Presentation given at Serious Request 2015, #SR15, Heerlen.
Within the Open University we started a 12 hours marathon college, to collect money for the charity action of radiostation 3FM. The collected money will go to the red cross and support young people in conflict areas.
Big Data, Data Science, Machine learning creating tremendous value in the education sector. Combination of open source with IBM value adds create compelling value. Artificial intelligence will revolutionize the sector with making education more relevant with Cognitive capabilities of students.
Big Data, Data Science, Machine learning creating tremendous value in the education sector. Combination of open source with IBM value adds create compelling value. Artificial intelligence will revolutionize the sector with making education more relevant with Cognitive capabilities of students.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
Presentation given by KPMG at the United Nations on the Internet of Things and the potential for sustainable development, with a focus on transportation. September 2016.
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic...DATAVERSITY
The Digital Economy is changing the way organizations do business across the globe, and is set to transform the economy on an unprecedented scale. Business optimization, and entirely new business models are emerging as data-driven technology provides unprecedented opportunity for innovation and change. In many organizations, data not only supports business profitability, but data itself has become the critical business asset.
What does it mean to leverage data as a business asset? And how can today’s data-centric technologies support the data-driven revolution? Join our expert panelists as they discuss the latest innovations in the data landscape.
Education 4.0 … the future of learning will be dramatically different, in school and throughout life.
Global connectivity, smart machines, and new media are just some of the drivers reshaping how we think about work, what constitutes work, and how we learn and develop the skills to work in the future. The concept of a “100 year life” becoming the norm, and the majority of that spent studying and working, means that learning will be a lot more important, and different, for the next generations. Most people will have at least 6 different careers, requiring fundamental reeducating, whilst the relentless speed of innovation will constantly demand new skills and knowledge to keep pace, let alone an edge.
“Education 4.0” is my vision for the future of education, which
1.) responds to the needs of “industry 4.0” or the fourth industrial revolution, where man and machine align to enable new possibilities
2.) harnesses the potential of digital technologies, personalized data, open sourced content, and the new humanity of this globally-connected, technology-fueled world
3.) establishes a blueprint for the future of learning – lifelong learning – from childhood schooling to continuous learning in the workplace, to learning to play a better role in society.
Baking analytics into the culture of an organization is not always the easiest thing because it doesn't come intuitively to humans. This presentation was given at Kumpul co-working space in Sanur, Bali and it involves a sharing of my team's experience in building a data-driven culture at TradeGecko.
Artificial intelligence in Energy and Utilities – Market OverviewIndigo Advisory Group
Artificial Intelligence has been around for decades, however, over the past 2-3 years the technology has been finding applications across a series of sectors, including energy and utilities. This presentation includes some of the highlights given on an Engerati Webinar on September 27th including three major application areas.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
This presentation focuses on the “Data - Big Data - Bigger Data” and the Challenges, Opportunities and Solutions from these trends.
What are the Challenges this massive data brings to the table?
What are the opportunities this data provide ?
Some solutions on how to handle this data.
I often hear from clients: “We don’t know much about Big Data – can you tell us what it is and how it can help our business?” Yes! The first step is this vendor-free presentation, where I start with a business level discussion, not a technical one. Big Data is an opportunity to re-imagine our world, to track new signals that were once impossible, to change the way we experience our communities, our places of work and our personal lives. I will help you to identify the business value opportunity from Big Data and how to operationalize it. Yes, we will cover the buzz words: modern data warehouse, Hadoop, cloud, MPP, Internet of Things, and Data Lake, but I will show use cases to better understand them. In the end, I will give you the ammo to go to your manager and say “We need Big Data an here is why!” Because if you are not utilizing Big Data to help you make better business decisions, you can bet your competitors are.
Privacy and Analytics – it’s a DELICATE Issue. A Checklist for Trusted Learni...Hendrik Drachsler
The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups about privacy and ethics applied to the handling of personal data. In this ongoing discussion, fears and realities are often indistin-guishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution’s learning support by implementing data and analytics with a view on improving student success. In this presentation, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
Presentation given by KPMG at the United Nations on the Internet of Things and the potential for sustainable development, with a focus on transportation. September 2016.
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic...DATAVERSITY
The Digital Economy is changing the way organizations do business across the globe, and is set to transform the economy on an unprecedented scale. Business optimization, and entirely new business models are emerging as data-driven technology provides unprecedented opportunity for innovation and change. In many organizations, data not only supports business profitability, but data itself has become the critical business asset.
