Presentation of the paper by Drachsler & Greller on Confidence in Learning Analytics given at LAK12 conference, April 30th 2012, Vancouver, Canada
Data and survey available at:
http://bit.ly/la_survey
This is an updated version of my presentation at LAK12 that includes related research on TEL RecSys and the new LinkedUp project that will address the current challenges we are facing like the lack of datasets, common evaluation framework and real world examples for LA and data supported education.
3 D Project Based Learning Basics for the New Generation Science Standardsrekharajaseran
This presentation is a part of the workshop presented at Griffin RESA Drive-In STEM Conference on September 28, 2016. It provides an introduction to the basics of three dimensional project based learning for STEM Education and New Generation Science Standards.
This presentation discusses findings on a high school AP Chemistry class constructing a desalination unit through a blended technology approach. This presentation was for the American Educational Research Association (AERA) conference in San Antonio, Texas, April 2017.
Multimodal Learning Analytics for Collaborative Learning Understanding and Su...Sambit Praharaj
This project has multiple focus points: using the help of Multimodal Learning Analytics to understand how co-located collaboration takes place, what are the indicators of collaboration (such as pointing at peer, looking at peer, making
constructive interruptions, etc.); then we try to form a Collaboration Framework (CF)
which defines the aspects of successful collaboration and forms a model. These
insights help us to build the support framework to enable efficient real-time feedback
during a group activity to facilitate collaboration.
Effect of Makerspace Professional Development Activities on Elementary & Midd...STEAM Learning Lab
Dissertation on the Effect of Makerspace Professional Development Activities on Elementary & Middle School Educator Perceptions of Integrating Technologies with STEM (science, technology, engineering, mathematics)
This is an updated version of my presentation at LAK12 that includes related research on TEL RecSys and the new LinkedUp project that will address the current challenges we are facing like the lack of datasets, common evaluation framework and real world examples for LA and data supported education.
3 D Project Based Learning Basics for the New Generation Science Standardsrekharajaseran
This presentation is a part of the workshop presented at Griffin RESA Drive-In STEM Conference on September 28, 2016. It provides an introduction to the basics of three dimensional project based learning for STEM Education and New Generation Science Standards.
This presentation discusses findings on a high school AP Chemistry class constructing a desalination unit through a blended technology approach. This presentation was for the American Educational Research Association (AERA) conference in San Antonio, Texas, April 2017.
Multimodal Learning Analytics for Collaborative Learning Understanding and Su...Sambit Praharaj
This project has multiple focus points: using the help of Multimodal Learning Analytics to understand how co-located collaboration takes place, what are the indicators of collaboration (such as pointing at peer, looking at peer, making
constructive interruptions, etc.); then we try to form a Collaboration Framework (CF)
which defines the aspects of successful collaboration and forms a model. These
insights help us to build the support framework to enable efficient real-time feedback
during a group activity to facilitate collaboration.
Effect of Makerspace Professional Development Activities on Elementary & Midd...STEAM Learning Lab
Dissertation on the Effect of Makerspace Professional Development Activities on Elementary & Middle School Educator Perceptions of Integrating Technologies with STEM (science, technology, engineering, mathematics)
Determining practicing and prospective teachers’ self-efficacy in TPACK in the science domain
Petra Fisser, Joke Voogt, Bart Ormel, Chantal Velthuis & Jo Tondeur, University of Twente, The Netherlands, Edith Stein University of Applied Sciences, The Netherlands, University of Ghent, Belgium
Teachers’ beliefs, practices and attitudes are important for understanding and improving educational processes, because they are closely linked to teachers’ challenges in their daily professional life. Self-efficacy (Bandura, 1977) seems to play a major role in this. In this study we look at teachers' self-efficacy towards the domain of science education and towards technology integration in this domain. Since most students who enter pre-service elementary school training in the Netherlands graduated from secondary school without science-related courses, many lack any foundational science knowledge. This contributes to their (absence of) confidence to teach science, and it also delimits their science-teaching related PCK. In a recent study Fisser, Ormel and Velthuis (submitted) measured teachers' beliefs, attitudes and self-efficacy in relation to science education in primary education, based on a Dutch version of the Science Teaching Efficacy Belief Instrument (STEBI) (Riggs & Enochs, 1990). The results for the pre-service teachers showed that the more pre-service teachers have the opportunity to experience actual teaching in the science domain, the higher the sense of self-efficacy is. Combining science education with technology integration offers even more challenges for teachers. Measuring teachers’ self-efficacy towards technology integration will be done by using a Dutch version of the TPACK survey (Schmidt et al., 2009). This survey will be complimented with the STEBI survey and, because the TPACK survey does not take into account teachers’ beliefs and attitudes towards technology, questions related to the attitude of teachers towards using technology in education will be added. The combined TPACK-STEBI survey will be distributed to Dutch pre-service primary education students and the results will be presented at the SITE symposium.
