Presentation at the Learning Analytics and Linked Data workshop held at the International Conference on Learning Analytics and Linked Data 2012 Vancouver, BC, April/May 2012
Active learning methods are known to improve academic achievement. Flipped learning takes advantage of preparation activities to increase student engagement. But how do we approach the design of such experiences?
How to deploy a flipped classroom? What are the problems? Issues? In this talk I review some basic definitions and propose several steps to approach the flipped classroom.
Combining Observational and Experiential Data to Inform the Redesign of Learn...Abelardo Pardo
Approached the redesign of a course from two viewpoints. Based solely on observational data, and solely on experiential data. Then we combined them and see the different conclusions reached regarding the redesign. Presented at the Int. Conference on Learning Analytics and Knowledge, Poughkeepsie, NY
Adagio: Agile and Distributed Authoring of Generic Learning ObjectsAbelardo Pardo
An authoring kit to manage large content repositories as if they were made of small production links that are connected as a chain to create the content repository of a course
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.
Slides of the presentation given at the University Analytics Forum about how to approach privacy when deploying learning analytic systems with emphasis on what is perceived by the student.
Active learning methods are known to improve academic achievement. Flipped learning takes advantage of preparation activities to increase student engagement. But how do we approach the design of such experiences?
How to deploy a flipped classroom? What are the problems? Issues? In this talk I review some basic definitions and propose several steps to approach the flipped classroom.
Combining Observational and Experiential Data to Inform the Redesign of Learn...Abelardo Pardo
Approached the redesign of a course from two viewpoints. Based solely on observational data, and solely on experiential data. Then we combined them and see the different conclusions reached regarding the redesign. Presented at the Int. Conference on Learning Analytics and Knowledge, Poughkeepsie, NY
Adagio: Agile and Distributed Authoring of Generic Learning ObjectsAbelardo Pardo
An authoring kit to manage large content repositories as if they were made of small production links that are connected as a chain to create the content repository of a course
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.
Slides of the presentation given at the University Analytics Forum about how to approach privacy when deploying learning analytic systems with emphasis on what is perceived by the student.
Connecting Pedagogical Intent with Analytics in a Flipped ClassroomAbelardo Pardo
Description of how to use learning analytics techniques to collect evidence about student engagement while preparing a flipped classroom. A case study is presented in which students interact with various electronic resources and a measure of such engagement is produced and returned to them.
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.
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.
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.
Learning Analytics: Sensing and making sense of Learning ActivitiesAbelardo Pardo
Sensors are ubiquitous. Any action mediated by technology can be tracked to an
increasing level of detail. Learning is no exception. In a learning scenario, there
are numerous interactions that may occur mediated by technology. If this is the
case, there is a wealth of sensor data available to store, process and use to act
or intervene in the learning environment. In this talk the potential for learning
analytics as well as some of its drawbacks will be discussed.
Using learning analytics to help flip the classroomAbelardo Pardo
Presentation given at the 2013 Blended Learning Summit.
How can learning analytics help flip the classroom? What kind of technology can help us increase the level of engagement of students? Can the flipped classroom increase the effectiveness of a learning experience?
Using data to support active learning experiencesAbelardo Pardo
How can you leverage the use of data to improve a learning experience? Learning analytics helps increase the accuracy of how we perceive the complexity of a learning scenario. In this talk I present some suggestions and an example of how to achieve this.
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
The use of data and analytics to guide the improvement of learning experiencesAbelardo Pardo
Invited talk given at the International Forum on Big Data Analysis for Learning Improvement. It explains what is Learning Analytics, which aspects should be targeted by this emerging area, the algorithms and possible interventions that are derived, and finally the view from the point of view of two key stakeholders: instructors and students.
Analytics for decision making in Learning EnvironmentsAbelardo Pardo
Presentation given at the IARU EdTech Horizons Workshop about learning analytics, the main stages in the process, some examples, and finally, how to approach it from the institutional and course level.
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.
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Description of how to use learning analytics techniques to collect evidence about student engagement while preparing a flipped classroom. A case study is presented in which students interact with various electronic resources and a measure of such engagement is produced and returned to them.
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
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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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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.
