The document discusses learning analytics and ways it can benefit students. It defines learning analytics as using data about learners and contexts to understand and optimize learning. While most work focuses on predicting outcomes, this research prioritizes the student perspective. It aims to use machine learning models and an interactive interface to help students visualize and reflect on their learning process and goals. The methodology involves gathering student activity data, qualitative interviews, and developing models for students to engage with their data and improve metacognition.
Increasing the Teacher's Effectiveness Toolboxjoniallison23
Can we change teachers’ attitudes and knowledge in
determining their own educator effectiveness by
looking through the lenses of data literacy, district
benchmarks, and student growth? This session will
include an overview of an action research project on
increasing teachers’ self-efficacy and demonstration of
the Benchmark Dashboard in Home Base.
Increasing the Teacher's Effectiveness Toolboxjoniallison23
Can we change teachers’ attitudes and knowledge in
determining their own educator effectiveness by
looking through the lenses of data literacy, district
benchmarks, and student growth? This session will
include an overview of an action research project on
increasing teachers’ self-efficacy and demonstration of
the Benchmark Dashboard in Home Base.
Keynote SEC2019 Leeds: The power of learning analytics to impact learning and...Bart Rienties
The second keynote will be delivered by Professor Bart Rienties of the Open University who will discuss how the power of learning and teaching can be unharnessed by using learning analytics on Friday, January 11 .
The theme – Learning Spaces – will examine the many arenas in which students can learn and develop, create and collaborate, forge partnerships with communities, cross thresholds or take risks.
Over the course of both days, plenaries, breakout sessions and a panel will also consider sub-themes, such as informal learning spaces and architecture, digital platforms and technology enhanced learning environments.
http://teachingexcellence.leeds.ac.uk/events/keynoted-announced-and-bookings-now-open-for-sec2019/
The Power of Learning Analytics: Is There Still a Need for Educational Research?Bart Rienties
Across the globe many institutions and organisations have high hopes that learning analytics can play a major role in helping their organisations remain fit-for-purpose, flexible, and innovative. A broad goal of learning analytics is to apply the outcomes of analysing data gathered by monitoring and measuring the learning process. Learning analytics applications in education are expected to provide institutions with opportunities to support learner progression, but more importantly provide personalised, rich learning on a large scale. Substantial progress in learning analytics research has been made in the last few years.
Researchers in learning analytics use a range of advanced computational techniques (e.g., Bayesian modelling, cluster analysis, natural language processing, machine learning) for predicting which learners are likely to fail or succeed, and how to provide appropriate support in a flexible and adaptive manner.
In this keynote, I will argue that unless educational researchers at EARLI embrace some of the key principles, methods, and approaches of learning analytics, educational researchers may be left behind. In particular, a main merit of learning analytics is linking large datasets of actual learning processes and outcomes with learning dispositions and learner characteristics. Using evidence-based approaches rapid insights and advancements are developed how learning designs and learning processes can be optimised to maximise the potential of each learner. For example, our recent research with 151 modules and 133K students at the Open University UK indicates that learning design has a strong impact on student behaviour, satisfaction, and performance. Learning analytics can also drive learning in more “traditional”, face-to-face contexts. For example, by measuring emotions, epistemological expressions, and cross-cultural dialogue, social interactions can be effectively supported by innovative dashboards and adaptive
approaches. I aim to unpack the advantages and limitations of learning analytics and how EARLI researchers can embrace such data-driven research approaches
More info at www.bartrienties.nl
Learners self-directing their learning in MOOCs #Ectel2019Inge de Waard
Informal learning in MOOCs is under-investigated. In this presentation we share how adult learners self-direct their learning when engaging in FutureLearn MOOCs. Five areas influence self-directed learning: individual characteristics, technical and media elements, individual & social learning, structuring learning and context. This study also identified two inhibitors or enablers of learning: intrinsic motivation and personal learning goals, where these two factors increase or decrease the dynamics in the five areas of SDL.
Keynote SEC2019 Leeds: The power of learning analytics to impact learning and...Bart Rienties
The second keynote will be delivered by Professor Bart Rienties of the Open University who will discuss how the power of learning and teaching can be unharnessed by using learning analytics on Friday, January 11 .
The theme – Learning Spaces – will examine the many arenas in which students can learn and develop, create and collaborate, forge partnerships with communities, cross thresholds or take risks.