Development of educational tools that enable large-scale ethical empirical re...Hassan Khosravi
The value of students developing the capacity to make accurate judgements about the quality of their work and that of others has been widely studied and recognised in higher education literature. To date, much of the research and commentary on evaluative judgement has been theoretical in nature, focusing on perceived benefits and proposing strategies seen to hold the potential to foster evaluative judgement. Their efficacy remains largely untested. The rise of educational tools and technologies which generate data on learning activities at an unprecedented scale, alongside insights from the learning analytics and educational data mining communities, provide new opportunities for fostering and supporting empirical research on evaluative judgement. Accordingly, this paper offers a conceptual framework and an instantiation of the framework in the form of an educational tool called RiPPLE for data-driven approaches to investigate the enhancement of evaluative judgement. Two case studies, demonstrating how RiPPLE can foster and support empirical research on evaluative judgement are presented.
This was part of the Doctoral Consortium presentation in the ICMI Conference 2019 at Suzhou, China on 14th October, 2019. Collaboration is an important skill of the 21st century. It can take place in an online (or remote) setting or in a colocated
(or face-to-face) setting. With the large scale adoption
of sensor use, studies on co-located collaboration (CC) has
gained momentum. CC takes place in physical spaces where
the group members share each other’s social and epistemic
space. This involves subtle multimodal interactions such
as gaze, gestures, speech, discourse which are complex in
nature. The aim of this PhD is to detect these interactions
and then use these insights to build an automated real-time
feedback system to facilitate co-located collaboration
Openness in Education: Teacher perspectives through Concept MappingROER4D
Openness in Education: Teacher perspectives through Concept Mapping
Presentation at the 29th AAOU Conference-2015 -30 November - 03 December, 2015, in Kuala Lumpur, Malaysia.
S. P. Karunanayaka, S. Naidu, S. Kugamoorthy, A. Ariyaratne,L.R. Gonsalkorala, T.D.T.L. Dhanapala
Delta Analytics Open Data Science Conference Presentation 2016Sara Hooker
Delta Analytics, a 501(c)3 Bay Area non-profit presented at the open data science conference 2016.
Interested in using your data skills to give back? Delta Analytics is a Bay Area non-profit that provides pro-bono data science to grant recipients all over the world. Eneza Education, a Delta grant recipient, worked with Delta Fellows to understand how over 1 million primary school students in Kenya used pre-smart phone technology to learn outside the classroom. We will share insights about edtech from our work with Eneza, discuss our analysis and partnership process, and suggest some best practices for skill based volunteering.
Talk given by Rebecca Ferguson at the iLife event 'Health, Education and Lifestyle in the Digital Era' organised by Maastricht University at the Bonbonniere, Maastricht on 24 November 2015.
The talk focuses on the 'Visions of the Future' Policy Delphi study carried out by the Learning Analytics Community Exchange (LACE) project.
