If we could observe all the events in a learning environment, even the most hidden ones, what kind of interventions would become feasible? The presentation finishes with a workshop to manipulate a dataset with R.
Why and how to use data to form compelling storytelling. Hands-on with basic tools of data analysis and visualisation (MS Excel and Tableau Software). A first version of this presentation was instructed on Wednesday, December 14th, 2016, during Panteion University's Communication, Media and Culture ADandPRLab series of workshops on digital skills.
How can universities scale up learning analytics beyond small-scale pilots to seriously use data to improve student learning? This interactive workshop was designed to help you think this through for your institution.
Universities are hard to change. Having good data and analytics is a good start, but is only one part of success. This session will provide tools and frameworks to help you analyse what else is needed, building on experiences of successful large-scale learning analytics activity at the Open University and the University of Technology, Sydney, and from the pan-European Learning Analytics Community Exchange project.
Slides for a talk at Bett, London, 20 January 2016.
Let me intervene. . Influencing a learning environment through analyticsAbelardo Pardo
Presentation given at the 8th JTEL Summer School held in May 2012 in Estoril. The hands-on workshop presented a description of Learning Analytics and then participants loaded two data sets into a statistical tool and manipulate them to deduce potential interventions
Why and how to use data to form compelling storytelling. Hands-on with basic tools of data analysis and visualisation (MS Excel and Tableau Software). A first version of this presentation was instructed on Wednesday, December 14th, 2016, during Panteion University's Communication, Media and Culture ADandPRLab series of workshops on digital skills.
How can universities scale up learning analytics beyond small-scale pilots to seriously use data to improve student learning? This interactive workshop was designed to help you think this through for your institution.
Universities are hard to change. Having good data and analytics is a good start, but is only one part of success. This session will provide tools and frameworks to help you analyse what else is needed, building on experiences of successful large-scale learning analytics activity at the Open University and the University of Technology, Sydney, and from the pan-European Learning Analytics Community Exchange project.
Slides for a talk at Bett, London, 20 January 2016.
Let me intervene. . Influencing a learning environment through analyticsAbelardo Pardo
Presentation given at the 8th JTEL Summer School held in May 2012 in Estoril. The hands-on workshop presented a description of Learning Analytics and then participants loaded two data sets into a statistical tool and manipulate them to deduce potential interventions
Practical privacy issues around Learning AnalyticsAbelardo Pardo
The controversy around privacy and security in ICT seems to be a never-ending source of newspaper headlines. Cases of security and privacy breaches are interspersed with an equal number of announcements of new legislative measures or amendments. Learning analytics is certainly not the first technological area to deal with these issues, but more often than not, the debate on privacy tends to prevent the design of practical solutions. Far from trying to
settle the main controversy around privacy, in this talk we will try to identify the major areas in which privacy concerns can be divided and provide some examples of practical solutions.
Flipping the classroom proposes moving activities to be done by the students before the class. But how do you know how engaged were they with them? Learning Analytics can help, and in this talk we present som simple idea to know.
Pushing the MOOC envelope with Learning AnalyticsAbelardo Pardo
How can Learning Analytics be used to bring about the true revolution traditionally assumed for MOOCs? With audiences in the thousands of users, the key is massive personalization, and Learning Analytics privide an ideal paradigm for this.
Analytics to understand learning environmentsAbelardo Pardo
Seminar for the CHAI Group at The University of Sydney. A summary of the initiatives I have worked on in the past years plus a brief account of my current work.
Using OnTask for Student Coaching in Large Student CohortsAbelardo Pardo
The provision of student feedback is a challenging and resource intensive
task for any instructor but at the same time it has the potential of
significantly improve the overall quality of a learning experience.
This challenge is magnified even further in the context of large student
cohorts. Current initiatives such as the one captured by the OnTask project
have explored how to use data about student engagement to support instructors
of large student cohorts in this process. But despite the use of technology
there are still important aspects to consider. What is the ideal tone of the
message? Should they focus on the material? Assessments? Strategies? How
often is idea to send these messages? In this talk we will cover some
principles and examples of how instructors are addressing the problem.
Using data to provide personalised feedback at scaleAbelardo Pardo
The current state of higher education has increasing pressure over academics to offer high quality experience at scale. But what could be the actions that can be deployed to achieve this increase? What would be a good guiding principle to decide these actions? In this talk we explore first the possibility of using feedback and a coach mentality to provide student support, and then how data can help us scale that technique. There are examples of potential scenarios to deploy this at the level of a course, program or overall student experience.
