LinkedUp Veni Competition: Linked and Open Data for Education
The LinkedUp Project invites everyone, from researchers and students, to developers and businesses, to join the ‘Veni’ competition. This is the first of three consecutive competitions, and we’re calling for you to submit a prototype or demo that uses linked and/or open data for educational purposes.
The total prize fund for ‘Veni’ is 5.000 EUR. However, the attractive prizes are only one reason to participate in the competition. It is also a great opportunity to work with the large, documented repository of linked datasets that the LinkedUp team is putting together. Participants will also be able to showcase their ideas and solutions to a wide
community of researchers and practitioners: All accepted demos and prototypes will be presented at OKCon (http://okcon.org/) in Geneva, on 17 September 2013.
To help participants, the LinkedUp team is providing dedicated support, how-to’s, examples and code recipes, and has a designated developer blog: http://data.linkededucation.org/linkedup/devtalk/
We have also curated a number of high profile use cases from organisations such as the Commonwealth of Learning and Elsevier to serve as inspiration.
Get started with the LinkedUp Dataset Catalogue now: http://data.linkededucation.org/linkedup/catalog/
What are you waiting for? Join the Challenge today!
- Find out about the LinkedUp Challenge: http://linkedup-challenge.org
- Subscribe to our mailinglist: https://www.l3s.de/mailman/listinfo/linkedup-public
- Follow @linkedupproject: http://twitter.com/linkedupproject
- Contact us directly for questions or support requests: http://linkedup-project.eu/contact/
- Get more information about the LinkedUp Project: http://linkedup-project.eu/
Evaluation of Linked Data Tools for Learning Analytics
Tutorial given at LAK13 conference, Leuven, April, 9th, 2013.
The presentation is informed by WP2 of the LinkedUp-project.eu that develops an Evaluation Framework for Open Web Data (Linked Data) Applications for Education purposes.
Drachsler, H., D’Aquin, M., Dietze, S., Herder, E., (2013). Evaluation of Linked Data Tools for Learning Analytics. Presentation given at Linked Data for Learning Analytics workshop at 3rd Learning Analytics and Knowledge Conference, Leuven, Belgium, 9th of April 2013.
Design of an Evaluation Framework for Open Web Data Applications
First insights into a GCM study for the design a community agreed Evaluation Framework for the upcoming LinkedUp data competitions. The presentation was given for WP2 of the LinkedUp project at Elsevier HQ, Amsterdam. The Evaluation Framework also provides an interesting solution for standardising Learning Analytics research by comparing the results of different Applications in that domain.
Drachsler, H. & Stoyanov, S. (2013, 25th February). Design of an Evaluation Framework for Open Web Data Applications, Presentation given at the LinkedUp project meeting at Elsevier, Amsterdam, The Netherlands.
Open Science
In December I was invited to give a presentation for the OUNL mamangement board on Open Science.
This presentation shows the vision of the Open University for Open Science in an elevator pitch format.
http://portal.ou.nl/documents/363049/ff7a3426-ce4a-4081-900d-f7d352046c62
http://creativecommons.org/licenses/by-nc-sa/3.0/
Drachsler, H. (2012, 06 December). Open Science. Open Universiteit Nederland, Heerlen, Netherlands.
The CLAS app – to improve medical handover procedures
There is a high potential for mobile learning and support applications in the medical domain. In a recent research initiative we developed together with the School of Medicine at the University College Cork, Ireland a smartphone app to train writing of medical discharge letters that are crucial for handovers (transferring information from one caregiver to another).
The so-called CLAS app is based on the “Cork Letter-Writing Assessment Scale” (created by Bridget Maher) and benefits from synergies between our different medical research projects like Handover, EMuRgency and BioApp.
Handover of patient information is a time of particular risk and it is important that accurate, reliable and relevant information is clearly communicated between one caregiver to another. The World Health Organization (WHO) lists accurate handovers as one of its High 5 Patient Safety initiatives (Joint Commission on Accreditation of Healthcare Organizations, 2011). Improperly conducted handovers lead to wrong treatment, delays in medical diagnosis, life threatening adverse events, patient complaints, medical litigation, increased health care expenditure, increased hospital length of stay and a range of other effects that impact on the health system.
The CLAS mobile app is designed to standardise and improve handover communication between hospital and General Practice. Mobile applications such as CLAS offer exciting opportunities for improving patient safety and minimising medical error at handover and are just the tip of the iceberg with regard to harnessing the vast potential of mobile communications and how medical professionals interact with each other and more importantly, how they interact with the patients. The CLAS mobile application is currently the basic of two ongoing research projects.
