Hendrik Drachsler

Katrien Verbert

BE K U Leuven

Martin Wolpers

Nikos Manouselis

Stefanie Lindstaedt

Frank Fischer

Nikos Manouselies

John Stamper

STELLAR Alpine Rendez-Vous White Paper Workshop 6: dataTEL -Datasets for Technology Enhanced Learning Workshop Organisers

The MAVSEL

Therefore, the main objective of the dataTEL workshop was to explore suitable datasets for TEL with a specific focus on recommender and adaptive information systems that can take advantage of these datasets. In this context, new challenges emerge like unclear legal protection rights and privacy issues, suitable policies and formats to share data, required preprocessing procedures and rules to create sharable datasets, common evaluation criteria for recommender systems in TEL and how a dataset driven future in TEL could look like.

The workshop aimed to bring together TEL researchers, data scientists, and privacy and legal protection experts to:

identify the most pressing topics on educational datasets come-up with achievable objectives to overcome the current issues on educational datasets and potential data applications

The relevant topic of the workshop were:

publicly available datasets for educational systems dealing with legal protection rights towards datasets on a European level privacy preservation for educational datasets methods of effective anonymisation of educational datasets management and pre-processing procedures for educational datasets future scenarios for educational datasets impact of educational datasets for learners and teachers mash-ups based on educational datasets recommender approaches that are based on educational data evaluation methodologies and metrics for educational recommender systems

Workshop description

The participants were invited to submit original unpublished research as papers (4-8 pages) to the workshop. Demonstrations and Hands-on sessions were explicitly encouraged.

Emerging research questions

The workshop focused as much as possible on group discussions and group work rather than individual presentations. Before the actual workshop the participants were asked to submit extended abstracts to the dataTEL group space at TELeurope.org and prepare the and statements for the workshop. participant had 15 minutes to present the main message of his/her research followed by 15 minutes questions and discussions. At the end of each topic session we had an overall discussion that took into the latest presentations. We finalised every topic session with a speed statement round, where each participant were asked to write down main challenges and ideas in one sentence.

we collected these statements and clustered those according to the 4 main topics. In that way we collected a couple of emerging research questions during the workshop for each topic:

Topic: Evaluation of recommender systems in TEL

-Does a common data format for evaluation also require a common format for TEL recommender systems? -Learning is a collaborative process, how can we translate that into evaluation measures for TEL recommender systems? -Do we have to converge to a common evaluation framework or should we diverge a wide range of (accepted) evaluation methods to choose from? -We need metrics that can be applied to every standardized dataset!

Topic: supported learning examples

can we get richer representations of the social context in learning? -How important are visualisations to reflect and learn from a dataset? -Can teachers or students deal with data visualisations to reflect their process? -Which new competences requires data supported learning? -How can we integrate the context of learning into the support systems?

Topic: from Learning Object Repositories and Web content

on the experiences with SCORM and IMS-LD that should have created a European Learning Object market, will a common dataset format really lead to more datasets in TEL? -can we create datasets that capture real-life learner data? -can we overcome the lack of data sharing opportunities? -Do we need a dataset format or rather well documented datasets? or both? -How can we deal with the diversity of data from various TEL systems? What are levels of granularity?

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-We need to create a representative association that requests datasets from the big players in LMS (e.g., Moodle, Blackboard) and learning object repositories (e.g., MERLOT, OERCommons)

4. Topic: Privacy and data protection for dataTEL -Does dataTEL require an ethical discussion on privacy, data protection and surveillance? -We are able to develop a new generation of support tools when we are allowed to track the and behaviour of learners. How can we deal with privacy issues in a practical way? -Data driven research will make unveil information visible that will challenge the way we learn, teach and conducting research.

the second day of the workshop we focused on the description of dataTEL Grand Challenges emerging out of the 4 pressing topics and the research questions. Therefore, we split the workshop into 4 smaller groups that developed a Grand Challenge for every pressing topic.

Grand Challenge Problems

Grand Challenge 1 / Topic 1: Reduce the drop-out rate in online learning environments by 10% through applying well evaluated and tested recommender systems for learning.

problems of the European education system are addressed, and what are the long term benefits for society?

A challenging problem for educational institutes and lifelong learning in general are the high drop-out rates esp. in online and distance education settings. The isolation and confusion of students may cause them to withdraw from their studies. These groups of students are called 'drop-outs'. The research on TEL recommender systems can contribute to decrease the dropout rate by disseminating its research outcomes for the development of different support systems for teachers and students to offer relevant information at the right time. Regarding the drop-out problem it is thinkable to develop a drop-out analyzer that informs the tutor of a (Moodle) course which learners are likely to drop-out. This could be done by training a certain recommender technology on the drop-out patterns of previous (Moodle) courses. The trained analyzer could than be applied on follow-up (Moodle) courses and mark students in a list that show similar drop-out patterns. The tutor of the course could then make an intervention and contact those students personally to offer additional support for their studies. First implantations recommender systems are already available and can be implemented within a year [START_REF] Manouselis | Recommender Systems in Technology Enhanced Learning[END_REF][START_REF] Manouselis | Recommender Systems in Technology Enhanced Learning[END_REF]. More challenging is the evaluation of the recommender systems that will take up to 2 -3 years. For the further development of systems publicly available educational datasets are needed to evaluate and compare different recommendation approaches to gain a solid body of knowledge (5-8 years).

What are measurable progress and success indicators?

Measurable progress and success indicators are depending on the applied type of recommender system (curriculum recommender system, drop-out analyzer etc.) [START_REF] Drachsler | Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning[END_REF]. For the this Grand Challenge a significant decrease of the drop-out rate within an educational institution would be an promising measure to value the impact of such a system. A challenging issue will be to isolate the effect of decreasing drop-out rates only to the recommender system as most educational institutes permanently improve their educational services.

How can funding be attracted?

Next to European and national funding such a research project could be funded by single Universities (Innovation funds) and LMS providers like Blackboard or IMC AG. Next to the commercial providers recommender projects can be initiated as open source project in the Moodle or SAKAI community for instance.

Grand Challenge 2 / Topic 2:

help students and teachers in TEL to use data supported information systems.

What problems of the European education system are addressed, and what are the long term benefits for society?

In order to make data supported information systems an effective tool for educational practice, various limitations and hurdles in technology, privacy and education need to be addressed. It is important to realise that data supported tools work with computational results that are not easy to understand and need to be presented in an easy way (e.g., by visualizations) to address the daily practice of the educational stakeholders. It is crucial to interpret the presented outcomes in a correct manner to take the right follow-up activities that can lead to improved learning. Therefore, the interpretation of educational data and its related tools requires new competences to deal with the outcomes (statistical knowledge, critical thinking, privacy awareness and ethical competences).

are the main activities to address this Grand Challenge Problem?

• Developing new data driven tools that are easy to understand

• Make new real time data tools available as test applications

• Identify suitable algorithms and map them to certain datasets and learning purposes The development of the data tools has already started, on every conference new data driven tools are presented [START_REF] Zhang | Moodog: Tracking Student Activity in Online Course Management Systems[END_REF]. Systems like Mendley and open access journals show us already the future of academic work. The training of the new competences teachers and students in the primary and secondary education level will take more time (5 to 10 years) with having many different levels in the EU partner countries.

What are measurable progress and success indicators?

• An increased effectiveness, efficiency or satisfaction of the learning process • Courses at educational providers that train competences to handle data products

• An increasing amount of data mashup systems for different educational stakeholders (students, teachers, parents and educational providers)

How can funding be attracted?

For the competence training the Lifelong Learning Programme of the EU is suitable. The development of the new data driven tools can be funded by FP7 and national calls.

Grand Challenge 3 / Topic 3: a generic infrastructure for sharing, analyzing and reusing learning resources and learning activity logs (educational datasets) and related research findings.