What does it mean to leverage data as a business asset? And how can today’s data-centric technologies support the data-driven revolution? Join our expert panelists as they discuss the latest innovations in the data landscape.
Education 4.0 … the future of learning will be dramatically different, in school and throughout life.
Global connectivity, smart machines, and new media are just some of the drivers reshaping how we think about work, what constitutes work, and how we learn and develop the skills to work in the future. The concept of a “100 year life” becoming the norm, and the majority of that spent studying and working, means that learning will be a lot more important, and different, for the next generations. Most people will have at least 6 different careers, requiring fundamental reeducating, whilst the relentless speed of innovation will constantly demand new skills and knowledge to keep pace, let alone an edge.
“Education 4.0” is my vision for the future of education, which
1.) responds to the needs of “industry 4.0” or the fourth industrial revolution, where man and machine align to enable new possibilities
2.) harnesses the potential of digital technologies, personalized data, open sourced content, and the new humanity of this globally-connected, technology-fueled world
3.) establishes a blueprint for the future of learning – lifelong learning – from childhood schooling to continuous learning in the workplace, to learning to play a better role in society.
Baking analytics into the culture of an organization is not always the easiest thing because it doesn't come intuitively to humans. This presentation was given at Kumpul co-working space in Sanur, Bali and it involves a sharing of my team's experience in building a data-driven culture at TradeGecko.
Artificial intelligence in Energy and Utilities – Market OverviewIndigo Advisory Group
Artificial Intelligence has been around for decades, however, over the past 2-3 years the technology has been finding applications across a series of sectors, including energy and utilities. This presentation includes some of the highlights given on an Engerati Webinar on September 27th including three major application areas.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
This presentation focuses on the “Data - Big Data - Bigger Data” and the Challenges, Opportunities and Solutions from these trends.
What are the Challenges this massive data brings to the table?
What are the opportunities this data provide ?
Some solutions on how to handle this data.
I often hear from clients: “We don’t know much about Big Data – can you tell us what it is and how it can help our business?” Yes! The first step is this vendor-free presentation, where I start with a business level discussion, not a technical one. Big Data is an opportunity to re-imagine our world, to track new signals that were once impossible, to change the way we experience our communities, our places of work and our personal lives. I will help you to identify the business value opportunity from Big Data and how to operationalize it. Yes, we will cover the buzz words: modern data warehouse, Hadoop, cloud, MPP, Internet of Things, and Data Lake, but I will show use cases to better understand them. In the end, I will give you the ammo to go to your manager and say “We need Big Data an here is why!” Because if you are not utilizing Big Data to help you make better business decisions, you can bet your competitors are.
Privacy and Analytics – it’s a DELICATE Issue. A Checklist for Trusted Learni...Hendrik Drachsler
The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups about privacy and ethics applied to the handling of personal data. In this ongoing discussion, fears and realities are often indistin-guishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution’s learning support by implementing data and analytics with a view on improving student success. In this presentation, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.
Dutch Cooking with xAPI Recipes, The Good, the Bad, and the ConsistentHendrik Drachsler
This paper presents the experiences of several Dutch projects in their application of the xAPI standard and different design patterns including the deployment of Learning Record Stores. In this paper we share insights and argue for the formation of an international Special Interest Group on interoperability issues to contribute to the Open Analytics Framework as envisioned by SoLAR and enacted by the Apereo Learning Analytics Initiative. Therefore, we provide an overview of the advantages and disadvantages of implementing the current xAPI standard by presenting projects that applied xAPI in very different ways followed by the lessons learned.
Presentation given at PELARS Policy event, Brussles, 09.11.2016. A follow up op the first LACE Policy event in April 2015. Special focus is on the exploitation and sustainability activities for LACE in the SIG LACE SoLAR.
Slides with notes used to support a keynote at the UKSG FE event 'Resourcing a rich and varied curriculum' at The King's Fund, London, on 30 November 2016. Event information at http://www.uksg.org/event/FE301116.
De toekomst van Learning Analytics - wat is haalbaar en wat is wenselijk?SURF Events
Woensdag 11 november
Sessieronde 4
Titel: De toekomst van Learning Analytics - wat is haalbaar en wat is wenselijk?
Spreker(s): Doug Clow (Open University UK), Hendrik Drachsler (Open Universiteit)
Zaal: Leeuwen I
Guest presentation: SASUF Symposium: Digital Technologies, Big Data, and Cybersecurity, Vaal University of Technology, Vanderbijlpark, South Africa, 15 May 2018
25 Technology Ideas in 60 Minutes. Technology for Educators.Scott Davis
25 Excellent Technology Resources for any educator. This presentation demos 25 ways you can use technology to increase learning in your classroom. Please contact me with any questions or suggestions.
