Learning analytics and personalisation: Observe, think act

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A review of different ideas that fit into the generic framework of observing an environment, thinking about it and acting based on these observations.

Educación

Julien Carnot ﬂickr.com
Learning analytics and personalisation
Observe, Think, Act

LATTE Retreat
Sydney, November 23 2012

Abelardo Pardo (@abelardopardo)
www.slideshare.net/abelardo_pardo

Ribarnica ﬂickr.com
Observe

Think

Act

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 2

sjdunphy ﬂickr.com
Repeat in a feedback loop

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 3

Tulane Public Relations ﬂickr.com
Laparoscopic surgery

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 4

Nick_r ﬂickr.com
>300 Km/h

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 5

jbgeronimi ﬂickr.com
Fly by wire

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 6

Shaylor ﬂickr.com
Did they lea
r n?

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 7

libraryman ﬂickr.com
Journey to 10 years from now

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 8

Ribarnica ﬂickr.com
Observe

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 9

http://blog.stephenwolfram.com/2012/03/the-personal-analytics-of-my-life/
Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 10

heydrienne ﬂickr.com
Which variables to sample?

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 11

xJasonRogersx ﬂickr.com
You liked it, you payed for it!

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 12

Slaunger ﬂickr.com
Activity in LMS is only the tip

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 13

pvanees ﬂickr.com
Are you in the
LMS?

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 14

Pepe Ortuño ﬂickr.com
A clearly identiﬁed
environment within
your computer

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 15

qmnonic ﬂickr.com
Observe while they learn

Procedural activities

Collection of related events

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 16

orangebrompton ﬂickr.com
Fully conﬁgured

Partial observation

Only while you learn

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 17

Ribarnica ﬂickr.com
Observe

Think

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 18

Sean Dreilinger ﬂickr.com
Automatic discovery
of complementary
learning resources

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 19

IAN RANSLEY DESIGN + ILLUSTRATION ﬂickr.com
Measure engagement in
out-of-class activities

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 20

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 21

Just in time teaching

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 22

Browser add-in

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 23

Quick intuitive
feedback

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 24

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 25

Learning Glass (WS)

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 26

jfravel ﬂickr.com
Team dynamics.
Emotions?

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 27

Mood
changes

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 28

Ribarnica ﬂickr.com
Observe

Think

Act

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 29

VanessaO ﬂickr.com
Closer to the user

Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 30

Link Prediction Algorithms
Sean MacEntee ﬂickr.com

Graph: nodes = entities, edges = relationships

Similar nodes, similar connections
Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 31

Document Recommendation

Recommend D2 to U3 and U4
Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 32

Action Recommendation

Recommend Thread to U3
Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 33

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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.

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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.

Using data to provide personalised feedback at scale

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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.

Facilitating feedback processes at scale through personalised support actions

Abelardo 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.

Providing personalised student support in blended learning at scale

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Designing Engaging Learning Experiences in Digital Environments

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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.

Provision of personalized feedback at scale using learning analytics

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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.

Articulating the connection between Learning Design and Learning Analytics

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Increasing Student Engagement with Personalised Feedback

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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.

The role of data in the provision of feedback at scale

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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.

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Using OnTask for Student Coaching in Large Student Cohorts

Using data to provide personalised feedback at scale

Facilitating feedback processes at scale through personalised support actions

Providing personalised student support in blended learning at scale

Designing Engaging Learning Experiences in Digital Environments

Provision of personalized feedback at scale using learning analytics

Articulating the connection between Learning Design and Learning Analytics

Increasing Student Engagement with Personalised Feedback

The role of data in the provision of feedback at scale

Designing Effective Flipped Learning Experiences

The role of data in the provision of feedback at scale

Exploring hands-on multidisciplinary STEM with Arduino Esplora

What is sustained innovation anyway?

The role of institutional data in Learning Analytics

Feedback at scale with a little help of my algorithms

Generating Actionable Predictive Models of Academic Performance

Exploring the relation between Self-regulation Online Activities, and Academi...

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Learning analytics and personalisation: Observe, think act

1. Julien Carnot ﬂickr.com Learning analytics and personalisation Observe, Think, Act LATTE Retreat Sydney, November 23 2012 Abelardo Pardo (@abelardopardo) www.slideshare.net/abelardo_pardo

2. Ribarnica ﬂickr.com Observe Think Act Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 2

3. sjdunphy ﬂickr.com Repeat in a feedback loop Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 3

4. Tulane Public Relations ﬂickr.com Laparoscopic surgery Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 4

5. Nick_r ﬂickr.com >300 Km/h Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 5

6. jbgeronimi ﬂickr.com Fly by wire Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 6

7. Shaylor ﬂickr.com Did they lea r n? Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 7

8. libraryman ﬂickr.com Journey to 10 years from now Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 8

9. Ribarnica ﬂickr.com Observe Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 9

10. http://blog.stephenwolfram.com/2012/03/the-personal-analytics-of-my-life/ Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 10

11. heydrienne ﬂickr.com Which variables to sample? Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 11

12. xJasonRogersx ﬂickr.com You liked it, you payed for it! Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 12

13. Slaunger ﬂickr.com Activity in LMS is only the tip Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 13

14. pvanees ﬂickr.com Are you in the LMS? Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 14

15. Pepe Ortuño ﬂickr.com A clearly identiﬁed environment within your computer Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 15

16. qmnonic ﬂickr.com Observe while they learn Procedural activities Collection of related events Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 16

17. orangebrompton ﬂickr.com Fully conﬁgured Partial observation Only while you learn Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 17

18. Ribarnica ﬂickr.com Observe Think Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 18

19. Sean Dreilinger ﬂickr.com Automatic discovery of complementary learning resources Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 19

20. IAN RANSLEY DESIGN + ILLUSTRATION ﬂickr.com Measure engagement in out-of-class activities Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 20

21. Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 21

22. Just in time teaching Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 22

23. Browser add-in Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 23

24. Quick intuitive feedback Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 24

25. Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 25

26. Learning Glass (WS) Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 26

27. jfravel ﬂickr.com Team dynamics. Emotions? Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 27

28. Mood changes Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 28

29. Ribarnica ﬂickr.com Observe Think Act Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 29

30. VanessaO ﬂickr.com Closer to the user Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 30

31. Link Prediction Algorithms Sean MacEntee ﬂickr.com Graph: nodes = entities, edges = relationships Similar nodes, similar connections Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 31

32. Document Recommendation Recommend D2 to U3 and U4 Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 32

33. Action Recommendation Recommend Thread to U3 Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 33

34. Julien Carnot ﬂickr.com Learning analytics and personalisation Observe, Think, Act LATTE Retreat Sydney, November 23 2012 Abelardo Pardo (@abelardopardo) www.slideshare.net/abelardo_pardo Abelardo Pardo Learning analytics and personalisation Observe, Think, Act 34

Learning analytics and personalisation: Observe, think act

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Learning analytics and personalisation: Observe, think act