Facilitating feedback processes at scale through personalised support actionsAbelardo Pardo
As education keeps advancing into the era of ubiquitous data availability there are certain challenges that are also increasing. The connection between data and direct improvements or benefit for students in terms of the overall quality of the learning experience is still an area under significant evolution. Learning analytics promises the use of data to improve learning experiences, but bridging the distance between widespread data availability and meaningful, effective and relevant actions informed by this data is still important. The current focus when considering the use of data tends to gravitate towards institutional interventions that target only a subset of the students (e.g. those at risk of dropping a course or abandoning the institution). But the student experience is much more complex and varied.
In this talk we will describe OnTask, a platform and approach to facilitate the connection between data and actions in the context of a learning experience. The framework used by the tool contains a generic architecture to simplify the combination of multiple data sources under a single data structure with an intuitive design of rule-based personalized support actions that can be scaled to large student cohorts. OnTask approaches the problem from the benefits of feedback processes that rely on a conversation between students and instructors at the level of a course.
Providing personalised student support in blended learning at scaleAbelardo Pardo
Blended learning environments can be used to deploy strategies to increase student engagement in learning experiences. However, for these strategies to be effective, this increase in engagement requires an increase in student support which can pose serious challenges for large cohorts. The increase in technology mediation offers unprecedented opportunities to collect information
about how students interact in a learning environment. Can this data be used to provide student support at scale? Is it feasible to blend data management techniques as part of a learning design to provide personalised suggestions to students? This talk will offer various practical examples of personalised
student support actions in the context of a large flipped classroom.
Designing Engaging Learning Experiences in Digital EnvironmentsAbelardo Pardo
Talk about how to address the design of learning experiences in the current digital environments and how to take into account the student perspective, motivation, feedback, and other various aspects.
Provision of personalized feedback at scale using learning analyticsAbelardo Pardo
The increasing presence of technology mediation offers an unprecedented opportunity to use detailed data sets about the interactions that occur while a learning experience is being enacted. Areas such as Learning Analytics or Educational Data Mining have explored numerous algorithms and techniques to process these data sets. Additionally, technology now offers the opportunity to increase the immediacy of interventions. However, not much emphasis has been placed on how to extract truly actionable knowledge and how to bring it effectively as part of a learning experience. In this talk, we will use the concept of feedback as the focus to establish a specific connection between the knowledge derived from data-analysis procedures and the actions that can be immediately deployed in a learning environment. We will discuss how there is a trade-off between low-level automatic feedback and high-level complex feedback and how technology can provide efficient solutions for the case of large or highly diverse cohorts.
Articulating the connection between Learning Design and Learning AnalyticsAbelardo Pardo
Learning analytics is a discipline that uses data captured by technology during a learning experience to increase our level of understanding, increase its quality, and improve the environment in which it occurs. But these experiences need to be designed first. In this talk we start from the statement that there is no such thing as a neutral design. In the era of increasing technology mediation Learning experiences need to be designed considering the capacity to capture data, the possibility of making sense and derive knowledge from the data, and the need to act on that knowledge. In this talk we will explore some initiatives to make these connections explicit in a learning design. Using a flipped learning experience, we will explore how to embed data and data analysis as part of the design tasks.
The role of data in the provision of feedback at scaleAbelardo Pardo
Technology mediation allows to capture comprehensive data sets about interactions occurring in learning experiences. Although these data sets have the potential of increasing the insight on how learning occurs, their use strongly depends on two aspects: the data has to be properly situated in the learning design, and the insights derived need to be translated into actions. In this talk we will explore how to establish this connection for the case of the provision of feedback. We will approach the problem from the point of view of intelligence amplification, that is, how data can support instructors to provide better support to learners through feedback. The talk will discuss some preliminary results from the Ontasklearning.org project.
Increasing student engagement has been one of the main focus to improve the quality of a learning experience. In this talk we cover two aspects that can contribute to this increase: flipped learning, and feedback.
The role of data in the provision of feedback at scaleAbelardo Pardo
The abundance of data in learning environments poses both a potential and a challenge. Improvements in the student experience need a strong connection between data, learning design and the delivery platform. In this talk we explore some ideas on how to establish this connection with respect to feedback.
Exploring hands-on multidisciplinary STEM with Arduino EsploraAbelardo Pardo
In this presentation we describe the Madmaker project. The use of Arduino Esplora to promote STEM activities in High Schools. It contains a description of our approach and data derived from the evaluation.