What problems of the European education system are addressed, and what are the long term benefits for society?

The increased application of LMS, e-portfolio systems, and PLEs in schools and higher education institutions produces large amounts of educational data. But, although these elearning environments store educational data automatically, exploitation of this data for learning and teaching is still very limited. These educational datasets offer an unused opportunity for the evaluation of learning theories, student support, learning technology, and the development of future learning applications. Furthermore, educational datasets can be supportive to advance research on TEL towards a basic theory for TEL [START_REF] Verbert | Dataset-driven Research for Improving Recommender Systems for Learning[END_REF]) by offering the recorded and observed behavior of the stakeholders (students, teachers, parents, lifelong learners, educational institutes) in different learning settings (formalinformal learning). In that way, the educational datasets extend the methodological and empirical approaches to analyze TEL that is dominated by design-based research approaches, simulations, and field studies [START_REF] Gray | A Transformed Scientific Method[END_REF].

What are the main activities to address this Grand Challenge Problem?

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• Data ownership and access rights are challenging because the LMS and PLE systems are collecting educational data and the current assumption is that this data belongs to them. However, who exactly holds the ownership of the data created by the students and what can be done with it is still unresolved.

• Data policies (licences) that regulate how different users can use, share, and reference certain datasets. Until now there are very limited data policies available in educational institutes. It could be considered to apply the Creative Commons licensing rights as a standard way to grant permissions to datasets.

Common dataset formats like from the CEN PT Social data group (https://sites.google.com/site/camschema/home) and a standardised documentation of datasets so that others can make proper use of it.

• Methods to anonymise and pre-process data according to privacy and legal protection rights.

What is the timeframe for the Grand Challenge Problem?

Anything between 5 and to 8 years. For learning resources there are already standards like LOM and Dublin Core. For learning activities it's more complicated (apart from very generic formats such as XML -which does not guarantee that data can be reused).

What are measurable progress and success indicators?

• An increasing amount of publicly available datasets and research articles that are based on shared datasets

• The availability of data or privacy policies at educational providers

• More data-driven tools at educational providers

• A common dataset format
How can funding be attracted?

Funding can be attracted from governmental funding bodies like FP7, national funding, or funding by companies like Microsoft, Google, or IBM.

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Grand Challenge 4 / Topic 4:

Reduce delivery costs and create more effective learning environments by applying advanced information retrieval technologies on educational data sets.

Europe's education systems suffer from decreasing amount of teachers and the request to increase the amount of high-educated students in a short time period. As a consequence there is less time available for the individual support of students, thus the teaching quality decreases. On the other hand, the education systems are increasingly based on electronic systems like LMS and e-portfolios. With the increase in available educational data, the application of information retrieval technologies becomes valuable to create new services for the educational stakeholders (students, parents, teachers, and educational institutes). The combination of educational data and information retrieval techniques also known as Learning Analytics (LA) will become a powerful means in educational practice and student guidance [START_REF] Johnson | The 2011 Horizon Report[END_REF]. LA promises the educational field to reduce delivery costs, create more effective learning environments and experiences, accelerate competence development, increase collaboration between students and teachers.

But LA also have barriers and limitations among these are issues of privacy and data protection that need to be addressed by policy guidelines. Additional, challenges arise with respect to data surveillance 1 sorting, cumulative disadvantages) and its ethical implications.

What are the main activities to address this Grand Challenge Problem?

• In order to discuss and improve the above-mentioned situation a new vocabulary needs to be accomplished in order to discuss privacy, data protection and surveillance issues. For instance, what are better terms to express concepts like ownership and access control, when in digital systems replication and distribution is so easy that the concepts have no traction.

• Research is needed on how existing privacy and transparency solutions can be integrated in dataTEL practice. Further, research is desirable on how state of the art security solutions can be used to secure large educational datasets.

• There is a need for data awareness education for society. Such an educational program should not be limited to teaching individuals when to reveal or conceal their data, but

1 Data surveillance refers to the process which individualizes each member of the population (or a group), and permits the observation and recording of each individual's activities, then collates these individual observations across the population. From these conglomerated observations, statistical norms are produced relating to any of a multitude of characteristics. These norms are then applied back to the subjected individuals, who are categorized and perhaps acted upon, either with gratification or punishment, according to their relation to the produced norm. (Phillips, Privacy Policy and PETs, 2004) | Page 12 of 13 also to increase their awareness with respect to large datasets, surveillance practices, and related problems.

• User and stakeholder studies (case studies) are necessary to understand the complex requirements with respect to privacy, data protection, surveillance in dataTEL.

• The issues around privacy, data protection, and surveillance need to be addressed from the beginning of the research and not as an add-on. Methodologies and guidelines that support this vision need to be developed to privacy and ethical practices.

needs to be research on how to bridge between dataTEL researchers and ethical boards with respect to advances in technologies and research and the related privacy, data protection, and surveillance concerns that arise with them.

• have to be defined to avoid unethical data mining research.

What is the timeframe for the Grand Challenge Problem?

The first four activities can be addressed in a time frame from 2 to 3 years because they mainly require the application or translation of existing examples or solution from other domains to the educational field. The activities 5 to 7 will require a longer timeframe (3-5 years) as they can only be developed out of the experiences with the activities 1 to 4.

What are measurable progress and success indicators?

progress and success indicators are an increasing amount of ethical boards in LA units at educational organisations. The integration of privacy and data protection statements in research projects as well as between educational providers and the students. The integration of data and privacy competence in job profiles at the educational providers.

How can funding be attracted?

Funding for theses challenges could be attracted from EU FP7 projects and the Lifelong Learning Programmes (Erasmus, Leonardo or Comenius).

Researchers and Communities

In the last 3 to 6 years a couple of new research communities emerged around the dataTEL topics like Educational Data Mining, Recommender Systems in TEL, and Learning Analytics. These research communities are interdisciplinary and populated partly from Psychology Science, Educational Science, Computer Science, Data Science, Ethical science and Jurisprudence.

For the Grand Challenge 1 and 3 an interdisciplinary team should consists of computer scientists, educational experts and lawyers. Before any development can take place lawyers are needed to create suitable privacy and data sharing agreements that secure individual data on the one side and enable research to use the data on the other side. In a next step the educational experts can cooperate with the computer scientists to develop the systems required by the Grand Challenge 1 and 3.

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For Grand Challenge 2 an interdisciplinary team out of educational experts, policy makers and data scientists is needed to define the new competences required to understand the outcomes of dataTEL tools and create a training program for educational stakeholders teachers.

In order address Grand Challenge 4 it is essential to work with educational scientists, policy makers, lawyers and computer scientists together. Furthermore, an European association or a Special Interest Group is needed that moderates the public discussion on ethics and privacy in TEL and offers guidelines for the work and use of educational data and related data tools.

  What are the main activities to address this Grand Challenge Problem? • Customize existing recommendation algorithms for learning • Employ recommender systems in real-life scenarios • Develop suitable evaluation criteria for different kind of recommender systems What is the timeframe for the Grand Challenge Problem? | Page 8 of 13

•

  Integrate statistical, critical thinking, privacy awareness and ethical competences into the teacher education programsWhat is the timeframe for the Grand Challenge Problem?

Topic 3: Datasets from learning object repositories and web content

  On the second day John gave a keynote about: DataShop: An Educational Data Mining Platform for the Learning Science Community In his talk he discussed the vision of creating a true platform for conducting educational data mining research. The talk focused on DataShop, part of the Pittsburgh Science of Learning Center, which is an open data repository and set of associated visualization and analysis tools. DataShop has data from thousands of students deriving from interactions with on-line course materials and intelligent tutoring systems. The data is fine-grained, with student actions recorded roughly every 20 seconds, and it is longitudinal, spanning semester or yearlong courses. As of February 2011, over 245 datasets are stored including over 51 million student actions which equates to over 150,000 student hours of data. Most student actions are "coded" meaning they are not only graded as correct or incorrect, but are categorized in terms of the hypothesized competencies or knowledge components needed to perform that action. John focused his talk to workshop related key issues like the developing of an open data repository, security, privacy, and data diversity.Based on the 4 pressing topics and the two keynote speakers we created the following workshop programme.