Over the course of both days, plenaries, breakout sessions and a panel will also consider sub-themes, such as informal learning spaces and architecture, digital platforms and technology enhanced learning environments.
http://teachingexcellence.leeds.ac.uk/events/keynoted-announced-and-bookings-now-open-for-sec2019/
The Power of Learning Analytics: Is There Still a Need for Educational Research?Bart Rienties
Across the globe many institutions and organisations have high hopes that learning analytics can play a major role in helping their organisations remain fit-for-purpose, flexible, and innovative. A broad goal of learning analytics is to apply the outcomes of analysing data gathered by monitoring and measuring the learning process. Learning analytics applications in education are expected to provide institutions with opportunities to support learner progression, but more importantly provide personalised, rich learning on a large scale. Substantial progress in learning analytics research has been made in the last few years.
Researchers in learning analytics use a range of advanced computational techniques (e.g., Bayesian modelling, cluster analysis, natural language processing, machine learning) for predicting which learners are likely to fail or succeed, and how to provide appropriate support in a flexible and adaptive manner.
In this keynote, I will argue that unless educational researchers at EARLI embrace some of the key principles, methods, and approaches of learning analytics, educational researchers may be left behind. In particular, a main merit of learning analytics is linking large datasets of actual learning processes and outcomes with learning dispositions and learner characteristics. Using evidence-based approaches rapid insights and advancements are developed how learning designs and learning processes can be optimised to maximise the potential of each learner. For example, our recent research with 151 modules and 133K students at the Open University UK indicates that learning design has a strong impact on student behaviour, satisfaction, and performance. Learning analytics can also drive learning in more “traditional”, face-to-face contexts. For example, by measuring emotions, epistemological expressions, and cross-cultural dialogue, social interactions can be effectively supported by innovative dashboards and adaptive
approaches. I aim to unpack the advantages and limitations of learning analytics and how EARLI researchers can embrace such data-driven research approaches
More info at www.bartrienties.nl
Learners self-directing their learning in MOOCs #Ectel2019Inge de Waard
Informal learning in MOOCs is under-investigated. In this presentation we share how adult learners self-direct their learning when engaging in FutureLearn MOOCs. Five areas influence self-directed learning: individual characteristics, technical and media elements, individual & social learning, structuring learning and context. This study also identified two inhibitors or enablers of learning: intrinsic motivation and personal learning goals, where these two factors increase or decrease the dynamics in the five areas of SDL.
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.
Learning analytics futures: a teaching perspectiveRebecca Ferguson
Talk given by Rebecca Ferguson on 22 November 2018 int Universita Ca'Foscario Venezia at the event Nuovi orizzonti della ricerca pedagogica: evidence-based learning e learning analytics
07 18-13 webinar - sharnell jackson - using data to personalize learningDreamBox Learning
Learning and competency data can be useful tools in assessing a student’s individual learning needs. In this month’s Blended Learning webinar, presenters Sharnell Jackson and Tim Hudson shared best practices for organizing and using student data in order to better meet student needs. They also discussed processes for using and analyzing data at the student, classroom, and district levels.
Dr Margo Greenwood (March 2017) Community- Based Participatory Research: A S...Sightsavers
This presentation was delivered at IAFOR’s Asian Conference on Education and International Development (ACEID) 2017 in Kobe, Japan.
Presentation abstract:
Community-based participatory research (CBPR) in an education context equitably involves teachers, pupils, community members, organisational representatives and researchers, with a commitment to sharing power and resources and drawing on the unique strengths that each partner brings. The aim through this approach is to increase knowledge and understanding of a given phenomenon and integrate the knowledge gained into interventions, policy and social change to improve the health and quality of life of those in the school community. Sightsavers, a disability-focused iNGO, has been implementing a community-based participatory research approach (CBPR) within its education and social inclusion research in the global South. This paper describes the CBPR methodology, how it works within international development, and its impact on Sightsavers interventions in schools. Specific reference will be made to working with teachers as peer researchers – including those with disabilities, training material for peer researchers, CBPR ethical principles, and community analysis of data.
Presentation by Rebecca Ferguson (IET, The Open University, UK) at the Learning Analytics Summer Institute event (LASI Asia) run in Seoul, South Korea, in September 2016. This presentation, on Visions of the Future of learning analytics, is based on work carried out by the European consortium working on the Learning Analytics Community Exchange (LACE) project.