Workshop materials for vocational further education college staff on a blended learning journey - referencing EU standards for teachers and learners digital literacy
B9_21_子供のプライバシー対策に必要なもの Speakers' slide deck for Privacy By Design Conference...Keiko Tanaka
Speakers' slide deck for Privacy By Design Conference 2023, session on Protecting Privacy of Children Through Data Governance.
Privacy by Design Conference 2023とは
プライバシーに関わる、文化、法律、テクノロジー、ビジネス、オペレーションなどのさまざまな立場の方が、多様な視点で対話を行うためのカンファレンスです。
15:20 ~ 16:20 「子供のプライバシー対策に必要なもの」
MyData Global Board Member 2020 & 2021 Dixon Siu
京都情報大学院大学 助教 田中恵子
EDDS創始者、ロンドン・スクール・オブ・エコノミクス客員研究員 ヴェリスラーバ・ヒルマン 氏
一般社団法人Privacy by Design Lab 代表理事 栗原宏平
In May 2018, the new General Data Protection Regulation (GDPR) will enter into force in the European Union. This new regulation is considered as the most modern data protection law for Big Data societies of tomorrow. The GDPR will bring major changes to data ownership and the way data can be accessed, processed, stored, and analysed in the European Union. From May 2018 onwards, data subjects gain fundamental rights such as ‘the right to access data’ or ‘the right to be forgotten’. This will force Big Data system designers to follow a privacy-by-design approach for their infrastructures and fundamentally change the way data can be treated in the European Union.
The presentation provides an overview of the Trusted Learning Analytics Programme as it has been recently initiated at the University of Frankfurt and the DIPF research institute in Germany. Educational data is under special focus of the GDPR, as it is considered as highly sensitive like data from a nuclear plant. It shows opportunities and challenges for using educational data for learning analytics purposes under the light of the GDPR 2018.
In this webinar, Prof Hendrik Drachsler will reflect on the process of applying learning analytics solutions within higher education settings, its implications, and the critical lessons learned in the Trusted Learning Research Program. The talk will focus on the experience of edutec.science research collective consisting of researchers from the Netherlands and Germany that contribute to the Trusted Learning Analytics (TLA) research program. The TLA program aims to provide actionable and supportive feedback to students and stands in the tradition of human-centered learning analytics concepts. Thus, the TLA program aims to contribute to unfolding the full potential of each learner. It, therefore, applies sensor technology to support psychomotor as well as web technology to support meta-cognitive and collaborative learning skills with high-informative feedback methods. Prof. Drachsler applies validated measurement instruments from the field of psychometric and investigates to what extent Learning Analytics interventions can reproduce the findings of these instruments. During this webinar, Prof Drachsler will discuss the lessons learned from implementing TLA systems. He will touch on TLA prerequisites like ethics, privacy, and data protection, as well as high informative feedback for psychomotor, collaborative, and meta-cognitive competencies and the ongoing research towards a repository, methods, tools and skills that facilitate the uptake of TLA in Germany and the Netherlands.
Smart Speaker as Studying Assistant by Joao ParganaHendrik Drachsler
The thesis by Joao Pargana followed two main goals, first, a smart speaker application was created to support learners in informal learning processes through a question/answer application. Second, the impact of the application was tested amongst various users by analyzing how adoption and
transition to newer learning procedures can occur.
Dieser Entwurf eines Verhaltenskodex richtet sich an Hochschulen, die mittels Learning Analytics die Qualität des Lernens und Lehrens verbessern wollen. Der Kodex kann als Vorlage zur Erstellung von organisationsspezifischen Verhaltenskodizes dienen. Er sollte an Hochschulen, die Learning Analytics einführen wollen, durch Konsultationen mit allen Interessengruppen überprüft und an die Ziele sowie die bestehende Praxis innerhalb der jeweiligen Hochschulen angepasst werden. Der Kodex wurde auf Grundlage einer Analyse bestehender europäischer Kodizes und der in Deutschland geltenden Rechtsgrundlage vom Innovationsforum Trusted Learning Analytics des hessenweiten Projektes "Digital gestütztes Lehren und Lernen in Hessen" entwickelt.
Abstract (English):
This code of conduct can be used as a template for creating organization-specific codes of conduct in Germany. The Code was developed on the basis of an analysis of existing European codes of conduct and the legal basis for the usage of data in higher education in Germany.