			Maren Scheffel, Hendrik Drachsler,		Topic 2: Data
	17:00 17:30 13:30	Presenter 5 Coffee beak Organisers	(Fraunhofer Institute for Applied Information Technology, DE) (Open University of the Netherlands, NL) Peter Kraker, (KnowCenter, AT)	From Keyword Extraction To Key Action Extraction Wrap up round, What are the low-hanging fruits? supported learning Writing down the Grand challenges. Reflection round supported by Research2.0 tools from STELLAR.
	18:00 15:00	Presenter 6 End of the workshop Netherlands, NL) (Open University of the Rory Sie,	authorship Network to Foster Knowledgeable Peers in a Co-Recommendation of Why should I connect and to whom should I connect?
				Innovation
			Felix Mödritscher,	On reconstructing and
	18:30	Presenter 7	(Vienna University of Economics and	analyzing personal learning environments of scientific
			Business, AT)	artifacts
	19:00	Organisers	All	Discussion of topic 2
	19:45	End of day 1
	Programme and workshop participants Second workshop day
	ARV2011 ARV2011 8:30 8:30 8:50 8:45	30.03.2011 31.03.2011 Organisers Organisers Keynote Keynote	Presenter Presenter Hendrik Drachsler, Hendrik Drachsler, (Open University of the (Open University of the Netherlands, NL Netherlands, NL John Stamper, Shlomo Berkovsky, (Tasmanian ICT Centre, DataShop, USA) AU) (Pittsburgh Science of Learning Center	Title Title Introduction day 2 Welcome, Introduction Setting Up a Research Contest for TEL The PSLC DataShop: A Data Repository for the TEL community
	9:30 9:30	Presenter 1 Presenter 8	Katrien Verbert, (K.U.Leuven, BE) Joris Klerkx, (K.U.Leuven, BE)	Evaluating Collaborative Filtering Algorithms on TEL Data Sets Personalized Services Contextual Open Educational Resources for Future Recommender Scenarios
	10:00 10:00 10:30 10:30 11:00 10:45 11:30 11:15	Presenter 2 Presenter 9 Coffee break Coffee break Presenter 3 Presenter 10 Organisers Presenter 11	Peter Kraker, (KnowCenter, AT) Miguel-Angel Sicilia, (University of Alcalá, ES) Catherine Mulwa, Eelco Herder, L3S Hannover, DE (Trinity College Dublin, IE) All Seda Gurses, (K.U.Leuven, BE)	supporting Work-Integrated Learning: An Evaluation of applicable Recommendation Recommenders inside learning object repositories: requirements for meaningful datasets Mechanisms for open accessible Datasets A Recommender Framework Experiences in Building the Public Web History Repository for End User Experience In Adaptive Technology-Enhanced Learning Systems Discussion of topic 1 Privacy issues and data protection in Technology Enhanced Learning	Topic 1: Evaluation of TEL recommender systems Topic 4: Privacy and data protection for educational
	12:15	Post-its session			datasets
	12:30 11:45	Lunch Organisers	ARV2011 Activities All	Discussion of topic 3 and 4
	13:30 12:15	Snow activities Post-its session
	16:30 12:30	Presenter 4 Lunch	Raquel Crespo-García, (University of Madrid, ES) ARV 2011 activities	Peeking into the black box: visualizing student activities	learning Topic 2: Data supported
	| Page 5 of 13 | Page 6 of 13

All submitted papers have been peer-reviewed by two members of the program committee. Based | Page 4 of 13

MAVSEL project. The dataTEL Theme Team consists of Riina Vuorikari, project was represented by Miguel-Angel Sicilia. Shlomo Berkovsky is a Senior Research Scientist and Research Team Leader at the TLI project (CSIRO -Commonwealth Scientific and Industrial Research Organisation, Tasmanian ICT Centre).


Effie L-C Law

Felix Mödritscher

Martin Wolpers

Provocateur Denis Gillet

Sandy El Helou

Carlo Giovannella

Martin Memmel

Maryam Najafian-Razavi

Christopher Nehaniv

Christian Prause

Jose L Santos

Benham Taraghi

Fridolin Wild

st Workshop on Exploring the Fitness and Evolvability of Personal Learning Environments

Introduction 1.1 Motivation

In the recent decade a plethora of interactive software tools, be they open source or proprietary, have emerged and perished in the realm of technology-enhanced learning (TEL). Concomitantly, there have also been surge and demise of contents, social networks, and activities associated with the use of these TEL tools. It is intriguing to understand what factors contribute to their rises and falls, and how. While controversies on the viability of making an analogy between the evolution of natural and artificial objects prevail, it is deemed worthwhile to explore its potential for analysing the changes in TEL and charting the future.

In accordance with evolutionary theory, the fitness of an environment or tool can be defined with respect to its purpose and depends on the 'genes' from former generations. In context of TEL, these genes can be understood as features of existing tools and functionality being reused from software libraries or developed over multiple lifecycles thus leading to new generations of software artefacts. Personal learning environments (PLEs) aggregate these functionalities to enable learners to connect to peers and shared artefacts along their learning activities. Consequently, the success of a PLE can be measured by its uptake and usage within different communities of practice, its perceived effectiveness and efficiency in supporting the attainment of learning goals, its application beyond pre-defined purposes, its distribution and outreach beyond single communities, and its evolution to new PLE generations through active developers. Moreover, data mining of so-called variables of evolvability (e.g., perceived pragmatic/learning and hedonic/fun value) will enable the derivation of specific guidelines for designing and developing PLEs. Such empirically grounded guidelines, supplementary to those for generic IT applications, are currently lacking and much desired.

Overall, the main aim of the workshop is to explore the fitness and evolvability of PLEs in order to identify and understand characteristics and mechanisms for successfully evolving PLEs.

Related Work

In principle, for a software system to be sustainable, it needs to be able to adapt to the changing requirements [START_REF] Ciraci | Evolvability as a Quality Attribute of Software Architectures[END_REF] in terms of use contexts, user goals, organizational cultures and technological opportunities. Specifically, in the field of TEL, there has been a shift from the pioneer work on designing and implementing full-featured, organisation-driven learning management systems (LMSs) to the emerging trend of developing specialised tools, which then can be assembled by users to extend/create personal learning environments (PLEs, [START_REF] Attwell | Personal learning environments: The future of eLearning, eLearning Papers[END_REF] [START_REF] Attwell | Personal learning environments: The future of eLearning, eLearning Papers[END_REF]. Not least due to the Internet, users have access to a seemingly innumerable amount of content and software tools, which are useful and partially even necessary to achieve the learning goals driven by the demands of job tasks, higher, and further education, or even private activities.

In the context of PLEs, the selection of tools is at the discretion of individual users, their organisations and the communities of practice (CoP) where users engage in a variety of collaborative activities. It is observed that some software tools, after being used for a few typical tasks by a few people only, unexpectedly spread out within a CoP widely as well as wildly through good practice sharing, convincing peers of the benefits of these tools for particular lifelong learning activities. In a very short period of time such tools can become as must-have infrastructure for collaborative work (e.g. various Google services). These tools and the environments built on them are not only intensively used but are also modified and sustained by active developer communities. On the other hand, some tools are endangered to be rejected by end-users and to die out after a few successful cases of application, even though they have undergone several iterations of redesign. Apparently, these observations manifest the notions of descent with modification, heritable variation and selection, sensitivity to changing environmental or contextual requirements, and "control of and types of variability" (Nehaniv, 2003 [3]; Wernick et al. 2004 [4]) that characterize Darwinian evolution. In the context of PLEs, it is relevant to understand the processes leading to successful tool uses, create respective models and learn how to control respective processes to increase the efficiency and effectiveness of modern individual learning environments.