Looking to the future of learning and technology, hopes for the future and sharing experience of teaching online with a Masters of Software Engineering & Database Technologies
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
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.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
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.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Overview on Edible Vaccine: Pros & Cons with Mechanism
Mary Loftus #ILTAEdTech - Ways of Seeing Learning - 2017 v0.6
1. • Ways of Seeing Learning
Learning Analytics for Learners
Mary Loftus Michael Madden
NUI Galway NUI Galway
mary.loftus@nuigalway.ie michael.madden@nuigalway.ie
@marloft
• The authors acknowledge the support of Ireland’s Higher Education Authority through the IT Investment Fund and ComputerDISC in NUI
Galway.
1
3. And today…
• Data, Artificial Intelligence &
Machine Learning are having a
similar effect across society
• This revolution is not only
showing us to ourselves in new
ways – it is shaping how we live.
3
4. Learning Analytics – a Definition
• “Learning analytics is the measurement, collection, analysis and
reporting of data about learners and their contexts, for purposes of
understanding and optimising learning and the environments in
which it occurs”
• Call for Papers of the 1st International Conference on Learning Analytics & Knowledge (LAK 2011)
Learning Sciences
Data Mining
Data Visualization
Psychology 4
5. Central to education’s purpose is “the coming into presence of unique individual beings”
Education “spaces might open up for uniqueness to come into the world”
– Biesta, G. J. J. (2015). Good Education in an Age of Measurement: Ethics, Politics, Democracy. Routledge.
5
6. Learning Analytics – The Story So Far
• Predicting student outcomes - identifying ‘at-risk students’
• Personalisation of student learning
• Multi-modal analytics – analyses of audio, video, location data
• Discourse and writing analytics
• Measuring ‘student engagement’ & disengagement
• Levels of Learning Analytics:
• Teachers
• Course
• Institution
• National
But little work done from
the Student perspective
6
7. If we can make learning more visible,
can we...
• Reduce the need for formal testing and
examinations?
• Do more problem-based learning & assessment?
• Provide more formative feedback for students?
• Model student’s conceptual understanding?
• Support metacognition?
8. Student Vulnerability, Agency, and
Learning Analytics
• Prinsloo & Slade examine how we:
• decrease student vulnerability,
• increase student agency,
• empower students as participants in learning analytics
• moving students from quantified data objects to qualified and
qualifying selves
• “In light of increasing concerns about surveillance,
higher education institutions (HEIs) cannot afford a
simple paternalistic approach to student data”
• Prinsloo & Slade (2016)
8
9. Measuring Gets Results – But Care
Needs to be Taken…
• When we measure and intervene
accordingly, we can clearly see
improvement and impact
• However, the Hawthorne effect is a
kind of "tell-me-what-you-measure-I-
will-tell-you-how-people-react-to-it"
effect
9
10. Data is Political
• We need to take care in our research to ensure fairness and not replicate
societal biases and discrimination
• There is nothing about doing data analysis that is neutral.
What and how data is collected, how the data is cleaned and
stored, what models are constructed, and what questions are
asked—all of this is political.
dana boyd (2017)
11. Unintended Consequences…
• Criminal Justice Systems – discriminating on the basis of race?
• Employment Screening – address, age, gender?
• Advertising – different ads served depending on race? (Sweeney 2013)
• Even if humans are there as a ‘final check’, there is potential for ‘Moral
Crumple Zones (Elish 2016)
Most data analysis makes prejudicial decisions as part of
clustering without having any understanding of the people or
properties that they are using. It’s merely math! But that math—
and the decisions that are determined by it—have serious social
ramifications.
dana boyd (2017)
12. White-Box Algorithms & Transparency
• Students (and citizens) need to be able to ‘see into’ algorithms that impact
their opportunities and quality of life.
The problem with contemporary data analytics is that we’re
often categorizing people without providing human readable
descriptors.
dana boyd (2017)
12
13. “Action can never manifest through a predictable, deterministic series
of consequences, since the subject, by acting, is placed within a
complicated web of relationships which cannot be predicted before
hand. In the same sense, Action is irreversible.”
Hannah Arendt
“For apart from inquiry, apart from the praxis, individuals cannot be
truly human.