Rödling, S. (2019). Entwicklung einer Applikation zum assoziativen Medien Ler...Hendrik Drachsler
Ziel der vorliegenden Bachelorarbeit ist es, den Einfluss von zusätzlicher am Handgelenk wahr-genommener Vibration in Verbindung mit der visuellen Darstellung eines Lerninhaltes auf denLernerfolg zu messen. Der Lernerfolg wird hierbei durch die Lerngeschwindigkeit sowie denUmfang der Wissenskonsolidierung über die Testreihe definiert. Zu diesem Zweck wurde eine Experimentalstudie zumAssoziativen Lernendurchgeführt. Für die Studie verwendeten 33Probanden eine App, die für die vorliegende Arbeit entwickelt wurde. Im Mittel aller Studiener-gebnisse wurden sowohl für die Lerngeschwindigkeit als auch für die Wissenskonsolidierungbessere Werte erzielt, wenn die Probanden die Möglichkeit hatten, den Lerninhalt sowohl visu-ell als auch haptisch zu erfahren. Die festgestellten Unterschiede des Lernerfolges erreichtenjedoch keine statistische Signifikanz. Die Abweichungen der Ergebnisse nach der Umsetzungder vorgeschlagenen Änderungen am Studiendesign sind abzuwarten. Die Bachelorarbeit ist vor allem für den Bildungsbereich interessant.
The present bachelor thesis aims to measure the influence of vibration perceived at the wrist in connection with the visual representation of learning content on the learning success. The learning success is defined by the learning speed and the extent of knowledge consolidation over the test series. For this purpose, an experimental study on Associative Learning was conducted. For the study, 33 test persons used an app, which was developed for the present work. On average of all study results better values were achieved for both learning speed and knowledge consolidation, if the test persons could experience the learning content both visually and haptically. However, the differences in learning outcomes did not reach statistical significance. The results of the deviations after the implementation of the proposed changes to the study design must be awaited. The Bachelor’s thesis is particularly interesting for the education sector.
E.Leute: Learning the impact of Learning Analytics with an authentic datasetHendrik Drachsler
Nowadays, data sets of the interactions of users and their corresponding demographic data are becoming more and more valuable for companies and academic institutions like universities
when optimizing their key performance indicators. Whether it is to develop a model to predict the optimal learning path for a student or to sell customers additional products, data sets to
train these models are in high demand. Despite the importance and need for big data sets it still has not become apparent to every decision-maker how crucial data sets like these are for the
future success of their operations.
The objective of this thesis is to demonstrate the use of a data set, gathered from the virtual learning environment of a distance learning university, by answering a selection of questions in
Learning Analytics. Therefore, a real-world data set was analyzed and the selected questions were answered by using state-of-the-art machine learning algorithms.
Romano, G. (2019) Dancing Trainer: A System For Humans To Learn Dancing Using...Hendrik Drachsler
Masters thesis by Romano, G., (2019). Dancing is the ability to feel the music and express it in rhythmic movements with the body. But learning how to dance can be challenging because it requires proper coordination and understanding of rhythm and beat. Dancing courses, online courses or learning with free content are ways to learn dancing. However, solutions with human-computer interaction are rare or
missing. The Dancing Trainer (DT) is proposed as a generic solution to fill this gap. For the beginning, only Salsa is implemented, but more dancing styles can be added. The DT uses the Kinect to interact multimodally with the user. Moreover, this work shows that dancing steps can be defined as gestures with the Kinect v2 to build a dancing corpus. An experiment with
25 participants is conducted to determine the user experience, strengths and weaknesses of the DT. The outcome shows that the users liked the system and that basic dancing steps were
learned.
Fighting level 3: From the LA framework to LA practice on the micro-levelHendrik Drachsler
This presentation explores shortcomings of learning analytics for the wide adoption in educational organisations. It is NOT about ethics and privacy rather than focuses on shortcomings of learning analytics for teachers and students in the classroom (micro-level). We investigated if and to what extend learning analytics dashboards are addressing educational concepts. Map opportunities and challenges for the use of Learning Analytics dashboards for the design of courses, and present an evaluation instrument for the effects of Learning Analytics called EFLA. EFLA can be used to measure the effects of LA tools at the teacher and student side. It is a robust but light (8 items) measurement to quickly investigate the level of adoption of learning analytics in a course (micro-level). The presentation concludes that Learning Analytics is still to much a computer science dicipline that does not fulfill the often claimed position of the middle space between educational and computer science research.