- Assessment of the quality of 200 hospital discharge letters using the CLAS scale.
- Assessment of the effect of the CLAS intervention on the letter-writing skills of 80 fourth year medical students.
Next to the medical research, we aim to further improve the CLAS app with typical mobile application features such as taking into account sensor information from the mobile device such as GPS coordinates and audio recordings. In addition, we want to make the CLAS app more interactive by enabling the end users (doctors and patients) to synchronise handover information, thus improving the quality of information transfer at handover.
At the upcoming mLearn conference in October in Helsinki we will present further details about the CLAS app in the context of a research paper.
Prospering future for Learning Analytics
Learning Analytics is a hot research topic at the moment and I’m curious what impact it will have on the education systems on the long term. However, at the moment it is of high importance on all research agendas. It is even an explicit research topic within the next EU FP7 TEL call in January 2013. At CELSTEC we have recently won two new EU projects that are directly supporting our Learning Analytics research efforts:
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| Open Discovery Space (Started 1st of April 2012) | LinkedUp (Start’s 1st of November 2012) |
Both projects addressing the main research challenges we identified during the dataTEL project. Based on those we have identified 6 main research objectives for the upcoming years:
- Collecting, sharing and open access to educational datasets
- Evaluation of data-driven applications
- Legal aspects (Ownership, Privacy, ethics)
- Visualizations of data
- Personalization and Recommender Systems
- Awareness support and reflection
Regarding research objective 1 – Educational data:
Open Discovery Space (ODS) and LinkedUP will make vast amounts of educational data available for end users and for data driven research. The Open Discovery Space project will be based on the ARIADNE Foundation infrastructure that has been used to already deploy an initial version of the resources at the portal that provide access to a critical mass of about 1.000.000 content resources. This existing critical mass of eLearning resources will be expanded over the runtime of the project up to ~1,550,000 resources in total. It is expected to be connected to around 15 educational portals of regional, national or thematic coverage. Besides providing the educational resources ODS will create technology to share and collect also social data about the educational resources (ratings, tags and annotations) and make them available as Linked Data. With these objectives ODS contributes to the research objectives of the Learning Analytics and Linked Data workshop we organized at the LAK12 conference.
LinkedUp also aims to make more educational datasets publicly accessible. It will therefore create a pool of existing educational datasets and organize various support and trainings activities around this data pool to stimulate the development of new and innovative data driven tools for Technology-Enhanced Learning and Learning Analytics.
LinkedUp will therefore strongly follow the Linked Data approach which has been applied successfully in a wide area of domains to expose datasets from a large variety of sources, leading to a globally distributed Web cloud of over 31 billion distinct statements. The following table provides an overview of the currently available datasets in the LOD cloud (source: http://lod-cloud.net/state).
Next to Open Educational Resources and Linked Data will LinkedUp also consider publicly accessible data from data-driven companies such as Open Calais Reuter or Mendeley. These companies provide access to their data over API’s that can be used to develop innovative data products within the LinkedUp competition.
Regarding research objective 2 – Evaluation of data applications:
There is a pressing need in Learning Analytics to make the effects of different data applications on learning and the stakeholders comparable to identify best practice examples. Until now there is no common knowledge which algorithm works better than another with a certain user model in a specific learning settings. LinkedUp directly address this challenge by developing an evaluation framework that can be applied to evaluated data –driven applications. The evaluation framework will be one of the major outcomes of the project. It will be developed together with a board of 30 experts in the field through the Group Concept Mapping approach.
Regarding research objective 3 – Legal aspects:
In this context both projects have to come up with solutions that enable the use of educational data for data support applications. Both projects will therefore mainly focus on the creative commons license model. All data sets for which this is appropriate shall be published on the project’s web site under a Creative Commons licence (http://creativecommons.org/) or another appropriate license. In addition we want to explore related initiatives like the Creative Commons Learning Resource Metadata Initiative (LRMI) that aims to merge different competing initiatives in the area of OER description and at producing a usable and well-defined RDF schema for Learning Resource description (http://wiki.creativecommons.org/LRMI). Regarding, privacy and ethics both projects will review privacy requirements and concerns in each participating country in order to develop a suitable IPR & licensing agreement for the data pools.