One of the objectives of the recently created Faculty of Engineering and IT Education Innovation unit is to promote "sustained innovation" in engineering educaiton. Innovation is a word that gets thrown around quite frequently and it is assumed we all know what it means. In recent times the term appears in more complex expressions such as "sustained innovation" or
"culture of innovation". Organisations in general are facing challenges to go from stating the intent of adopting a culture of innovation and actually achieving it. Engineering and IT education is no exception. In fact, there are recent studies that point to the disparity of perception among academics about
what exactly means innovation in the context of learning and teaching engineering and IT disciplines. In this session we will discuss several elements that need to be present for innovation to occur and collaboratively distil some conditions that would provide the right climate so that learning and teaching innovation flourishes in the faculty.
The role of institutional data in Learning AnalyticsAbelardo Pardo
Learning analytics has the potential of improving how higher education institutions operate. A significant portion of this potential derives from the use of institutional data. In this talk we review the role of these units in achieving institutional capacity and show some examples of the type of solutions possible at the level of instructors.
Feedback at scale with a little help of my algorithmsAbelardo Pardo
Talk exploring how to use data to provide scalable feedback in learning experiences. The solutions explored propose the use of algorithms to enhance how humans instructors provide feedback to students more effectively
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Practical privacy issues around Learning AnalyticsAbelardo Pardo
The controversy around privacy and security in ICT seems to be a never-ending source of newspaper headlines. Cases of security and privacy breaches are interspersed with an equal number of announcements of new legislative measures or amendments. Learning analytics is certainly not the first technological area to deal with these issues, but more often than not, the debate on privacy tends to prevent the design of practical solutions. Far from trying to
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Pushing the MOOC envelope with Learning AnalyticsAbelardo Pardo
How can Learning Analytics be used to bring about the true revolution traditionally assumed for MOOCs? With audiences in the thousands of users, the key is massive personalization, and Learning Analytics privide an ideal paradigm for this.
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The provision of student feedback is a challenging and resource intensive
task for any instructor but at the same time it has the potential of
significantly improve the overall quality of a learning experience.
This challenge is magnified even further in the context of large student
cohorts. Current initiatives such as the one captured by the OnTask project
have explored how to use data about student engagement to support instructors
of large student cohorts in this process. But despite the use of technology
there are still important aspects to consider. What is the ideal tone of the
message? Should they focus on the material? Assessments? Strategies? How
often is idea to send these messages? In this talk we will cover some
principles and examples of how instructors are addressing the problem.
Using data to provide personalised feedback at scaleAbelardo Pardo
The current state of higher education has increasing pressure over academics to offer high quality experience at scale. But what could be the actions that can be deployed to achieve this increase? What would be a good guiding principle to decide these actions? In this talk we explore first the possibility of using feedback and a coach mentality to provide student support, and then how data can help us scale that technique. There are examples of potential scenarios to deploy this at the level of a course, program or overall student experience.
Facilitating feedback processes at scale through personalised support actionsAbelardo Pardo
As education keeps advancing into the era of ubiquitous data availability there are certain challenges that are also increasing. The connection between data and direct improvements or benefit for students in terms of the overall quality of the learning experience is still an area under significant evolution. Learning analytics promises the use of data to improve learning experiences, but bridging the distance between widespread data availability and meaningful, effective and relevant actions informed by this data is still important. The current focus when considering the use of data tends to gravitate towards institutional interventions that target only a subset of the students (e.g. those at risk of dropping a course or abandoning the institution). But the student experience is much more complex and varied.
In this talk we will describe OnTask, a platform and approach to facilitate the connection between data and actions in the context of a learning experience. The framework used by the tool contains a generic architecture to simplify the combination of multiple data sources under a single data structure with an intuitive design of rule-based personalized support actions that can be scaled to large student cohorts. OnTask approaches the problem from the benefits of feedback processes that rely on a conversation between students and instructors at the level of a course.
Providing personalised student support in blended learning at scaleAbelardo Pardo
Blended learning environments can be used to deploy strategies to increase student engagement in learning experiences. However, for these strategies to be effective, this increase in engagement requires an increase in student support which can pose serious challenges for large cohorts. The increase in technology mediation offers unprecedented opportunities to collect information
about how students interact in a learning environment. Can this data be used to provide student support at scale? Is it feasible to blend data management techniques as part of a learning design to provide personalised suggestions to students? This talk will offer various practical examples of personalised
student support actions in the context of a large flipped classroom.