The assumption that changes in PLEs can be modelled by Darwinism underpins this proposed workshop, which aims to explore several pertinent issues: Nahaniv et al [START_REF] Nehaniv | What software evolution and biological evolution don't have in common[END_REF] (2006) define the notion of evolvability as "the capacity to vary robustly and adaptively over time or generations in digital and natural systems". This definition leads to a basic question: What is evolvable? Is it a matter of the complexity of a system that is quantifiable such as lines of codes, number of modules? Or is it more a matter of quality-in-use manifests in terms of user experience [START_REF] Law | Modeling of user experience: An agenda for research and practice[END_REF] (i.e. a non-functional requirement)? Another key question: Why does a system evolve? It can be instigated by changes in a system's environment, user requirements, usage, implementation methodologies and technologies. Answers to these what and why questions can shed some light onto the question How to effectively and reliably evolve a system [START_REF] Ciraci | Evolvability as a Quality Attribute of Software Architectures[END_REF]; footnote 3)? Addressing these questions in the context of PLEs will instigate stimulating discussions.

Fitness for survival is a widely known but poorly understood concept of Darwinian evolution. Paradoxically, the idea of heritable variation and selection is necessary but not sufficient to explain inherent phenotypic expression of fitness [START_REF] Nehaniv | What software evolution and biological evolution don't have in common[END_REF]; footnote 5). It hinges on the rigidity (or flexibility) of the genotype-phenotype mappings. The main difficulties lie in drawing analogies between biological concepts and artificial artifacts (e.g. What constitutes an "individual", a "species", or "interbreeding"). Insights can be gained from the notion of fit-forpurpose in the field of HCI (e.g. [START_REF] Wong | Fit for Purpose Evaluation: The case of a public information kiosk for the socially disadvantaged[END_REF] [START_REF] Wong | Fit for Purpose Evaluation: The case of a public information kiosk for the socially disadvantaged[END_REF] and the fitness model of nodes in the science of (social) networks [START_REF] Barabasi | The Linked: The new science of networks[END_REF] [START_REF] Barabasi | The Linked: The new science of networks[END_REF]. Nonetheless, it remains an open question on how to define and measure the fitness of PLE tools.

Workshop Description

There were 10 presentations, including a keynote speech. In addition, plenary discussions on specific topics were held. Section 2.1 reports the main ideas addressed by individual presentations. Section 2.2 highlights the ideas explored by the workshop participants.

Report on Presentations

Keynote speech by Prof. Chrystopher Nehaniv, University of Hertfordshire, UK

-Core concepts addressed: individual, reproduction, population, robustness, variability, phenotypic plasticity, autopoiesis, self-replication and repair, and evolvability -The notion 'replicating individual' is difficult to define in the realm of software evolution -Is it a behaviour, an artifact or software release? -Self-replication is a key notion in evolution (cf. computer viruses, cancer cells, selfreproducing automata); replicators entail external support; -Constraints of evolution: finite resources, heredity, variability, differing reproductive success, turn-over of generations; -Increasing complexity through successive inheritable mutation; a measure of complexity in biological sciences can be number of cell types and in software can be level of embeddedness, lines of code, number of loops, etc. -Adaptive changes in population over generations (genotype-phenotype map) -Artificial selection vs. natural selection; -Variability: neutral mutation (no harm, no benefit) is important: similar fitness in the same environment; mutation that is neutral in such an environment is beneficial as a resource; -Neutral mutation such as user interfaces -a variety of choice for selection; -Fitness landscape: inheritable fitness to flourish -Open-ended evolution is unbounded increase of complexity over time; -External fitness function imposed on agriculture (can we learn from this domain?); number of offspring and living long enough to reproduce (fitness measures); -Symbiogenesis: dynamic user-synthesis of PLE from components; combinations from the lower level units; -Evolvability for artefacts: capacity for producers to rise to adaptive variants for flexibly meeting changing requirements; lineage, different fitness between offspring and parents -Properties of evolvable systems: robustness to genetic variability, phenotypic robustness, redundancy, conservation of core mechanisms/features; robustness to environment change (resilience), self-monitoring, compartmentalization (modularity), symbiogenesis -Software evolution: re-use, modularity, information hiding, encapsulation, OO inheritance, coupling and cohesion; -PLE: system as fielded (instance: individual) -Persist over time, descent with modification -Lines of code, modules can be considered as genes (re-usable) -Variation: customization of generic software product via parameterization, copying and sharing -Iteratively adapted by users to context and changing requirements; -Immediate fitness is very different from capacity to support possible evolvability; -Variational capacity (vary/be varied robustly and adaptively) is crucial to evolvability

Discussion on the keynote speech

-Notion of energy/resources in the context of software; -Areas of tension:

o immediate fitness vs. variability o simplicity: usability vs. complexity o genotype (design: functionality) vs. phenotype (affordances: practices) -Complexity: base is interaction, energy comes from interaction, non predictable -Consciousness/Intentionality (or awareness): comes from interaction, collaboration -Is evolvability kind of higher level creativity -Success: performance improvement of learners; "form follows failures" -Complexity: maximise contact with environment subject to being able to understand and manipulate: complexity needs to be close to contact -Educational technology so far has failed: because there are no solutions of scale (past: LMS have been successful, but not 'real' learning support tools) -Capacity for variability: Learning is development of potential for action: competence, but we can only assess performance -Capacity relates to complexity through adaptation through exchange of modules and over time! -Freedom of adaptation vs. ethical concerns experimenting with bad combinations of software -Sharing of successful practices/arrangements/etc. is hereditary replicability -Problem: It's not the PLEs surviving and being fit, it's the widgets -Problem: PLE: Livespan of generations is not controlled -But: Behaviour vs. artefacts: patterns of practices vs. widgets -Behaviour: duplication and divergence; behaviour patterns can be very far away from genetics; active copying vs. environment driven auto discovery -Controlling of behaviour: we can (to a part) control the environment to recreate 'situations' -Translation of behaviour (phenotype) into genotype? No convergence in other areas.

-Would be helpful to very clearly define concepts such as genotype, phenotype in the PLE context -Groundbreaking works in e.g. evolutionary algorithms: e.g. von Neumann: theory about live; e.g. evolutionary algos: were designed as optimisation techniques (example: designing nozzles, aircraft wings) -Selection strategies: r-strategy (short livespan, unknown environments) vs. K-strategy (long livespan, known environments) -Mutation: slight variation of existing functionality or UI -Recombination: combining code of different widgets to build new ones: code sex -Tracking of use: frequency of activated widgets, frequency of interactions with widgets that can be tracked in the system -TUG system: 1000 users, 30% active users -Competition not between widgets, but between PLE system and competing websites -Code complexity of the PLEs: PLE as a whole (of one user) or widgets? How did it change over time? Lines of code? Level of embeddedness? Modularisation? Interwidget communcation? Service orientation? -Affordances (= in a certain cultural context)? -Other factors (besides fitness): usability, usefulness (e.g. indirect via level of the learners)? -Need to look at overall PLE system, not only at single widget; still: number of contexts, number of functions, number of other widgets it has been used with (degree centrality, betweenness, prestige): indicator of complexity -Symbiotic relations: themingWidget: cannot exist on its own -Coevolution of development and users

Presentation by Benham Taraghi

Presentation by Carlo Giovanella

-Evolution: strong focus on learning analytics: e.g. activity graphs, emotions, social networks, emotion in social networks -Use traces of user activity to observe evolution -Arrival of facebook changed the use of the system -New journal: Interaction Design & Architecture

Presentation by Felix Moedritscher

-Environment: socio-technical system: activities, purposes, patterns, interaction, features, functionality, implementation -Evolvability: versioning, copying/reusing, interoperability -Fitness: usefulness & usability, user feedback, technological compliance -Distribution approximation -Fitness depends on the usage context (e.g. publication impact) -Impact of papers very strongly relates on experience of the researcher (years of experience in a field). What about production of widgets? Are widgets produced by more experienced users more successful?