Research objectives 4-6 – Visualizations, Personalization, and Awareness support:
These research objectives will also be addressed by both projects at a later stage. ODS addresses all three research objectives by providing innovative navigation and visualizations tools to explore the vast amount of collected data within the ODS portal in a personalized way. We will investigate how to combine visualization and social navigation to increase the satisfaction of users when searching for resources as well as explaining the rationale for the various selections or recommendations. Within LinkedUp we will support various projects that focus on these research objectives within the LinkedUp competition. We will organize three data competitions and support the participants with suitable datasets, technology support workshops, and provide substantial funding based on the assessment of the tools of the participating teams with the evaluation framework.
Looking forward to these exciting research activities!
Presentation on Confidence in Learning Analytics / The Pulse of LA
Below the presentation of the paper written by Wolfgang Greller and myself and our international survey on Confidence in Learning Analytics at the LAK12 conference, Vancouver, Canada. The framework study was rated by many stakeholders as very helpful to describe the current needs of the young Learning Analytics field. There are quite some pointers to the study made by other researchers like SURF or OUUK. It was rested as the most helpful model to introduce learning analytics and the core research challenges to related stakeholders.
The article reported the results of an exploratory community survey in learning analytics that aimed at extracting the perceptions, expectations and levels of understanding of stakeholders in the domain. Divided up into six different dimensions we came to a number of conclusions which we are going to present below.
- Stakeholders: Participants identified the main beneficiaries in learning analytics as learners and teachers followed by organisations. Furthermore, the majority of respondents agreed that the biggest benefits would be gained in the teacher-to-student relationship and that learners would almost certainly require teacher help to learn from an analysis and for taking the right course of action. This is rather surprising as learning analytics is seen by many researchers as an innovative liberating force that would be able to change traditional learning by reflection and peer support, thus strengthening independent and lifelong learning. This latter opinion on independence could be seen in the ‘objective’ section of the survey (cf. chapter 3.2 above) where the majority expressed a preference for learning analytics to pay special attention to non-formalised and innovative ways of teaching and learning. Yet, respondents expect less potential impact on the student-to-student and the teacher-to-teacher relationships. This current perspective may be affected by the scarcity of learning analytics applications that demonstrate the innovative possibilities for learning and teaching. Thus people may not have a clear point of reference as, for example, is the case for ‘social networks’ where an established group of competitive platforms already exists.
- Objectives: The survey concludes further that research on learning analytics should focus on reflection support. The attained results clearly emphasized the importance of ‘stimulating reflection in the stakeholders about their own performance’. This goal could be supported by revealing hitherto hidden information about learners, which was the second most important objective. At the same time more timely information, institutional insights, and insights into the learning context were other areas of interest to the constituency.
- Data: Our institutional inventory in chapter 3.3 gives an overview of the most widespread IT systems. These could be prioritised by learning analytics technologies to gain an institutional foothold. They also provide the best ground for inter-institutional data sharing. Anonymisation can perhaps be seen as the most important enabler for such sharing to happen. It is emphasised in a number of responses as the second most important data attribute and confirmed in the willingness of people to share if data is anonymised. For a clear majority anonymisation also reduces fears of privacy breaches through sharing (cf. chapter 3.5). On the other hand, when it comes to internal sharing with departments and operations’ units of the same institution, the use of available data will continue to be an uphill struggle, and, according to participants, require good justification. Here, perhaps, a clearer mandate to ethical boards may help. These are already widely in place.
- Methods: Chapter 3.4 on methods revealed that trust in learning analytics algorithms is not well developed. We interpret the mid-range return levels as hesitation towards “calculating” education and learning. What seems interesting to us is that the widely interpretable hope for gaining a comprehensive view on the learning progress was given the highest confidence, but perhaps this shows wishful thinking rather than a real expectation. Overall rather low was the expectations of impact on assessment. A majority of people did not see easier or more objective assessments coming out of learning analytics (cf. chapter 3.2). They were also not fully convinced that it would provide a good assessment of a learner’s state of knowledge (cf. chapter 3.4).
- Constraints: A large proportion of respondents thought learning analytics may lead to breaches of privacy and intrusion. Yet, they ranked privacy and ethical aspects as of lesser importance to consider (cf. chapter 3.5) or as belonging to further competence development (cf. chapter 3.6). However, data ownership was expressed as highly important. This may be interpreted in that way that if ownership of data lies with the learners themselves, there is no perceived risk for privacy or ethical abuse. In any case, it seems that many organisations have ethical boards and guidelines in place. These may come to play an increasingly important role for institutional data exploitation since a large number of respondents trust that anonymisation of educational data is possible but not necessarily sufficient to enable full internal exploitation of the educational data within an organisation.