Designing Engaging Learning Experiences in Digital EnvironmentsAbelardo Pardo
Talk about how to address the design of learning experiences in the current digital environments and how to take into account the student perspective, motivation, feedback, and other various aspects.
Provision of personalized feedback at scale using learning analyticsAbelardo Pardo
The increasing presence of technology mediation offers an unprecedented opportunity to use detailed data sets about the interactions that occur while a learning experience is being enacted. Areas such as Learning Analytics or Educational Data Mining have explored numerous algorithms and techniques to process these data sets. Additionally, technology now offers the opportunity to increase the immediacy of interventions. However, not much emphasis has been placed on how to extract truly actionable knowledge and how to bring it effectively as part of a learning experience. In this talk, we will use the concept of feedback as the focus to establish a specific connection between the knowledge derived from data-analysis procedures and the actions that can be immediately deployed in a learning environment. We will discuss how there is a trade-off between low-level automatic feedback and high-level complex feedback and how technology can provide efficient solutions for the case of large or highly diverse cohorts.
Articulating the connection between Learning Design and Learning AnalyticsAbelardo Pardo
Learning analytics is a discipline that uses data captured by technology during a learning experience to increase our level of understanding, increase its quality, and improve the environment in which it occurs. But these experiences need to be designed first. In this talk we start from the statement that there is no such thing as a neutral design. In the era of increasing technology mediation Learning experiences need to be designed considering the capacity to capture data, the possibility of making sense and derive knowledge from the data, and the need to act on that knowledge. In this talk we will explore some initiatives to make these connections explicit in a learning design. Using a flipped learning experience, we will explore how to embed data and data analysis as part of the design tasks.
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Technology mediation allows to capture comprehensive data sets about interactions occurring in learning experiences. Although these data sets have the potential of increasing the insight on how learning occurs, their use strongly depends on two aspects: the data has to be properly situated in the learning design, and the insights derived need to be translated into actions. In this talk we will explore how to establish this connection for the case of the provision of feedback. We will approach the problem from the point of view of intelligence amplification, that is, how data can support instructors to provide better support to learners through feedback. The talk will discuss some preliminary results from the Ontasklearning.org project.
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The abundance of data in learning environments poses both a potential and a challenge. Improvements in the student experience need a strong connection between data, learning design and the delivery platform. In this talk we explore some ideas on how to establish this connection with respect to feedback.
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What if we could observe all events in a learning environment?
1. Auntie p flickr.com
What if we could observe
all events in a
learning environment?
MUI-TIC, Valladolid, April 2012
Abelardo Pardo
www.slideshare.net/abelardo_pardo
@abelardopardo
2. libraryman flickr.com
Journey to 10 years from now
Abelardo Pardo What if we could observe all events in a learning environment? 2
3. ribarnica flickr.com
Analytics
today
Abelardo Pardo What if we could observe all events in a learning environment? 4
10. Reigh LeBlanc flickr.com
Medical
records
Abelardo Pardo What if we could observe all events in a learning environment? 11
11. (Campbell, De Blois, Oblinger 2007)
C. Elle flickr.com
Refine
Collect
Act The five steps
Report
of analytics
Predict
Abelardo Pardo What if we could observe all events in a learning environment? 12
12. ribarnica flickr.com
Analytics
today
Potential
in
learning
Abelardo Pardo What if we could observe all events in a learning environment? 13
13. Shaylor flickr.com
Did they lea
rn?
Abelardo Pardo What if we could observe all events in a learning environment? 14
15. Biking Nikon SFO flickr.com
If you can not measure it,
you can not improve it.
Abelardo Pardo What if we could observe all events in a learning environment? 16
16. bencrowe flickr.com
“We need better evaluation
to acknowledge and esteem
excellence when it occurs,
as it does.”
John Hattie. Visible Learning: A Synthesis of Over 800
Meta-Analyses Relating to Achievement.
Abelardo Pardo What if we could observe all events in a learning environment? 17
17. bodhithaj flickr.com
Learning analytics improve
the feedback loop
Abelardo Pardo What if we could observe all events in a learning environment? 18
18. (Jones, 2011)
bodhithaj flickr.com
Administration Students
Teachers Researchers
Abelardo Pardo What if we could observe all events in a learning environment? 19
19. Marketingfacts flickr.com
Where is learning analytics?