Presentation by Martin Memmel

Presentation by Jose L. Santos

-CAM dashboard -Activity -actions executed in widgets -Capturing communcation data from interwidget communication -Specialisation to styles? -Active use of the dashboard to change behaviour? -Evolution: Awareness > Social Behaviour > … -How to support awareness between developer and user? -Representation of context to make use of the activity monitoring -Fitness: take care of environment -Visual quality -Trust relationship between developers and user

Presentation by Fridolin Wild

-Acceptance: expectancies, social influence, facilitating conditions etc.

-Longer term

Presentation by Christian Prause

-"Walking on water and developing software from a specification are easy if both are frozen." (Edward V. Berard) -high costs of change lead to extinction evolvablity: internal quality software quality: ISO 9126: functionality, reliability, usability, efficiency, maintainability, portability developers learn software: documentation! Code! -Fitness = external quality + quality in use = Tool in environment in its context -Case-based tools

Presentation by Maryam Najafian-Razavi

Report on Plenary Discussions

Contextual Issues

-Flexibilisation of technology support for any kind of educational process -Culture of certification: assessment and accreditation; -Fitness: Integration of environments: mobile, web, all -Fitness of users: critical design skills, measure experience / styles -Context: capture context of learners holistically, make this context description available to sound applications; -Plasticity: Support change in pedagogical approaches

Teachers as Target Groups

-Find a way to prove to the teacher that relying on a specific technology will help them be more effective -Tackle danger for teachers: environments disappear: but environments change with their needs -How to sell technology to the teachers? -Show that with the help of any technology, the learners in the classroom/course became 10% better: works only with criterion-referenced testing (no norm referenced testing): skills assessment: increase by 10% -Emergence of new widgets coming from the teacher and learner community -Living community: Increased sharing of best practices: 1 million teachers / million learner using a PLE; There are enough teachers in Europe -Digital literacy of teachers is a problem -Technology is seen as an amplifier -Combine agents and human tutors to provide high quality tutoring to every child

Invisible PLE

very low entry barrier -Sharing a curriculum in 15 minutes -No good idea: it is rather about reconfiguration, not sharing: more about the adoption than that it is fast -Extremely complex issue -Widgets: 1000 widgets: which one is better and how do we measure that? Through the community -Testing: could include teacher has to be able to re-use a PLE in 15 minutes; but: it's not about time, it's about the return on investment -Identifying the scores that someone gets based on the traces that someone leaves in the system -Pedagogically sound user interfaces

Predictive Modelling

-Predictive models: Predicting performance based on traces -Testing of predictive models in competitions: accuracy vs. satisfaction -Learning analytics: graphical user interfaces that foster quick understanding of performance and aesthetic display, streaming feedback -Learning analytics, traces, context capturing; Privacy-ensured, anonymised; Streaming analysis -Open requirements elicitation: Implicit requirement modelling, helpdesk monitoring, Implementation competitions in the bartering platforms for software development

Emerging Research Questions

Find a way to prove to the teacher that relying on a specific technology will help them be more By understanding a learner as an actor in a learning ecology, TEL research has started to capture and anaylse the interactions of a learner with her environments which can be characterised as (ad-hoc) networks of actors, artefacts, tools, activities, and communities. In the beginning strongly motivated by being a counterpart to managed learning technology, streams like personal learning environments (PLEs) have emerged over the last few years with the aim to empower learners to design their own environments and to connect to learner networks to collaborate on shared artefacts and goal achievements (Wild et al., 2008;Van Harmelen, 2008). EU projects like ROLE, ITEC, or LTfLL have been investigating into PLE technologies and approaches.

From a more technical perspective, app and widget technology is being developed and applied for learning in many different settings and for different purposes, as reported by projects like ROLE or ITEC. The overall goal here is about reaching a certain level of variability in using technology for learning. On the other hand, interopability has being investigated on different levels and in various EU projects, such as ICOPER or ROLE, as well as in various R&D communities, like CSCW, SCORM, IMS etc. Basically these standardisation movements aim at making learning objects, learning designs, or educational scripts accessible for others in order to foster sharing and reusability of educational resources.

In fact, one grand challenge in the European Educational Area deals with reaching a certain level of scale in variability, e.g. flexibility of learning technologies, and building capacity for sharing and consuming practices of technology use in learning and teaching. Variability and the capacity for variability is a precondition for a flexibly changing learning environments. Therefore and to assist evolution of teaching with technology, the grand challenge aims at providing facilities so that a million different (individual) practices describing differing technology arrangements (e.g. a widget space in a PLE) are shared. It is not an explicit goal to show that a certain practice is used by a million people but that a million people have differing, individual needs and practices in technology support.

Grand challenge 1 includes several necessary achievements:

-Practice capturing and sharing formalisms such as scripts, learning designs, or activity streams -Facilities to capture context information such as attention meta-data -Means for interoperability -Understanding, building, and sustaining networks of teachers, including ad-hoc formation and dissolution of such cliques -Large tool repositories such as widget-and app-stores Building capacity for variability includes:

-Supporting change in pedagogical approaches, shifting the focus from instructional theories and course-based teaching to environment design capabilities and outcome-oriented learning -Finding ways to evidence to the teacher that relying on a specific technology will help them be more effective -Since a constantly changing tools portfolio brings along the danger of environments disappearing, teachers have to be supported in meeting this with resilience. This may include inexpensive benchmarking methods for assessing effectiveness or efficiency gains that can be achieved with certain practices of technology use.

The degree of variability can be studied and measured. There has to be a significantly large number of differences, which can be assured by investigating provenance (contributions from all EU countries), position in lifelong learning (school, university, continuing professional development or work place learning), stakeholders (teachers, learners, providers, institutions, policy-makers), heterogeneity in practice (activities supported, different flows for the same aim) and tools (arrangements of widgets, combinations of web apps). The 'million people, million practices' benchmark could be seen as a 'hard' target, whereas evidencing a sufficent level of variability could be let up to the applicants in the challenge.

Grand Challenge 2 (GC2): Fitness of learning environments is plasticity with respect to user requirements

Plasticity of learning environments is their ability to flexibly adapt (or be adapted) to changing requirements. Learning environments are complex ecosystems. Creating plasticity refers to increased mass individualisation of practices and accompaning technology support.

This involves:

-Supporting digital literacy of the teachers.

-Technolology as an amplifier.

-Increasing the speed of change.

-Identifying means to evidence that a trajectory is followed that a system has been adapted: providing means to evidence pasticity. -Knowledge management for teachers -Understanding and facilitating the ad hoc formation and dissolution of communities of practices: problem solved, community dissolved. -Environments of scale.

-Investigating mutation, variability, and fitness of learning environments.

-Building developer -user communities (coding on demand, mash-ups and end-user development, improved human-computer interaction). -Providing means for quick.

-Flexibility with respect to pedagogical and andragogical approaches -Low entry barriers. This could be measured via an open market: when e.g. an open market for widgets and educational practices exists (widgets that are embedded in individual practices that are shared), the market decides which ones are best.

Grand Challenge 3 (GC3): One tutor per child

Human tutors can be assisted by technology to help learners become more competent and meet the demands of our knowledge-driven society. An individual tutor for every child in Europe (and beyond) is a desideratum that so far cannot be reached. With the help of predictive models and learning analytics, this area could be significantly strengthened. Combining agents and human tutors to provide high quality tutoring to every child can be achieved.