- Competences: In the area of competences, participants mainly stressed the importance of self-directedness, critical reflection, analytic skills, and evaluation skills. On the other hand, few believe that students already possess these skills. This indicates to us a need to support students in developing these learning analytics competences. In conclusion of this section we can say, that the results suggest that there is little faith that learning analytics will lead to more independence of learners to control and manage their learning process. This identifies a clear need to guide students to more self-directedness and critical reflection if learning analytics should be applied more broadly in education. This interpretation is quite in contrast with some suggestions made with respect to empowerment of learners through providing graphical reflection of the learning process and further access to additional information regarding their learning progress.
The dataset used for this article and a pre-print of the study is available at the dspace.ou.nl repository (at http://dspace.ou.nl/handle/1820/3850). In that way, we would like to encourage the learning analytics community to gain additional insights from our dataset for the fast evolving of the learning analytics research topic.
International Workshop on Learning Analytics and Linked Data (#LALD2012)
OUNL is really a corner stone at LAK12 conference with 2 workshops and 2 full papers. Together with some international colleagues (George Siemens, Dragan Gasevic, Stefan Dietze, Wolfgang Reinhard, and Abelardo Pardo) we organized a full day workshop on ‘Linked Data and Learning Analytics – #LALD’ at the LAK12.
LALD is a very visionary workshop that assumes that linked datasets will become increasingly important for data driven research. We envision that in a close future research will take advantage of kind of configuration files that create linked datasets that can be used for data driven research. At the moment Learning Analytics and data research lack publicly available datasets to test and compare their findings. The main objective of the 1st International Workshop on Learning Analytics and Linked Data (#LALD2012) is to connect the research efforts on Linked Data and Learning Analytics in order to create visionary ideas and foster synergies between the two young research fields.
Below you can find the slides we used dunking the workshop.
Learning Analytics and Linked Data Workshop at LAK12
Dragan’s slides on semantic web are here: [here]
Representation of the data is critical to sense making: [here]
Learning analytics and guidelines for ethical use: [here]
The connectivist pedagogy, a concept that addressed the potential of Learning Analytics and linked data:
Anderson and Dron 2011 [here]
Introduction slides for the LALD workshop at the LAK12
Hi folks, here you can find the introduction slides for the LALD workshop 29th of April 2012 at the LAK12, Vancouver, Canada. Looking forward to it, as we made very good experiences with the PMI rating and the Grand Challenge task in previous dataTEL workshops.
Agenda of the Learning Analytics and Linked Data workshop (#LALD) at the LAK12 conference
Here you can find the agenda for the 1st International Workshop on Learning Analytics and Linked Data (#LALD) at the 2nd International Conference on Learning Analytics and Knowledge (LAK12), Vancouver, Canada
The workshop is co-orgnised by the EATEL SIG dataTEL and the LinkedEducation.org research initiatives. It is motivated by the multitude of datasets exists in TEL that offer new opportunities for teaching and learning. The available datasets can be roughly distinguished between (a) Open Web Data to (b) Personal Learning data originating from different learning environments.
Open Web data covers educational data publicly available on the Web, such as Linked Open Data (LOD) published by institutions about their courses and other resources; examples include (but are not limited to), e.g., The Open University (UK), the National Research Council (CNR, Italy), Southampton University (UK) or the mEducator Linked Educational Resources. It also includes the emergence of LD-based metadata schemas and TEL-related datasets. The main driver in the adoption of the LOD approach in the educational domain is the enrichment of the learning content and the learning experience by making use of various connected data sources.
Personal Learning data from different learning environments originate from tracking learners’ interactions with different tools and resources. The main driver for analyzing these data is the vision of personalized learning that offers potential to create more effective learning experiences through new possibilities for the prediction of and reflection over the learning processes.
The main objective of the LALD workshop is to connect the research efforts on LinkedData and Learning Analytics to create visionary ideas about how the synergy of Web of Data and Learning Analytics can transform and support TEL processes and applications. Therefore, the workshop will explore, collect and review datasets for TEL to discuss Learning Analytics approaches which make use of the Web of Data. During the workshop, an overview of available educational datasets will be given. The participants will have the opportunity to present own datasets or dataset descriptions, show their own data products and tools, and workout Grand Challenges that need to be overcome to collect, use and share educational datasets and their products.
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