Recommendations for Open Online Education: An Algorithmic StudyHendrik Drachsler
Recommending courses to students in online platforms is studied widely. Almost all studies target closed platforms, that belong to a University or some other educational provider. This makes the course recommenders situation specific. Over the last years, a demand has developed for recommender system that suit open online platforms. Those platforms have some common characteristics, such as the lack of rich user profiles with content metadata. Instead they log user interactions within the platform that can be used for analysis and personalization. In this paper, we investigate how user interactions and activities tracked within open online learning platforms can be used to provide recommendations. We present a study in which we investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. We use data from the OpenU open online learning platform that is in use by the Open University of the Netherlands. The results show that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system proves to outperform the classical approaches on prediction accuracy of recommendations in terms of recall. We conclude that, if the algorithms are chosen wisely, recommenders can contribute to a better experience of learners in open online courses.
Soude Fazeli, Enayat Rajabi, Leonardo Lezcano, Hendrik Drachsler, Peter Sloep
DELICATE checklist - to establish trusted Learning AnalyticsHendrik Drachsler
The DELICATE checklist contains eight action points that should be considered by managers and decision makers planning the implementation of Learning Analytics / Educational Data Mining solutions either for their own institution or with an external provider.
The eight points are:
1. Determination: Decide on the purpose of learning analytics for your institution. What aspects of learning or learner services are you trying to improve?
2. Explain: Define the scope of data collection and usage. Who has a need to have access to the data or the results? Who manages the datasets? On what criteria?
3. Legitimate: Explain how you operate within the legal frameworks, refer to the essential legislation. Is the data collection excessive, random, or fit for purpose?
4. Involve: Talk to stakeholders and give assurances about the data distribution and use. Give as much control as possible to data subjects (permission architecture), and provide access to their data for the individuals.
5. Consent: Seek consent through clear consent questions. Provide an opt-out option.
6. Anonymise: De-identify individuals as much as possible, aggregate data into meta-models.
7. Technical aspects: Monitor who has access to data, especially in areas with high staff turn-over. Establish data storage to high security standards.
8. External partners: Make sure externals provide highest data security standards. Ensure data is only used for intended purposes and not passed on to third parties.
We hope that the DELICATE checklist will be a helpful instrument for any educational institution to demystify the ethics and privacy discussions around Learning Analytics. As we have tried to show in this article, there are ways to design and provide privacy conform Learning Analytics that can benefit all stakeholders and keep control with the users themselves and within the established trusted relationship between them and the institution.
Updated Flyer of the LACE project with latest tangible outcomes and collaboration possibilities.
LACE connects players in the fields of Learning Analytics (LA) and Educational Data Mining (EDM) in order to support the development of a European community and share emerging best practices.
Objectives
-------------
• Promote knowledge creation and exchange
• Increase the evidence base about Learning Analytics
• Contribute to the definition of future directions
• Build consensus on pressing topics like data interoperability, data sharing, ethics and privacy, and Learning Analytics supported instructional design
Activities
• Organise events to connect organisations that are conducting LA/EDM research
• Create and curate a knowledge base to capture evidence for the effectiveness of Learning Analytics
• Produce reviews to inform the LACE community about latest developments in the field
The Impact of Learning Analytics on the Dutch Education SystemHendrik Drachsler
The article reports the findings of a Group Concept Mapping
study that was conducted within the framework of the Learning Analytics Summer Institute (LASI) in the Netherlands. Learning Analytics are expected to be beneficial for students and teacher empowerment, personalization, research on learning design, and feedback for performance. The study depicted some management and economics issues and identified some possible treats. No differences were found between novices and experts on how important and feasible are changes in education triggered by
Learning Analytics.
Paper available at: http://dl.acm.org/citation.cfm?id=2567617&CFID=427722877&CFTOKEN=73282080
What do analytics on learning analytics tell us? How can we make sense of this emerging field’s historical roots, current state, and future trends, based on how its members report and debate their research?