The study focuses on eight possible visions of the future of learning analytics:
1. In 2025, classrooms monitor the physical environment to support learning and teaching
2. In 2025, personal data tracking supports learning
3. In 2025, analytics are rarely used in education
4. In 2025, individuals control their own data
5. In 2025, open systems for learning analytics are widely adopted
6. In 2025, learning analytics systems are essential tools of educational management
7. In 2025, analytics support self-directed autonomous learning
8. In 2025, most teaching is delegated to computers
More details of the study are available at laceproject.eu
LACE: Learning Analytics Community Exchange (for LASI 2014)Doug Clow
Presentation about the LACE project (Learning Analytics Community Exchange) at LASI2014, the Learning Analytics Summer Institute held at Harvard, on 30 June 2014.
Slides for the plenary feedback session at #cetis14 the Cetis Conference; Building the Digital Institution held at The University of Bolton on the 17th and 18th June 2014.
如何利用社交媒体制造商机 Using social media to find business opportunitiesBelinda Ang
This is the deck for the Mandarin Workshop delivered at the 16th SME Infocomm Commerce Conference in Singapore.
The content is proprietary of and presented by Belinda Ang, thinkBIG Communications. All sources of the statistics are credited within. We appreciate it if you can return the link or credit back to us when you use this.
Thank you.
Determining practicing and prospective teachers’ self-efficacy in TPACK in the science domain
Petra Fisser, Joke Voogt, Bart Ormel, Chantal Velthuis & Jo Tondeur, University of Twente, The Netherlands, Edith Stein University of Applied Sciences, The Netherlands, University of Ghent, Belgium
Teachers’ beliefs, practices and attitudes are important for understanding and improving educational processes, because they are closely linked to teachers’ challenges in their daily professional life. Self-efficacy (Bandura, 1977) seems to play a major role in this. In this study we look at teachers' self-efficacy towards the domain of science education and towards technology integration in this domain. Since most students who enter pre-service elementary school training in the Netherlands graduated from secondary school without science-related courses, many lack any foundational science knowledge. This contributes to their (absence of) confidence to teach science, and it also delimits their science-teaching related PCK. In a recent study Fisser, Ormel and Velthuis (submitted) measured teachers' beliefs, attitudes and self-efficacy in relation to science education in primary education, based on a Dutch version of the Science Teaching Efficacy Belief Instrument (STEBI) (Riggs & Enochs, 1990). The results for the pre-service teachers showed that the more pre-service teachers have the opportunity to experience actual teaching in the science domain, the higher the sense of self-efficacy is. Combining science education with technology integration offers even more challenges for teachers. Measuring teachers’ self-efficacy towards technology integration will be done by using a Dutch version of the TPACK survey (Schmidt et al., 2009). This survey will be complimented with the STEBI survey and, because the TPACK survey does not take into account teachers’ beliefs and attitudes towards technology, questions related to the attitude of teachers towards using technology in education will be added. The combined TPACK-STEBI survey will be distributed to Dutch pre-service primary education students and the results will be presented at the SITE symposium.
Development of educational tools that enable large-scale ethical empirical re...Hassan Khosravi
The value of students developing the capacity to make accurate judgements about the quality of their work and that of others has been widely studied and recognised in higher education literature. To date, much of the research and commentary on evaluative judgement has been theoretical in nature, focusing on perceived benefits and proposing strategies seen to hold the potential to foster evaluative judgement. Their efficacy remains largely untested. The rise of educational tools and technologies which generate data on learning activities at an unprecedented scale, alongside insights from the learning analytics and educational data mining communities, provide new opportunities for fostering and supporting empirical research on evaluative judgement. Accordingly, this paper offers a conceptual framework and an instantiation of the framework in the form of an educational tool called RiPPLE for data-driven approaches to investigate the enhancement of evaluative judgement. Two case studies, demonstrating how RiPPLE can foster and support empirical research on evaluative judgement are presented.
This was part of the Doctoral Consortium presentation in the ICMI Conference 2019 at Suzhou, China on 14th October, 2019. Collaboration is an important skill of the 21st century. It can take place in an online (or remote) setting or in a colocated
(or face-to-face) setting. With the large scale adoption
of sensor use, studies on co-located collaboration (CC) has
gained momentum. CC takes place in physical spaces where
the group members share each other’s social and epistemic
space. This involves subtle multimodal interactions such
as gaze, gestures, speech, discourse which are complex in
nature. The aim of this PhD is to detect these interactions
and then use these insights to build an automated real-time
feedback system to facilitate co-located collaboration
Openness in Education: Teacher perspectives through Concept MappingROER4D
Openness in Education: Teacher perspectives through Concept Mapping
Presentation at the 29th AAOU Conference-2015 -30 November - 03 December, 2015, in Kuala Lumpur, Malaysia.