This involves the research of:

-Predictive models: Predicting performance based on traces -Testing of predictive models in competitions / evaluation forums. Such evaluation competitions are available in other fields: e.g. search engines are evaluated in yearly cycles (with varying focus points) in TREC and CLEF. Evaluations can be done along two lines: accuracy vs. satisfaction. -Learning analytics: graphical user interfaces that foster quick understanding of performance supported by aesthetic displays; streaming feedback provides real-time support in analysis -Open requirements elicitation: Implicit requirement modelling, helpdesk monitoring, Implementation competitions in the bartering platforms for software development -Developing shared methodologies for evaluating effectiveness gains of teachers and learners -Pedagogically sound user interfaces -Research of digital identity, privacy, and trust A set of traces (with objective, human assessed performance scores) can be provided to evaluate the predictive models / learning analytics against. A test set (with non-disclosed human performance scores) can be retained to be used in the a competition.

5.

Researchers and Communities

To tackle the three aforementioned Grand Challenges (GC), a broad spectrum of expertise is required.

It is crucial to involve researchers from a variety of fields, including biology, mathematics, statistics, computer science, engineering, education, sociology, marketing and management, psychology, and anthropology. The orchestration of contributions from such an interdisciplinary and multidisciplinary team is a mega-challenge per se. Boundary objects [START_REF] Star | Institutional Ecology, 'Translations' and Boundary Objects: Amateurs and Professionals in Berkeley's Museum of Vertebrate Zoology, 1907-39[END_REF] such as design artefacts need to be established to facilitate scientific discourses among them. It is deemed indispensable given that individual fields adopt different values, assumptions, and metaphors.

A team with such diverse expertise is typical of the field human-computer interaction (HCI). Members of the HCI community can roughly be classified as academics and practitioners. Another dichotomous categorisation is researchers and designers, although there are numerous sub-categories under these broad terms. Specifically, in addressing the GC1 above, communities of teachers are a must. Concomitantly, learners, policy makers and administrators with which teachers frequently interact will definitely be involved. For GC2, software engineers, interaction designers, and evolutionists/biologists are key players. For GC3, specialists in education technology, statistical modelling, and evaluation methodologies are essential.

Furthermore, it is necessary to construct as well as validate theoretical frameworks with representative target groups, be they teachers, learners, developers, designers and researchers. Mixed method approaches involving different stakeholders, techniques, tools and resources are required to triangulate complex findings. Besides, longer-term studies capturing required data over time are recommended. With the use of Web2.0 applications, multi-source data can be gathered, analysed and reviewed collaboratively to generate richer insights into the issues of interest.

Concluding Remarks

Evolutionary or Darwinist theories are inherently controversial; applying them to explain and predict the trajectory of the development of Personal Learning Environments (PLE) is particularly challenging. PLE is still at its infancy stage, and a consensual definition is still lacking. Amongst others, the task of defining fitness models for predicting the rise and demise of specific widgets (which are commonly seen as the building blocks of PLE) and a specific configuration of PLE per se is daunting. The workshop is seen as the first step moving in the direction, though there are still many steps to be taken to achieve this seemingly insurmountable task. The initial step is seen as successful with intriguing ideas being conceived. Future work includes organizing a series of related workshops/seminars that involve participants with diverse backgrounds. Project proposals addressing the emergent topics are seen as a promising way to explore them in depth over a relatively long period of time. In the meantime several meetings amongst the workshop participants have been held to explore these possibilities.

7 Presentation by Sandy El Helou

  Use of Graaasp -Flexible representation: not necessarily dependant on number of users

-Sustainability -Interoperability: using and offering APIs, following standards -Number of application scenarios: very many application scenarios for PLEs -Low technical and low conceptual barriers to system use -Resources are finite: people, time, infrastructure, money -Repurposing and re-theming/branding of systems -Solve a specific problem, but do it in a generic way -Support tools for setup and deployment -Refactor -Fitness is plasticity with respect to user requirements 2.1.-Viability: o flexible representation of interaction and contents o adopt social media paradigms (encouraging participation) o elastic community and CMS services o automate/openness: recommender systems: open corpus environments -

Grand Challenge 1 (GC1): The million practices & million teachers challenge: ad hoc formation of large scale learning networks

  effective o The million practices & million teacher challenge: ad hoc formation of large scale learning networks: Reach a certain level of scale in variability and build capacity for variablity of practices of technology use in learning and teaching. o This includes: sharing of context information such as attention meta data, interoperability, practice capturing and sharing facilities such as scripts or learning designs or activity streams o This is not about showing that a certain template is used by a million people, but that 1 million people have differing, adapted to their needs practices in technology support o Ad hoc formation of large scale learning networks Fitness of learning environments is plasticity with respect to user requirements:In the educational area, technology is considered an enabler for successful and collaborative activities supporting learners with the increasing complexity and dynamics observable in all knowledge domains and leading to meaningful outcomes. Although being fostered by various European research programmes, a shift in funding strategies can be identified. Instead of pushing technologies for organizational driven teaching and learning more attention is paid to learner-centric approaches fostering competences beyond professional ones, i.e. digital literacy and social competencies, in order to prepare and strengthen individuals for acting in digital ecosystems. Amongst others, EU projects like ARISTOTELE, GRAPPLE, IMREAL, MATURE, METAFORA, ROLE, and TERENCE indicate the importance on transcompetences, personalized learning experiences, collaboration and reflection. Other research, for instance the projects 80DAYS, ALICE, COSPACIAL, GALA, ITEC, or V-CITY, focus on interactivity of content, educational games, or learner characteristics beyond domain-specific knowledge.

	differing learning contexts (school, university, lll) and stakeholders (providers,
	learners, teachers, educational institutions)
	4. Grand Challenges
	4.1
	o Variation: Adaptation or mutation: construction set widget-based PLE, coding
	according to changing user requirements, mash-ups
	o Speed of change:
	 Evidence that a trajectory is followed that a system has been adapted: evidence
	of plasticity
	 Knowledge management for teachers
	 Dissolving of communities of practices: problem solved, community dissolved
	Invisible PLE
	o Low entry barriers

o Flexibility with respect to pedagogical and andragogical approaches o fitness of widgets: create an open market for widgets; then we can use the market mechanisms; show that there are widgets from each of the European countries;


Anne-Sophie Messiaen

Thomas Verbrugghen

Charlotte Declerck

Regina Ortmann

Martin Schlitzer

Hans Nelis

Serge Van Calenbergh

Tom Coenye
email: tom.coenye@ugent.be

Resistance of the Burkholderia cepacia complex to fosmidomycin and fosmidomycin derivatives

Keywords:

The Burkholderia cepacia complex (BCC) is a group of 17 closely related opportunistic pathogens that are able to infect the respiratory tract of cystic fibrosis patients. BCC bacteria are intrinsically resistant to many antibiotics and are therefore difficult to eradicate. Fosmidomycin could be a new therapeutic agent to treat BCC infections as it inhibits 1-deoxy-D-xylulose-5-phosphate reductoisomerase (Dxr), a key enzyme in the non-mevalonate pathway essential in BCC bacteria for isoprenoid synthesis. In this study, the antimicrobial activity of fosmidomycin and eight fosmidomycin derivatives towards 40 BCC strains was investigated. All BCC strains were resistant to fosmidomycin, although addition of glucose-6-phosphate reduced the minimum inhibitory concentrations values of FR900098, the fosmidomycin acetyl derivative, from 512 mg/L to 64 mg/L for Burkholderia multivorans and B. cepacia.

This enhanced activity was linked to increased expression of the genes involved in glycerol-3-phosphate transport, which appears to be the only route for fosmidomycin import in BCC bacteria. Furthermore, upregulation of a fosmidomycin resistance gene (fsr) encoding an efflux pump was observed during fosmidomycin and FR900098 treatment. These results strongly suggest that the observed resistance in BCC bacteria is due to insufficient uptake accompanied by fosmidomycin and FR900098 efflux.

