011). Dimensions of Mobile Augmented Reality for Learning: A First Inventory. Journal of the Research for Educational Technology (RCET), 7(1), 117-127. Spring 2011.This article discusses technological developments and applications of mobile augmented reality (AR) and their application in learning. Augmented reality interaction design patterns are introduced and educational patterns for supporting certain learning objectives with AR approaches are discussed. The article then identifies several dimensions of a user context identified with sensors contained in mobile devices and used for the contextualization of learning experiences. Finally, an AR game concept, “Locatory”, is presented that combines a game logic with collaborative game play and personalized mobile augmented reality visualization

D2.2.1 Evaluation Framework

Drachsler, H., Greller, W., & Stoyanov, S. (2013). D2.2.1 Evaluation Framework. LinkedUp project. Heerlen, The Netherlands.The main purpose of the current deliverable D2.2.1 is to hold the current version of the Evaluation Framework and to operationalise it for the LinkedUp challenge judges into a concrete evaluation instrument. This deliverable is not intended as a very elaborated report rather than a summary of the current version of the Evaluation Framework based on the extensive studies in deliverable D2.1 – Evaluation Methods and Metrics. D2.2.1will be reconsidered in the final report of WP2 to demonstrate the development of the Evaluation Framework during the life cycle of the LinkedUp project. For this purpose it is supportive to have the first version of the Evaluation Framework as a tangible outcome and an own entity as conducted in this deliverable.LinkedU

D8.6 Dissemination, training and exploitation results

Mauerhofer, C., Rajagopal, K., & Greller, W. (2011). D8.6 Dissemination, training and exploitation results. LTfLL-project.Report on sustainability, dissemination and exploitation of the LtfLL projectThe work on this publication has been sponsored by the LTfLL STREP that is funded by the European Commission's 7th Framework Programme. Contract 212578 [http://www.ltfll-project.org

Managing IMS Learning Design

Commentary on: Chapter 15: How to Integrate Learning Design into Existing Practice. (Janssen & Hermans, 2005) Abstract: Taking IMS Learning Design (LD) beyond the domain of researchers and programmers, this short paper looks at some of the challenges of mainstreaming it within institutional strategies, processes, and cultures. The experiences of embedding EML at the Open University of the Netherlands will be taken as a reference framework for stimulating managerial attitudes, and thoughts. The paper also intends to provoke some discussion and reflection on cost benefit of Learning Design in a higher education environment. Editors: Colin Tattersall and Rob Koper

Monitoring online biomass with a capacitance sensor during scale-up of industrially relevant CHO cell culture fed-batch processes in single-use bioreactors

In 2004, the FDA published a guideline to implement process analytical technologies (PAT) in biopharmaceutical processes for process monitoring to gain process understanding and for the control of important process parameters. Viable cell concentration (VCC) is one of the most important key performance indicator (KPI) during mammalian cell cultivation processes. Commonly, this is measured offline. In this work, we demonstrated the comparability and scalability of linear regression models derived from online capacitance measurements. The linear regressions were used to predict the VCC and other familiar offline biomass indicators, like the viable cell volume (VCV) and the wet cell weight (WCW), in two different industrially relevant CHO cell culture processes (Process A and Process B). Therefore, different single-use bioreactor scales (50–2000 L) were used to prove feasibility and scalability of the in-line sensor integration. Coefficient of determinations of 0.79 for Process A and 0.99 for Process B for the WCW were achieved. The VCV was described with high coefficients of determination of 0.96 (Process A) and 0.98 (Process B), respectively. In agreement with other work from the literature, the VCC was only described within the exponential growth phase, but resulting in excellent coefficients of determination of 0.99 (Process A) and 0.96 (Process B), respectively. Monitoring these KPIs online using linear regression models appeared to be scale-independent, enabled deeper process understanding (e.g. here demonstrated in monitoring, the feeding profile) and showed the potential of this method for process control. © 2019, The Author(s)