S. P. Karunanayaka, S. Naidu, S. Kugamoorthy, A. Ariyaratne,L.R. Gonsalkorala, T.D.T.L. Dhanapala
Delta Analytics Open Data Science Conference Presentation 2016Sara Hooker
Delta Analytics, a 501(c)3 Bay Area non-profit presented at the open data science conference 2016.
Interested in using your data skills to give back? Delta Analytics is a Bay Area non-profit that provides pro-bono data science to grant recipients all over the world. Eneza Education, a Delta grant recipient, worked with Delta Fellows to understand how over 1 million primary school students in Kenya used pre-smart phone technology to learn outside the classroom. We will share insights about edtech from our work with Eneza, discuss our analysis and partnership process, and suggest some best practices for skill based volunteering.
Talk given by Rebecca Ferguson at the iLife event 'Health, Education and Lifestyle in the Digital Era' organised by Maastricht University at the Bonbonniere, Maastricht on 24 November 2015.
The talk focuses on the 'Visions of the Future' Policy Delphi study carried out by the Learning Analytics Community Exchange (LACE) project.
The study focuses on eight possible visions of the future of learning analytics:
1. In 2025, classrooms monitor the physical environment to support learning and teaching
2. In 2025, personal data tracking supports learning
3. In 2025, analytics are rarely used in education
4. In 2025, individuals control their own data
5. In 2025, open systems for learning analytics are widely adopted
6. In 2025, learning analytics systems are essential tools of educational management
7. In 2025, analytics support self-directed autonomous learning
8. In 2025, most teaching is delegated to computers
More details of the study are available at laceproject.eu
LACE: Learning Analytics Community Exchange (for LASI 2014)Doug Clow
Presentation about the LACE project (Learning Analytics Community Exchange) at LASI2014, the Learning Analytics Summer Institute held at Harvard, on 30 June 2014.
Slides for the plenary feedback session at #cetis14 the Cetis Conference; Building the Digital Institution held at The University of Bolton on the 17th and 18th June 2014.
如何利用社交媒体制造商机 Using social media to find business opportunitiesBelinda Ang
This is the deck for the Mandarin Workshop delivered at the 16th SME Infocomm Commerce Conference in Singapore.
The content is proprietary of and presented by Belinda Ang, thinkBIG Communications. All sources of the statistics are credited within. We appreciate it if you can return the link or credit back to us when you use this.
Thank you.
Using video online to increase sales slideshareDIYMarketers
If you're not marketing with video, your customer can't find you. By 2013 90% of all web traffic will be through video! Use these simple tips to get started quickly.
Project Opera (Operation Rational): A Tool In Bridging The Learning GAPS In F...AJHSSR Journal
ABSTRACT : The study was conducted to determine the Mathematical performance of the students. The
study aimed to evaluate the effect of project opera in the mathematical performance of the students during pretest and post-test performance in fractions. The study employed the quasi -experimental one group-pre-test-posttest research design. The paired t-test was employed to establish the existence of significant difference between
pre-and post-test scores in fractions.
Type and Use of Innovative Learning Environments in Australasian Schools .Tec...eraser Juan José Calderón
Type and Use of Innovative Learning Environments in Australasian Schools ILETC Survey 1
Wesley Imms, Marian Mahat, Terry Byers & Dan Murphy
• What types of learning environments are in use
in Australian and New Zealand schools?
• What types of teaching approaches happen in these?
• What types of learning do they facilitate?