Challenge submissions should exploit the LAK Dataset for a meaningful purpose. This may include submissions which cover one or more of the following, non-exclusive list of topics:
Analysis & assessment of the emerging LAK community in terms of topics, people, citations or connections with other fields
Innovative applications to explore, navigate and visualise the dataset (and/or its correlation with other datasets)
Usage of the dataset as part of recommender systems
Analysis of the evolution of LAK discipline
Improvement or enrichment of the LAK Dataset
Standardisierte Medizinische Übergaben - Wie lernen, lehren und implementiere...Hendrik Drachsler
Presentation given at Workshop 22 Jahrestagung der Gesellschaft für Medizinische Ausbildung, 27.09.2013, GMA2013, Graz, Austria.
http://portal.ou.nl/documents/363049/fd32b9eb-df7b-4b18-bf5a-d9560425625e
http://creativecommons.org/licenses/by-nc-sa/3.0/
Sopka, S., Druener, S., Stieger, L., Hynes, H., Stoyanov, S., Orrego, C., Secanell, M., Maher, B., Henn, P., Drachsler, H. (2013). Standardized Medical handovers – How to Learn, teach and implement? Workshop at Jahrestagung der Gesellschaft für Medizinische Ausbildung (Annual Meeting of the Society for Medical Education), Graz, Austria.
The presentation provides an overview of the R&D activities of the Learning Analytics topic at the Open Universiteit in October 2013.
http://portal.ou.nl/documents/363049/789b3323-d55c-4e3e-93ba-a716ade14463
http://creativecommons.org/licenses/by-nc-sa/3.0/
Drachsler, H., Specht, M. (2013).
Hoe ziet de toekomst van Learning Analytics er uit?Hendrik Drachsler
Presentation given in the Dutch Masterclass: 'Hoe ziet de toekomst van Learning Analytics er uit?'
http://portal.ou.nl/documents/363049/1adc41e5-52f5-4b08-8b98-bf19b635931a
http://creativecommons.org/licenses/by-nc-sa/3.0/
Drachsler, H., (September, 2013). Hoe ziet de toekomst van Learning Analytics er uit? Open Universiteit, CELSTEC, Heerlen, The Netherlands.
Presentation given at Learning Analytics Summer School Institute (LASI) to kickoff the national GCM study on LA, Amsterdam, The Netherlands.
http://portal.ou.nl/documents/363049/3430aeb1-2450-4587-8f26-e56efd7b80c4
http://creativecommons.org/licenses/by-nc-sa/3.0/
Stoyanov, S., Drachsler, H. (2013). Group Concept Mapping on Learning Analytics. Presentation given at Learning Analytics Summer School Institute (LASI) to kickoff the national GCM study on LA, Amsterdam, The Netherlands.
TEL4Health research at University College Cork (UCC)Hendrik Drachsler
Invited talk given at Application of Science to Simulation, Education and Research on Training for Health Professionals Centre (ASSERT for Health Care)
http://portal.ou.nl/documents/363049/e42710d3-255b-46df-bcba-169f7a5e0341
http://creativecommons.org/licenses/by-nc-sa/3.0/
Drachsler, H., (May, 2013). TEL4Health research at University College Cork (UCC). Invited talk given at Application of Science to Simulation, Education and Research on Training for Health Professionals Centre (ASSERT for Health Care). Cork, Ireland.
Evaluation of Linked Data tools for Learning AnalyticsHendrik Drachsler
Presentation given in the tutorial on 'Using Linked Data for Learning Analytics' at LAK13.
http://portal.ou.nl/documents/363049/ca242534-8996-4fc7-8e42-073cc194c763
http://creativecommons.org/licenses/by-nc-sa/3.0/
Drachsler, H., Herder, E., d'Aquin, M., Dietze, S. (2013). Presentation given in the tutorial on 'Using Linked Data for Learning Analytics' at LAK2013, the Third Conference on Learning Analytics and Knowledge, Leuven, Belgium.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
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6. 17
Graph by Rob Koper. Data science voor de realisatie van online activerend onderwijs.
Presentation given at Dag van het Onderwijs (5 November 2015). Heerlen. The Netherlands
New insights
Learning
Activities
Studytime
in days
7. 17
Graph by Rob Koper. Data science voor de realisatie van online activerend onderwijs.
Presentation given at Dag van het Onderwijs (5 November 2015). Heerlen. The Netherlands
New insights
Learning
Activities
Studytime
in days
8. Chances vs. Issues of Learning Analytics
Chances
1.Monitoring learning
while it happens
2.On demand Learning
Measures
3.Personalize education
4.Identify students
at risk early
5.Reflection and
Feedback support
Dangers
1. Data not protected
2. Anonymisation
3. Unfair discrimination
4. Different behaviour
through panoptic
effect
5. No trust into the
organisation
@Hdrachsler, #SR15
9.
Step 1:
Short presentation of a vision (Marty)
Step 2:
Rate Vision on (Audience)
1. ‘haalbaar’ =‘feasible’
2. ‘wenselijk’ = ‘desirable’
By raising your hands!