A c c e p t e d M a n u s c r i p t

Introduction

The Burkholderia cepacia complex (BCC) is a group of 17 closely related species. In the early 1980s, BCC bacteria emerged as problematic pathogens in cystic fibrosis (CF) patients, with Burkholderia cenocepacia and Burkholderia multivorans being the most frequently isolated species. Compared with Pseudomonas aeruginosa, BCC infections account for only a small percentage of respiratory infections within the CF population, however they are often associated with rapid deterioration of lung function and increased mortality [START_REF] Mahenthiralingam | The multifarious, multireplicon Burkholderia cepacia complex[END_REF]. Once infection with BCC bacteria is established, therapeutic options are limited because of the innate resistance of these bacteria to many antibiotics. Different resistance mechanisms have already been described, including increased efflux and decreased permeability of the outer membrane [START_REF] Avgeri | Therapeutic options for Burkholderia cepacia infections beyond co-trimoxazole: a systematic review of the clinical evidence[END_REF]. Despite these high levels of resistance, there has been little progress in developing new therapeutic strategies to fight these infections.

The non-mevalonate pathway for isoprenoid synthesis was discovered in the early 1990s as an alternative to the classical mevalonate pathway. Whilst most bacteria exclusively synthesise isoprenoids, such as coenzyme Q, through this pathway, mammalian cells do not possess it [START_REF] Eisenreich | Biosynthesis of isoprenoids via the non-mevalonate pathway[END_REF]. Fosmidomycin, a natural phosphonic acid antibiotic, is a specific inhibitor of a key enzyme of the non-mevalonate pathway, i.e.

1-deoxy-D-xylulose-5-phosphate reductoisomerase (Dxr). Although it has shown good safety and efficacy, e.g. for the treatment of uncomplicated Plasmodium falciparum malaria [START_REF] Ruengweerayut | Pharmacokinetics and pharmacodynamics of fosmidomycin monotherapy and combination therapy with clindamycin in the treatment of multidrug resistant falciparum malaria[END_REF], rapid development of resistance during therapy is of great concern. White et al. [START_REF] White | Targeting metalloenzymes: a strategy that works[END_REF] suggest that this resistance is due to decreased uptake of the compound. Uptake of fosmidomycin is hypothesised to follow the same route as its structural analogue fosfomycin, either through hexose phosphate or through A c c e p t e d M a n u s c r i p t 5 glycerol-3-phosphate transporters, as mutations in genes encoding these transport systems confer resistance to both fosmidomycin and fosfomycin [START_REF] Castañeda-García | The glycerol-3phosphate permease GlpT is the only fosfomycin transporter in Pseudomonas aeruginosa[END_REF].

Furthermore, the B. cenocepacia genome contains a fosmidomycin resistance gene (fsr) encoding an efflux pump that transports fosmidomycin out of the cell [START_REF] Fujisaki | Cloning of a gene from Escherichia coli that confers resistance to fosmidomycin as a consequence of amplification[END_REF].

The aim of the present study was to evaluate the in vitro activity of fosmidomycin and eight derivatives against BCC and to elucidate the mechanisms of resistance in this group of organisms.

Materials and methods

Strains

The were obtained from the BCCM/LMG Bacteria Collection (Ghent, Belgium) or were kindly provided by Dr P. Vandamme (Ghent University, Belgium). Two Escherichia coli K-12 strains were used as positive controls as it has previously been shown that fosmidomycin is active against these isolates [START_REF] Neu | In vitro and in vivo antibacterial activity of FR-31564, a phosphonic acid antimicrobial agent[END_REF].

Antimicrobial agents

Fosmidomycin was obtained from Invitrogen (Eugene, OR). All fosmidomycin derivatives were synthesised as previously described [9-13] (see Supplementary Fig. 1 for an overview of the structures).

Determination of minimum inhibitory concentrations (MICs)

MICs were determined according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) standard broth microdilution protocol in Mueller-Hinton broth (MHB) [START_REF] Hasselmann | Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by broth dilution[END_REF]. A two-fold antibiotic dilution series ranging from 1 mg/L to 512 mg/L was tested. MICs were also determined in the presence of glucose-6phosphate and glucose-1-phosphate as the sole carbon sources. To this end, a chemically defined minimal salt medium supplemented with 50 M FeCl 3 .6H 2 O (designated as CDM) [START_REF] Peeters | Resistance of planktonic and biofilm-grown Burkholderia cepacia complex isolates to the transition metal gallium[END_REF] and equimolar concentrations of glucose-6-phosphate and glucose-1-phosphate instead of glucose (designated as CDM-G) was used.

RNA extraction and cDNA synthesis

Cells were cultured in either CDM or CDM-G medium. Antibiotic concentrations corresponding to the previously determined MIC values were used during treatment.

If MIC values were >512 mg/L, cultures were grown in medium containing 512 mg/L A c c e p t e d M a n u s c r i p t 7 of the antimicrobial agent. After 24 h of incubation with constant agitation, cells were collected and RNA was extracted using a RiboPure TM Bacteria Kit (Ambion, Austin, TX) according to the manufacturer's instructions, except for the DNase treatment that was prolonged to 1 h. After RNA extraction, RNA yields were assessed using an RNA Quant-iT TM Kit (Invitrogen). Then, 15 L RNA samples were used to synthesise cDNA using an iScript TM cDNA Synthesis Kit (Bio-Rad, Hercules, CA).

Quantitative real-time polymerase chain reaction (PCR)

Expression of seven genes (including two reference genes) was investigated by means of quantitative real-time PCR (qPCR). Primers were developed using the website of the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov) and their specificity was checked using BLAST (see Supplementary Table 1 for primer sequences). All qPCR experiments were performed on a Bio-Rad CFX96 Real-Time System C1000 Thermal Cycler using iQ TM SYBR ® Green Supermix Kit (Bio-Rad) according to the manufacturer's instructions.

To activate the polymerase included in the iQ SYBR Green Supermix Kit, the reaction mixture was heated at 95 C for 3 min, followed by 40 amplification cycles consisting of 15 s at 95 C and 60 s at 60 C. A melting curve analysis was included at the end of each run. Real-time PCR data were normalised to the geometric mean of the expression of two reference genes (BCAL0289 and BCAL1861), which were stably expressed in all conditions investigated in the present study (data not shown).

A c c e p t e d M a n u s c r i p t 8

Results

Susceptibility testing in Mueller-Hinton broth

For all the BCC strains tested, MICs of fosmidomycin were >512 mg/L, whereas those for the two E. coli K-12 strains serving as positive controls ranged from 0.5 mg/L to 2 mg/L, in agreement with previously reported data [START_REF] Neu | In vitro and in vivo antibacterial activity of FR-31564, a phosphonic acid antimicrobial agent[END_REF]. The seven other derivatives were tested against three BCC type strains, B. cepacia LMG 1222 T , B. multivorans LMG 13010 T and B. cenocepacia LMG 16656 T , but growth inhibitory concentrations of these derivatives were never <512 mg/L (Table 1).

A c c e p t e d

M a n u s c r i p t 10

Effect of glucose-6-phosphate on expression of genes involved in glycerol-3phosphate transport during FR900098 treatment

Expression of three genes involved in glycerol-3-phosphate transport in B. cenocepacia LMG 16656 T (BCAL0282, BCAL0284 and BCAL0285) during treatment with 64 mg/L FR900098 was investigated. Expression levels for the three genes tested were significantly higher (P < 0.05) in the presence of glucose-6-phosphate (CDM-G) compared with controls (CDM) (Fig. 2a). Expression of BCAL0283, which encodes a putative ABC transporter permease involved in glycerol-3-phosphate transport, was too low to allow accurate quantification (data not shown).