CSCL Tools for Regulating Collaboration & TeamworkTieLab
Mariel Miller & Allyson Hadwin, University of Victoria
Presented at the 2013 conference for the Canadian Society for the Study of Education (CSSE)
Increasing emphasis on collaboration in academic and work contexts means learners are required to develop skills for regulating teamwork. The purpose of this study was to examine scripting and visualization tools for supporting regulation of shared task perceptions during a complex collaborative task. Prior to the task, groups engaged in individual and group planning using either (a) a scripting tool structuring regulation including task analysis, or (b) a scripting tool augmented with visualization of each member’s task perceptions. Findings indicated that, in both groups, shared task perceptions were generally accurate in relation to the instructor’s expectations. However, groups (a) struggled to construct consensus among diverse individual perceptions, (b) demonstrated little active and purposeful construction of shared task perceptions, and (c) encountered planning related challenges during collaboration. Groups showed small improvements across assignments, however, many difficulties re-occurred.
Keynote Presentation: Implementing learning analytics and learning design at ...Bart Rienties
The University of the Roller Coaster
How can Higher Education function in a world struggling to save itself from climate change, pandemics and war? How can it drive innovation and shape the future as the pace of technological change constantly increases? How can it re-invent itself to respond imaginatively to the new challenges facing humanity?
We are living in an uncertain, unpredictable world with no “back to normal” any more. So, how can we re-imagine higher education when nothing can be taken for granted? What kind of technologies can help universities to adapt? What lessons can we learn from recent successes and failures? What 'best practice' examples point the way into the future? How can we shape the development of institutions, so that they are neither “ivory towers” nor “competence factories"? How can we encourage future-oriented universities in which both pedagogy and research are fit for the challenges ahead?
In the Academic Plenary, our experts will examine the threats and opportunities facing higher education today and ask how we can design new approaches that prepare staff and students to thrive in the University of the Roller Coaster.
Open Science and Ethics studies in SLE researchdavinia.hl
Beardsley, M., Santos, P., Hernández-Leo, D., Michos, K. (2019). Ethics in educational technology research: informing participants in data sharing risks. British Journal of Educational Technology, 50(3), 1019-1034, https://doi.org/10.1111/bjet.12781
Beardsley, M., Hernández-Leo, D., Ramirez, R., (2018) Seeking reproducibility: Assessing a multimodal study of the testing effect. Journal of Computer Assisted Learning, 2018, vol. 34, no 4, p. 378-386.
Hawe dh vic november 2011 school hp (pp tminimizer)sarahportphillip
Critically important whole school health promotion work has to be sustained: Shifting from program thinking to system thinking
Penny Hawe
Population Health Intervention Research Centre
University of Calgary , Canada
www.ucalgary.ca/PHIRC phawe@ucalgary.ca
Tutorial on qualitative approaches to learning analytics given by Rebecca Ferguson of The Open University UK at the Learning Analytics Summer Institute (LASI) run by the Society for Learning Analytics Research (SoLAR) at the University of British Columbia (UBC) in Vancouver, Canada, on 17 June 2019
Multidisciplinarity vs. Multivocality, the case of “Learning Analytics"Nicolas Balacheff
In this communication presented at LAK2013 (Leuven), we consider an analysis of the TeLearn archive, of the Grand Challenges from the STELLAR Network of Excellence, of two Alpine Rendez-Vous 2011 workshops and research conducted in the Productive Multivocality initiative in order to discuss the notions of multidisciplinarity, multivocality and interidisciplinarity. We use this discussion as a springboard for addressing the term “Learning Analytics” and its relation to “Educational Data Mining”. Our goal is to launch a debate pertaining to what extent the different disciplines involved in the TEL community can be integrated on methodological and theoretical levels.
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.
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.
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.
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.
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.
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
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.
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
Presentation given at Serious Request 2015, #SR15, Heerlen.