Knowledge emerges only through invention and re-invention, through
the restless, impatient, continuing, hopeful inquiry human beings
pursue in the world, with the world, and with each other.”
Paulo Freire
13
14. Research Values & Ethical Grounding
• Grounded in the Student perspective
• Students as owners of their learning data
• Links learning analytics to learning design
• Machine Learning with an emphasis on white-box
modelling & visibility as well as prediction
• Data literacy capacity building for students
• An application to the NUIG Research Ethics Committee
was approved in Feb 2017
14
15. Research Questions
1. Can a Learning Analytics system provide an interface for
students to engage in metacognitive activities around
their own learning, thereby improving individual
learning?
2. Can we retool an existing learning analytics system using
machine learning, modelling and classifiers to provide
this metacognitive interface to students?
3. Can such a system help students visualize, track and
reflect on their own learning and development goals
and help them to improve performance? 15
19. Data Gathering
• Data Gathered so far
• Internet of Things module - 1st Yr
Computing Students activity in Moodle
(Blackboard equivalent)
• Student activity in Github & Trello Connected Learning
Analytics Toolit
Kitto et al (2016)
19
20. Methodology
• Qualitative Research – interviews with students, showing them some
of their data and finding out what data interests them
• Identifying suitable Data Models for showing students their own data
• Next Stages:
• Present models to Students
• Identify Machine Learning & Modelling Enhancements to improve understanding
• Refine models further
• Reflect to students again
• Identify positive effects on student learning experience
20
21. Research Timeline
• Literature
Review
• Research
Questions
Ethical
Approval
• Data Modelling
• Qualitative
Research
Data
Gathering • Share improved
models with
students
• Assess impact
Write up
2017 2018 2019
21
22. Presentation References
• Biesta, G. J. J. (2015). Good Education in an Age of Measurement: Ethics, Politics, Democracy. Routledge.
• boyd, danah. (2017, April 12). Toward Accountability. Retrieved 18 April 2017, from
https://points.datasociety.net/toward-accountability-6096e38878f0
• Bull, S., Ginon, B., Boscolo, C., & Johnson, M. (2016). Introduction of learning visualisations and metacognitive
support in a persuadable open learner model. In Proceedings of the Sixth International Conference on
Learning Analytics & Knowledge (pp. 30–39). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2883853
• Elish, M. C. (2016). Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction (We Robot 2016)
(SSRN Scholarly Paper No. ID 2757236). Rochester, NY: Social Science Research Network. Retrieved from
https://papers.ssrn.com/abstract=2757236
• Millán, E., Loboda, T., & Pérez-de-la-Cruz, J. L. (2010). Bayesian networks for student model engineering.
Computers & Education, 55(4), 1663–1683. https://doi.org/10.1016/j.compedu.2010.07.010
• Sweeney, L. (2013). Discrimination in Online Ad Delivery. Queue, 11(3), 10:10–10:29.
https://doi.org/10.1145/2460276.2460278
• Madden, Michael G. (NUI, Galway), Lyons, William and Kavanagh, Ita (Limerick Institute of Technology).“A
Data-Driven Exploration of Factors Affecting Student Performance in a Third-Level Institution”, Proceedings of
AICS-2008: 19th Irish Conference on Artificial Intelligence and Cognitive Science, Cork, August 2008.
• Other refs in Abstract 22
The title for this talk is taken from the 1972 BBC series by John Berger called Ways of Seeing. I first saw this as a very green Communications Studies 1st year undergrad – and it blew my mind. I had never seen or realised how powerful the media was in shaping our perspectives and how the hidden power structures behind it exerted control over our societies, for better or worse. Berger pointed out how the invention of the printing press and later the camera meant that a work of art and its audience could be in different physical places. There was a separation of the viewer and the viewed.
I want to borrow Berger’s ‘Ways of Seeing’ metaphor and apply it to the field of Learning Analytics – where data about learning can be used to separate the learner from their learning. The choices we make about how to use this new perspective on learning will have a significant impact on the schools and universities of the future.
So, what do we mean by the term: ‘Learning Analytics’
Learning Analytics in this research is considered in the context of third-level education – so a word on what we mean by the term ‘education’
boyd, danah. (2017, April 12). Toward Accountability. Retrieved 18 April 2017, from https://points.datasociety.net/toward-accountability-6096e38878f0