Expression of BCAL2085 (dxr) and BCAL1451 (fsr)

Expression levels of dxr and fsr in the presence of fosmidomycin or FR900098 (64 mg/L) in CDM-G medium relative to untreated controls are shown in Fig. 2b. The fsr gene encoding an efflux pump was significantly upregulated during treatment compared with untreated controls (P < 0.05), whilst the gene encoding the target enzyme (Dxr) was stably expressed in both conditions.

Discussion

BCC bacteria are known for their resistance against a wide range of antimicrobial agents. The aim of the present study was to evaluate the potential of fosmidomycin for the treatment of BCC infections.

Despite the presence of dxr (BCAL2085) in the B. cenocepacia J2315 genome, a clear resistance to fosmidomycin and its derivatives was observed for all BCC strains A c c e p t e d M a n u s c r i p t 11 tested. This indicates that the presence of the non-mevalonate pathway, and more specifically Dxr, does not guarantee susceptibility towards inhibitors of this pathway and that resistance may be due to altered transport. The glycerol-3-phosphate transporter (GlpT) is reportedly the main importer of fosmidomycin in E. coli [START_REF] Castañeda-García | The glycerol-3phosphate permease GlpT is the only fosfomycin transporter in Pseudomonas aeruginosa[END_REF]. No glpT homologues were found in the B. cenocepacia J2315 genome, nevertheless an alternative system involved in glycerol-3-phosphate import is present. This transporter belongs to the ABC transporter superfamily and consists of a periplasmic substrate binding protein [ugpB (BCAL0282)], two permease proteins [ugpA (BCAL0283) and ugpE (BCAL0284)] and an ATP-binding protein [ugpC (BCAL0285)]. Because of the structural similarity of fosmidomycin to glycerol-3phosphate, we suspect that fosmidomycin can enter B. cenocepacia cells through this alternative glycerol-3-phosphate transport system as well.

The activity of both fosmidomycin and FR900098 was enhanced in the presence of glucose-6-phosphate when used either as a sole carbon source or in combination with glucose-1-phosphate, suggesting the presence of an inducible sugar phosphate transport system through which fosmidomycin can be imported into BCC bacteria.

The hexose phosphate transporter (UhpT) is a main transport system for fosfomycin, a structural analogue of fosmidomycin, in E. coli cells [START_REF] Castañeda-García | The glycerol-3phosphate permease GlpT is the only fosfomycin transporter in Pseudomonas aeruginosa[END_REF][START_REF] Takahata | Molecular mechanisms of fosfomycin resistance in clinical isolates of Escherichia coli[END_REF]. Based on the genes involved in the hexose phosphate transport system in E. coli, we searched for their homologues in the genome of B. cenocepacia J2315 using BLAST. However, we did not identify uhp homologues, suggesting that B. cenocepacia lacks a specific sugar phosphate transport system. This indicates that fosmidomycin can only be imported into B. cenocepacia cells through its glycerol-3-phosphate transport system. Therefore, the effect of glucose-6phosphate on the expression of ugpB, ugpC and ugpE was investigated. A significant upregulation of these genes in CDM-G medium compared with CDM medium was observed. As the only difference between these media is the carbon source, the upregulation must be attributed to the presence of glucose-6-phosphate or glucose-1-phosphate in CDM-G medium. As previous observations indicated that glucose-1phosphate had no influence on MIC values, the upregulation is likely caused by glucose-6-phosphate.

A gene conferring resistance to fosmidomycin (fsr) was shown to be involved in fosmidomycin efflux in E. coli [START_REF] Fujisaki | Cloning of a gene from Escherichia coli that confers resistance to fosmidomycin as a consequence of amplification[END_REF]. As an fsr homologue is present in the genome of B. cenocepacia J2315 and is significantly upregulated in treated samples, this could in part explain the observed resistance in BCC bacteria.

In conclusion, these results suggest that the only transport system for fosmidomycin in BCC bacteria is a glycerol-3-phosphate transport system, which is induced in the presence of glucose-6-phosphate. Consequently, uptake of fosmidomycin can be increased by addition of glucose-6-phosphate. However, a fosmidomycin efflux pump is capable of compensating for this increased fosmidomycin uptake, providing resistance in BCC bacteria to fosmidomycin and FR900098.

  following 40 BCC strains were used: B. cepacia LMG 18821 and LMG 1222 T ; B. multivorans LMG 18822, LMG 18825, LMG 13010 T and LMG 17588; B. cenocepacia LMG 16656 T , LMG 18828, LMG 18829, LMG 18830, LMG 6986 and K56-2; Burkholderia stabilis LMG 14294 T and LMG 14086; Burkholderia vietnamiensis LMG 18835 and LMG 10929 T ; Burkholderia dolosa LMG 18943 T and LMG 18941; Burkholderia ambifaria LMG 19182 T and LMG 19467; Burkholderia anthina LMG 20980 T and LMG 20983; Burkholderia pyrrocinia LMG 14191 T and LMG 21824; Burkholderia ubonensis LMG 20358 T and LMG 24263; Burkholderia latens R-11768 and LMG 24264; Burkholderia diffusa LMG 24065 T and LMG 24266; Burkholderia arboris LMG 24066 T and R-132; Burkholderia seminalis LMG 24067 T and LMG 24272; Burkholderia metallica LMG 24068 T and R-2712; Burkholderia lata LMG 6992 and R-9940; and Burkholderia contaminans LMG 16227 and R-12710. All strains A c c e p t e d M a n u s c r i p t 6

  The acetyl derivative FR900098 was capable of inhibiting the growth of 18 of the 40 strains tested at concentrations ≤512 mg/L. Burkholderia multivorans LMG 18822 and LMG 18825, B. cenocepacia LMG 18830 and LMG 6986, B. stabilis LMG 14294 T , B. vietnamiensis LMG 18835 and LMG 10929 T , B. dolosa LMG 18941, B. pyrrocinia LMG 21824, B. ubonensis LMG 20358 T and LMG 24263, B. seminalis LMG 24067 T and B. metallica LMG 24068 T and R-2712 were inhibited at a concentration of 512 mg/L. Further, B. cepacia LMG 1222 T , B. multivorans LMG 13010 T and B. lata LMG 6992 were inhibited at a concentration of 256 mg/L and B. anthina LMG 20983 was inhibited at a concentration of 128 mg/L.

16 Fig. 1 .Fig. 2 .

 1612 Fig. 1. Activity of fosmidomycin in the presence of glucose-6-phosphate as the sole carbon source. ---, activity in CDM medium; --, activity in CDM medium with glucose-6-phosphate replacing glucose; , Burkholderia multivorans LMG 13010 T ; ▲, Burkholderia cepacia LMG 1222 T .

Funding

This research was supported by Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO) and Special Research Fund (BOF) (Ghent University, Ghent, Belgium). TV is

A c c e p t e d M a n u s c r i p t 9

Susceptibility testing in the presence of glucose-6-phosphate

First, the activity of fosmidomycin in the presence of glucose-6-phosphate was investigated by adding 25 mg/L glucose-6-phosphate to MHB. However, MICs remained >512 mg/L for all 40 strains tested (data not shown).

To investigate the activity of fosmidomycin in the presence of glucose-6-phosphate as the sole carbon source, CDM medium supplemented with Fe 3+ [START_REF] Peeters | Resistance of planktonic and biofilm-grown Burkholderia cepacia complex isolates to the transition metal gallium[END_REF] (designated CDM) and CDM with glucose-6-phosphate replacing glucose (designated as CDM + G6P) was used. Although it should be noted that reduced growth in these media precluded the determination of the exact MIC value, an increased sensitivity of B. multivorans LMG 13010 T and B. cepacia LMG 1222 T to fosmidomycin in CDM + G6P compared with CDM was observed (Fig. 1).

Because of the higher activity of FR900098, the effect of glucose-6-phosphate was

Competing interests

None declared.

Ethical approval

Not required.