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Model Attribute Check Company Auto PropertyCeline George
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Embracing GenAI - A Strategic ImperativePeter Windle
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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
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Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
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Confidence in Learning Analytics aka. The Pulse of Learning Analytics
1. Confidence in Learning Analytics
Understandings and Expectations from the Stakeholders
Hendrik Drachsler & Wolfgang Greller
LAK12,Vancouver, Canada, 30th April 2012
1
2. Goals of the Presentation
LA Framework
Survey
Findings
Conclusions
2
3. A view on Learning Analytics
The Learning
Analytics
Framework
3
4. Greller, W., & Drachsler, H., (submitted). Turning Learning into Numbers. Toward a Generic
Framework for Learning Analytics. Journal of Educational Technology & Society.
4
5. Greller, W., & Drachsler, H., (submitted). Turning Learning into Numbers. Toward a Generic
Framework for Learning Analytics. Journal of Educational Technology & Society.
4
6. Greller, W., & Drachsler, H., (submitted). Turning Learning into Numbers. Toward a Generic
Framework for Learning Analytics. Journal of Educational Technology & Society.
4
7. Greller, W., & Drachsler, H., (submitted). Turning Learning into Numbers. Toward a Generic
Framework for Learning Analytics. Journal of Educational Technology & Society.
4
8. Greller, W., & Drachsler, H., (submitted). Turning Learning into Numbers. Toward a Generic
Framework for Learning Analytics. Journal of Educational Technology & Society.
4
9. Greller, W., & Drachsler, H., (submitted). Turning Learning into Numbers. Toward a Generic
Framework for Learning Analytics. Journal of Educational Technology & Society.
4
10. Greller, W., & Drachsler, H., (submitted). Turning Learning into Numbers. Toward a Generic
Framework for Learning Analytics. Journal of Educational Technology & Society.
4
11. Stakeholders vs. LA Framework
Opinions from
the stakeholders
toward the
dimensions of the
LA framework
5
12. Learning Analytics Questionnaire
Extrapolate opinions from
different target groups:
(a) what is the current
understandings and the
expectations on LA
(b) is there a common
understanding of LA
6
13. Learning Analytics Questionnaire
• 4 weeks available
• 156 people after clean up
• 121 people full records
Data and survey available at:
http://bit.ly/la_survey
7
15. Participants - Roles
%
50
44% Teachers
36% Researchers
37.5 L.Designers
26% Managers
25
16%
12.5
0
s s s s
er er er er
Te
ac
h
ea
rch sig
n
na
g Roles
es L .D
e Ma
R
9
18. Stakeholders
(a) who was expected
to benefit the most
from learning analytics
Teachers
Parents
Institutions
Learners
12
19. Stakeholders
(a) who was expected
to benefit the most
from learning analytics
Outcomes:
1. Teachers
2. Learners
3. Institutions
4. Parents
Teachers
Parents
Institutions
Learners
12
20. Stakeholders
(b) how much will
learning analytics
influence bilateral
relationships?
Teachers
Parents
Institutions
13 Learners
21. Stakeholders
(b) how much will
learning analytics
influence bilateral
relationships?
Outcomes:
1. Teacher-student 84%
2. Student-teacher 63%
3. Student-student 46%
4. Teacher-teacher 41%
Teachers
Parents
Institutions
13 Learners
24. Objectives
The importance of 3 generic
objectives:
(a) reflection
(b) prediction
(c) unveil hidden information
15
25. Objectives
In which way learning analytics will change educational
practice in particular areas?
n = 119
11% no changes at all
43% small changes
45% extensive changes
16
26. Objectives
In which way learning analytics will change educational
practice in particular areas?
Item=2: Timely information
n 119
about learning
11% no changes at all
43% small changes
Item 8: extensive changes
45% Better insights by
institutions in their courses
Item 5: Easier grading
Item 6: Objective assessment
16
27. Educational Data
Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for
Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder
Systems in Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28,
2010, Barcelona, Spain.
17
28. Educational Data
Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for
Verbert, K., Manouselis, in Technology Enhanced Learning.E. (accepted).Recommnder
Recommender Systems N., Drachsler, H., and Duval, 1st Workshop Dataset-driven
Systems in Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28,
Research to Support Learning and Knowledge Analytics. Journal of Educational Technology
2010, Barcelona, Spain.