Abelardo Pardo What if we could observe all events in a learning environment? 20
20. PranceyDog flickr.com
LA as vertical market
Specialized objectives
Specialized audiences
Specialized topic
Abelardo Pardo What if we could observe all events in a learning environment? 21
21. Abelardo Pardo What if we could observe all events in a learning environment? 22
22. ribarnica flickr.com
Analytics
today
Potential
in
learning
Challenges
Abelardo Pardo What if we could observe all events in a learning environment? 23
23. ljubar flickr.com
More detailed observations
Keep the tutor in the loop
Abelardo Pardo What if we could observe all events in a learning environment? 24
24. Pepe Ortuño flickr.com
A clearly identified
environment within
your computer
Abelardo Pardo What if we could observe all events in a learning environment? 25
25. Sean Dreilinger flickr.com
Automatic discovery
of complementary
learning resources
Abelardo Pardo What if we could observe all events in a learning environment? 26
26. IAN RANSLEY DESIGN + ILLUSTRATION flickr.com
Measure engagement in
out-of-class activities
Abelardo Pardo What if we could observe all events in a learning environment? 27
27. Just in time teaching
Abelardo Pardo What if we could observe all events in a learning environment? 28
37. Dru! flickr.com
Interventions!
Data set
Scenario manipulation
Abelardo Pardo What if we could observe all events in a learning environment? 38
38. net_efekt flickr.com
• 15 weeks. Sessions X, F 12-13:30
• 100 students
• 7 score tests per student
• Course notes with 30 “nodes” published
gradually
• 5 events: forum_view, forum_post,
visit_node, use_tool_1, use_tool_2
Make it your own course!
Abelardo Pardo What if we could observe all events in a learning environment? 39
39. jovike flickr.com
Events (events.csv.gz): around 81,000 x 4 matrix
datetime,event_type,user,payload
2011-09-05 15:26:01,use_tool_1,7a869b4d9,block_1
...
Scores (scores.csv.gz): 100 x 7 matrix
user,score_1,score_2,score_3,score_4,...,score_7
7a869b4d9, 6.4, 9.0, 7.9, 7.6, 5.1, 5.5, 6.7
...
Abelardo Pardo What if we could observe all events in a learning environment? 40
41. cesarastudillo flickr.com
Analyze, propose, discuss
Abelardo Pardo What if we could observe all events in a learning environment? 42
42. What did you observe?
theunquietlibrarian flickr.com
1
2 Which data is missing?
3 What would you do?
4 What would the system do?
Which challenge for LA do you envision?
Abelardo Pardo What if we could observe all events in a learning environment? 43
43. paral_lax flickr.com
script.R
1 Load data into “data frames”
2 Show data summaries
3 Change type of column in data frame
4 View sample histogram
5 Add a column with the mean scores
6 Plot scores for one student
7 Separate events into types
8 Merge event counts with scores
Select command + CRTL-Return (Execute)
Abelardo Pardo What if we could observe all events in a learning environment? 44
44. Calsidyrose flickr.com
R cheat sheet
• Data frame: heterogeneous matrix,
“events.csv”
• Data frame column: events.csv$event_type
• Arrays: c(’one’, ’two’, ’three’), c(1, 2, 3),
c(2:8)
• Sequences: 1:5, seq(1, 6)
• Graphics: barplot(), boxplot(), hist(), plot()
• Functions: length(), rownames(),
colnames(), mean()
• cor() (correlation), var() (variance)
• mean(), median(), min(), max(), sum()
Abelardo Pardo What if we could observe all events in a learning environment? 45
45. Calsidyrose flickr.com
R tests
• cor.test(v1, v2)
• wilcox.test(v1, v2)
• shapiro.test(v1)
R help
• help(any function name)
• Various cheatsheets included in workspace
(cheatsheet_*.pdf)
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46. References
The personal analytics of my life
Stephen Wolfram
http://blog.stephenwolfram.com/2012/03/the-personal-analytics-of-my-life/
How waiters read your table
Sara Nassauer
The Wall Street Journal, Feb. 22nd 2012
The illusion of privacy (and what we actually care about)
Seth Godin
http://sethgodin.typepad.com/seths_blog/2012/02/the-illusion-of-privacy-and-what-we-actually-care-about.html
Academic Analytics
Campbell J., DeBlois P., Oblinger D.
EDUCAUSE White paper, 2007
Visible learning: A synthesis of over 800 meta-analyses related to achievement
Hattie, J. A. C.
Routledge, New York 2009
Learning analytics: Starvation and telling us what we already know?
David Jones
http://davidtjones.wordpress.com/2012/04/01/learning-analytics-starvation-and-telling-us-what-we-already-know/
Abelardo Pardo What if we could observe all events in a learning environment? 47