Learning Analytics: Sensing and making sense of Learning ActivitiesAbelardo Pardo
Sensors are ubiquitous. Any action mediated by technology can be tracked to an
increasing level of detail. Learning is no exception. In a learning scenario, there
are numerous interactions that may occur mediated by technology. If this is the
case, there is a wealth of sensor data available to store, process and use to act
or intervene in the learning environment. In this talk the potential for learning
analytics as well as some of its drawbacks will be discussed.
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How can learning analytics help flip the classroom? What kind of technology can help us increase the level of engagement of students? Can the flipped classroom increase the effectiveness of a learning experience?
Using data to support active learning experiencesAbelardo Pardo
How can you leverage the use of data to improve a learning experience? Learning analytics helps increase the accuracy of how we perceive the complexity of a learning scenario. In this talk I present some suggestions and an example of how to achieve this.
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The use of data and analytics to guide the improvement of learning experiencesAbelardo Pardo
Invited talk given at the International Forum on Big Data Analysis for Learning Improvement. It explains what is Learning Analytics, which aspects should be targeted by this emerging area, the algorithms and possible interventions that are derived, and finally the view from the point of view of two key stakeholders: instructors and students.
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The provision of student feedback is a challenging and resource intensive
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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.
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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.
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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.
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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.
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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.
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Extending Course Level Learning Analytics with Linked Data
1. contemplative imaging flickr.com
Extending Course Level
Learning Analytics
with Linked Data
Workshop on
Learning Analytics
and Linked Data
Vancouver, April 29th 2012
Abelardo Pardo (@abelardopardo)
www.slideshare.net/abelardo_pardo
3. Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 4
4. Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 5
5. Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 6
6. Use big data to really
understandSteve Schoettler, CEO Junyo
the student
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 7
7. Slaunger flickr.com
Activity in LMS is only the tip
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 8
8. Reigh LeBlanc flickr.com
Intense
activity
outside of
LMS
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 9
9. pvanees flickr.com
Are you in the
LMS?
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 10
11. Pepe Ortuño flickr.com
A clearly identified
environment within
your computer
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 12
12. qmnonic flickr.com
Observe while they learn
Procedural activities
Collection of related events
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 13
13. orangebrompton flickr.com
Fully configured
Partial observation
Only while you learn
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 14
17. PranceyDog flickr.com
LA as vertical market specialized by
objectives
audiences
topics
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 18
18. intan_a3 flickr.com
Disambiguation by course topic
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 19
19. Sean Dreilinger flickr.com
Automatic discovery
of complementary
learning resources
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 20
20. Ontoolsearch
Find tools for collaborative learning
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 21
28. Linked Data at Open University UK
Evolve the way universities
expose material to students
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 29
29. Linked Data at University of Southampton UK
Non confidential data which is of use
to our members, visitors, and the public
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 30
31. pylbug flickr.com
Projects Student created content
Discovered resources
(Compounded measurements)
(Resource usage)
(Student evaluations)
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 32
32. Tricia Wang flickr.com
Courses use linked data
for real time adaptation
Courses offer
“relevant data”
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 33
33. contemplative imaging flickr.com
References I
The signal project
Purdue University
http://www.itap.purdue.edu/studio/signals/
Last accessed April 2012
Blackboard Analytics
Blackboard Inc.
http://www.blackboard.com/Platforms/Analytics/Overview.aspx
Last accessed April 2012
Desire2Learn Analytics
Desire2Learn
http://www.desire2learn.com/products/analytics/
Last accessed April 2012
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 35
34. References II
A linked data approach for the discovery of educational ICT tools
in the Web of Data
Ruiz-Calleja, A., Vega-Gorgojo, G., Asensio-Pérez, J. I.,
Bote-Lorenzo, M. L., Gómez-Sánchez, E., Alario-Hoyos, C.
Computers & Education
Accepted manuscript
Didactalia Gnoss
Gnoss
http://didactalia.net
Last accessed April 2012
Linked data at Open University UK
Open University UK
http://data.open.ac.uk
Last accessed April 2012
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 36
35. References III
Linked data at University of Southampton UK
University of Southampon UK
http://data.southampton.ac.uk/
Last accessed April 2012
Abelardo Pardo Extending Course Level Learning Analytics with Linked Data 37