Envision the future of Learning Analytics …
@Hdrachsler, #SR15
10. Vision 1: 2025, LA are essential tools for educational
management
10Pic by: Janneke Staaks, https://www.flickr.com/photos/jannekestaaks/14204590229/
• A wide range of data about
learner behaviour is used
• This generates good quality,
real-time predictions about
likely study success
• Learners, teachers,
managers and policymakers
have access to live
information from schools
11. Vision 2: 2025, Learning analytics support self-
directed autonomous learning
11Pic by: SparkFun, https://www.flickr.com/photos/sparkfun/4536382170/
• No Curricula and assessment
anymore
• Students create communities for
own learning goals
• Analytics support information
exchange and group
collaborations
• Teachers become MENTORS
12. Vision 3: 2025, analytics are rarely used in education
12Pic by: Tara Hunt, https://www.flickr.com/photos/missrogue/94403705
• Courses that are automated by
analytics are seen as inferior
• There have been major leaks
and misuse of sensitive
personal data
• All use of data for educational
purposes has to be approved by
the learners and inspectorates
13. Vision 4: 2025, classrooms monitor the physical
environment to support learning and teaching
13Pic by: SparkFun, https://www.flickr.com/photos/sparkfun/4536382170/
• Furniture, pens, writing pads –
almost any learning tool uses
sensors.
• Information is used to monitor
learners’ progress.
• Teachers are alerted to signs of
individual learner’s boredom,
confusion, and deviation from task.
14. Vision 5: 2025, most teaching is delegated to
computers
14Pic by: Charis Tsevis, https://farm6.staticflickr.com/5215/5470451264_c0612f2102_z
• Enormous datasets containing
information about millions of
learners
• Computers suggest successful
routes for learning
• Recommendations are better
informed and more reliable than by
even the best-trained humans
15. Vision 6: 2025, personal data tracking supports learning
15Pic by: SparkFun, https://www.flickr.com/photos/sparkfun/4536382170/
• Sensors gather personal
information about factors such as
posture, attention, rest, stress,
blood sugar, etc.
• This data helps people to master
skills as swimming, driving, and
passing examinations
• Programmes using this data to
optimise learning for different
ages and courses
16. Vision 7: 2025, individuals control their own data
16Pic by: Gideon Burton, https://www.flickr.com/photos/wakingtiger/3157622608
• People are aware of the importance
and value of their data.
• Learners control the type and quantity
of personal data that they share, and
with whom they share it
• Most educational institutions run
campaigns to raise awareness of the
risks and exposure of data
17. Summary of the audience
• The most feasible Vision at Serious Request is
Vision Number …
• The most desirable Vision at Serious Request is
Vision Number ...
• The most feasible and desirable Vision at #SR15 is
Vision Number ...
@Hdrachsler, #SR15
18. “The future of Learning Analytics” by Hendrik Drachsler, OUNL was
presented at 3FM Serious Request 2015 in Heerlen, Netherlands
23 December, 2015.
Questions or Comments to:
hendrik.drachsler@ou.nl OR @HDrachsler
This work was undertaken as part of the LACE Project, supported by the European Commission
Seventh Framework Programme, grant 619424.
www.laceproject.eu
@laceproject
18
Editor's Notes
For example, when one competition asked teams to predict whether a student would drop out during the next ten days, based on student interactions with resources on an online course, there were many possible factors to consider. Teams might have looked at how late students turned in their problem sets, or whether they spent any time looking at lecture notes. But instead, MIT News reports, the two most important indicators turned out to be how far ahead of a deadline the student began working on their problem set, and how much time the student spent on the course website. These statistics weren’t directly collected by MIT’s online learning platform, but they could be inferred from data available.
add nice pics
copy and put between slides
add nice pics
copy and put between slides
In 2015, companies were beginning to develop systems to recommend resources and to predict outcomes. By 2025, these systems are highly developed. A wide range of data about learner behaviour is used to generate good quality, real-time predictions about likely success. Learners, teachers, managers and policymakers all have access to live and accurate information about how well a learner is likely to do. Learners and teachers plan their work on the basis of reliable tools that can produce detailed and personalised recommendations about what should be done to achieve the best learning outcomes. A growing industry offers services to institutions and individuals, advising on how to respond to predictions generated by analytics, and how to take appropriate action in the light of recommendations. Accurate predictive information enables managers and policymakers to expand or contract learning provision before success or failure is evident: you don’t have to wait to see if a course is booming or failing, with funding changes happening quickly.