& Society. 17
29. Educational Data
Researcher:
1. Added context information
(n=43, means 3.42)
Teacher:
1. Added context information
(n=52, means 3.42)
Manager:
1. Sharing within the institution
(n=16, means 3.63)
2. Anonymisation
(n=19, means 3.53)
18
32. Technologies
Reflection
Peter Kraker, Claudia
Wagner, Fleur
Jeanquartier, Stefanie
N. Lindstaedt (2011):
On the Way to a
Science Intelligence:
Visualizing TEL Tweets
for Trend Detection
Sixth European
Conference on
Technology Enhanced
Learning (EC-TEL 2011)
Manouselis, N., Drachsler, H., Verbert, K., and Duval, E. (to appear). Recommender
Systems for Learning. Berlin:Springer
19
33. Technologies
Trust in accurate and appropriate LA results ...
1.View on learning progress
2. Predict learning resource
3. Assessment
4.View on engagement
5. Compare learners
6. Prediction of peers
7. Prediction of learner
performance
20
42. Limitations
1. Dominance of responses
from the HE sector
2. Absence of students
3. Low awareness of LA;
Only surveyed innovators and early adopters
4. Mainly opinions from western cultures
26
44. + Stakeholders
1.Main beneficiaries of LA are learners
and teachers followed by organisations
2.Biggest benefits would be gained in the
teacher-to-student relationship
3.Learners require teacher support to
learn from LA
28
45. + Stakeholders
1.Main beneficiaries of LA are learners
and teachers followed by organisations
2.Biggest benefits would be gained in the
teacher-to-student relationship
LA asto
3.Learners require teacher support
tio n?
learn from LA
i nnova
28
46. + Objectives
1. Reflection support is main objective from
the stakeholders view ...
2. ...by revealing hidden information about
learners
29
47. + Objectives
1. Reflection support is main objective from
the stakeholders view ...
2. ...by revealing hidden information about
learners
Reflection support only for
teacher student relationship?
29
48. + Educational Data
1. Context information from learners and the learning
process
2. Anonymisation is the second most important data
attribute
3. Willingness to share if data is anonymised
30
49. + Educational Data
1. Context information from learners and the learning
process
2. Anonymisation is the second most important data
attribute
3. Willingness to share if data is anonymised
Can we achieve a collection of
reference datasets?
30
50. + Technologies
1. Trust in LA algorithms is not well developed
2. High confidence on gaining a comprehensive
view of the learning progress
31
51. + Technologies
1. Trust in LA algorithms is not well developed
2. High confidence on gaining a comprehensive
view of the learning progress
How accurate can we measure
a learning progress?
31
52. + Constraints
1. Data ownership is the most important topic
2. LA lead to breaches of privacy but privacy and ethical aspects
are of lesser importance
3. Many organisations have ethical boards and guidelines in place
32
53. + Constraints
1. Data ownership is the most important topic
2. LA lead to breaches of privacy but privacy and ethical aspects
are of lesser importance
3. Many organisations have ethical boards and guidelines in place
Do we need new policies for
data ownership and privacy?
32
54. + Competences
1. Skepticism that LA will lead to more independence of
learners to control their learning process
2. Training need to guide students to more self-directedness
and critical reflection
33
55. + Competences
1. Skepticism that LA will lead to more independence of
learners to control their learning process
2. Training need to guide students to more self-directedness
and critical reflection
Do we need mandatory courses on
statistics for the edu. sector?
33
56. Future R&D
Future
R&D
picture by Tom Raftery http://www.flickr.com/photos/traftery/4773457853
34
57. Future R&D
1. Encourage the LA community
to gain additional insights from
our dataset: http://bit.ly/la_survey
2. Look for partners to survey
public bodies like school
foundations and governmental
institutions
3. Compare LA in different
educational cultures and extend
survey to eastern countries
35
58. Future R&D
Group Concept
Mapping
to identify
consensus about
particular issues
in learning
analytics
!
36
59. Many Thanks::Questions?
This presentation is available at:
slideshare.com/Drachsler
Email: hendrik.drachsler@ou.nl
Email: wolfgang.greller@ou.nl
Supporting projects:
37