In 2015, learners in educational institutions and in businesses had to follow a curriculum developed by others. In 2025, they create groups that work together to decide their learning goals and how to achieve these. A ‘Learning Trajectory System’ uses analytics to support information exchange and group collaborations, and learners receive support from mentors, rather than teachers. Activity towards a learning goal is monitored, and analytics provide individuals with feedback on their learning process. This includes suggestions, including peer learners to contact, experts to approach, relevant content, and ways of developing and demonstrating new skills. Formative assessment is used to guide future progress, taking into account individuals’ characteristics, experience and context, replacing exams that show only what students have achieved. Texts and other learning materials are adapted to suit the cultural characteristics of learners, revealed by analysis of their interactions As a result, learners are personally engaged with their topics, and are motivated by their highly autonomous learning. The competences that they develop are valuable in a society in which collection and analysis of data are the norm. There is also convergence between the learning activities of the education system and the methods used by employees to develop their knowledge and skills.
In 2015, many people hoped that analytics would be able to improve teaching and learning and the environments where these take place. However, in 2025, it is clear that there are many problems. Courses that are automated by analytics are seen as inferior, and learners have realised that they can game the system. There have been major leaks of sensitive personal data, and it is clear that, even where this has not happened, many companies have misused the data generated by their analytics. Many governments have ruled that individuals are the sole owners of the data they generate. All use of data for educational purposes now has to be approved not only by the learner but also by new inspectorates. In practice this has meant that use of analytics is restricted to summative assessment carried out by government agencies. A consensus has emerged in educational policy that the move away from learning analytics is not only ethically desirable, it is also educationally effective.
In 2015, learning analytics were mainly used to support online learning. By 2025, they can be used to support most teaching and learning activities, wherever these take place. Furniture, pens, writing pads – almost any tool used during learning – can be fitted with sensors. These can record many sorts of information, including tilt, force and position. Video cameras using facial recognition are able to track individuals as they learn. These cameras monitor movements, and record exactly how learners work with and manipulate objects. All this information is used to monitor learners’ progress. Individuals are supported in learning a wide range of physical skills. Teachers are alerted to signs of individual learner’s boredom, confusion, and deviation from task. Teachers and managers are able to monitor social interactions, and to identify where they should nurture socialisation and cooperative behaviour.
In 2015, people were beginning to assemble datasets that could represent learner’s activities. By 2025, these are used on a large scale in teaching, and this has led to the development of enormous datasets containing information about hundreds of thousands of learners. Analysing in detail the progress of such a wide variety of learners has made it possible to provide reliable evidence-based recommendations about the most successful routes to learning, as well as identifying the learning materials and approaches that are most suitable for each individual at each point in their progress. These recommendations are better informed and more reliable than those that can be produced by even the best-trained humans. Learners now spend most of their time working with analytics-driven systems, and the role of teachers has been reduced. Education policy is driven by the evidence generated by the use of these systems.
In 2015, people were beginning to wear devices such as heart-rate monitors and run-trackers as they went about their daily lives. By 2025, sophisticated sensors can gather personal information about factors such as posture, attention, rest, stress, blood sugar, and metabolic rate. People collect this information about their activities, and feed it into programmes of their choice which provide recommendations on how to act in ways that improve their learning. Learners can download the statistics and data that are associated with successful learning in a certain area. Aligning personal data with these ‘ideal’ sets is claimed to help people to master skills as diverse as swimming, driving, carrying out surgery and passing examinations. Academic stars sell programmes using this data to optimise learning for different ages and courses. Business gurus market similar programmes for topics such as presentation skills and workload management. Some learners create and share their own data analysis programmes, which provide recommendations that often include the consumption of high energy foods and stimulants. The majority of high school and university students follow self-monitoring programmes, and avidly discuss the merits of these on social media.
In 2015, it was not clear who owned educational data, and it was often used without learners' knowledge. By 2025, most people are aware of the importance and value of their data. Learners control the type and quantity of personal data that they share, and with whom they share it. This includes information about progress, attendance and exam results, as well as data collected by cameras and sensors. Learners can choose to limit the time for which access is allowed, or they can restrict access to specific organisations and individuals. The tools for making these choices are clearly laid out and easy to use. In the case of children, data decisions are made in consultation with parents or carers. If they do not engage with these tools, then no data is shared and no benefits gained. Most educational institutions recognise this as a potential problem, and run campaigns to raise awareness of the both the risks of thoughtless exposure of data, and the benefits to learners of informed sharing of selected educational data.