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.
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.
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.
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.
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.
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.
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.
Have an idea for a startup but not sure where to begin? This presentation will provide guidance about how to turn that idea into a viable business. Learn a step-by-step methodology that will help you get beyond the idea phase and on the path to a successful startup venture.
Explicación teórica de los componentes didácticos de un taller. El desarrollo de una clase tiene su éxito en la articulación de cada uno de los componentes planificados
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.
Learning and Behavioral Analytics From concept to realityAbelardo Pardo
How can learning analytics be taken from its design to its deployment in an educational institution? What are the issues, limitations, strategies? This presentation includes a descirption of Learning Analytics, examples, how to tackle systemic deployment and suggestions on how to build institutional capacity.
Have an idea for a startup but not sure where to begin? This presentation will provide guidance about how to turn that idea into a viable business. Learn a step-by-step methodology that will help you get beyond the idea phase and on the path to a successful startup venture.
Explicación teórica de los componentes didácticos de un taller. El desarrollo de una clase tiene su éxito en la articulación de cada uno de los componentes planificados
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.
Learning and Behavioral Analytics From concept to realityAbelardo Pardo
How can learning analytics be taken from its design to its deployment in an educational institution? What are the issues, limitations, strategies? This presentation includes a descirption of Learning Analytics, examples, how to tackle systemic deployment and suggestions on how to build institutional capacity.
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?
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.
Technology for Active and Personalised Engineering EducationAbelardo Pardo
What type of educational technology is better suited for engineering education? What are the possible improvements? In this talk I present how educational technology can be used to improve engineering education and provide some samples of my past and current research.
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?
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
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.
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.
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.
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.
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.
Using technology to support the flipped classroomAbelardo Pardo
Learning experiences are increasingly relying in technology. At the same time, active learning, in which students participate in activities in the classroom has been shown to increase learning gains. Flipped classrooms refer to the paradigm in which certain activities are scheduled for the students before the classroom so that the face to face time is devoted to more active ones. In this talk we will review how technology can be used to support this paradigm and the challenges and issues that need to be addressed.
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.
Will Learning Analytics Transform Higher Education?Abelardo Pardo
Discussion on the elements, actors, cultural change and scenarios that are related to Learning Analytics in Higher Education Institutions. Presentation given at the Digital Education Show Asia, Kuala Lumpur, June 2015
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.
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.
Visual data-enriched design technology for blended learningLaia Albó
Presentation at Tallinn University.
Archimedes Foundation fellow - Research visit during 3 months at TLU.
Learning analytics is the most known type of data collected from specific technological environments that allow educators to evaluate how students are learning within a learning context. However, there are more types of data available, less-explored, that may contribute to better design educational practices. These include design analytics, which are the metrics of design decisions and related aspects that inform learning designs. Laia Albó, from Universitat Pompeu Fabra, will talk about how visual representations, authoring support, and design analytics can aid teachers in designing for learning in complex scenarios that blend the use of different spaces for learning and different types of technological tools and resources, e.g. Massive Open Online Courses. This presentation is based on her PhD thesis work, defended in November 2019.
Similar to Analytics to understand learning environments (20)
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.
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
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Exploring hands-on multidisciplinary STEM with Arduino EsploraAbelardo Pardo
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Generating Actionable Predictive Models of Academic PerformanceAbelardo Pardo
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Exploring the relation between Self-regulation Online Activities, and Academi...Abelardo Pardo
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Informal notes about a presentation in the New South Wales Learning Analytics Work group about how to send meaningful feedback to a large student cohort using learning analytics and semi-automatic processing.
How to approach the design of flipped classroom. Discuss the rational and motivation to adopt flipped learning, the use of resources and the steps designing a module.
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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
Slides of the hands on seminar at UNSW with Negin Mirriahi. We first selected a learning outcome from your course, and then work our way from there to a set of activities to orchestrate before and during the face to face time.
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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.
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Analytics to understand learning environments
1. r2hoxFlickr
Analytics to understand learning
environments
Chai Seminar, 14 April 2014
Dr Abelardo Pardo (@abelardopardo)
School of Electrical and Information Engineering
slideshare.net/abelardo_pardo
3. Abelardo Pardo Analytics to understand learning environments 3
StateRecordsNSWFlickr
Lecturer at School of EIE
Two courses
Grad/Undergrad
Active learning
Use of technology
Tech to empower individuals
and communities
Behavioral analytics
User validation
5. Abelardo Pardo Analytics to understand learning environments 5
VanessaOflickr.com
“Historically, humanity has made sense of the world
through discourse, dialogue, artifacts, myth, story,
and metaphor. While those sensemaking approaches
won’t disappear, they will be augmented by data and
analytics.”
(Siemens, 2014 www.elearnspace.org/blog)
6. Abelardo Pardo Analytics to understand learning environments 6
QuinnAnyaflickr.com
Learning
Analytics
Observe and
Collect
View and Act
Self-motivation
Flipped
classroom
8. Abelardo Pardo Analytics to understand learning environments 8
UnhinderedbyTalentflickr.com
Many colleges and universities have demonstrated
that analytics can help significantly advance an
institution in such strategic areas as resource
allocation, student success, and finance
(Bichsel, 2012)
11. Humans are Subjective
Abelardo Pardo Analytics to understand learning environments 11
Mind Sights: Original Visual Illusions, Ambiguities, and Other Anomalies, With a Commentary on the Play of Mind in Perception and Art, Roger N. Shepard, 1990
15. “We need better evaluation
to acknowledge and esteem
excellence when it occurs,
as it does.”
Abelardo Pardo Analytics to understand learning environments 15
BencroweFlickr
(Hattie, 2009)
16. Abelardo Pardo Analytics to understand learning environments 16
QuinnAnyaflickr.com
Learning
Analytics
Observe and
Collect
17. Abelardo Pardo Analytics to understand learning environments 17
ChefCookeFlickr.com
Observe while
working on
course
activities
18. A clearly identified
environment within
your computer
Abelardo Pardo Analytics to understand learning environments 18
KevinDaviesflickr.com
(Pardo & Delgado Kloos, 2011)
20. Abelardo Pardo Analytics to understand learning environments 20
HoriaVarlanflickr.com
Tool usage vs academic performance
(Romero-Zaldívar et al., 2012)
21. Abelardo Pardo Analytics to understand learning environments 21
Chiot’sRunFlickr.com
Collect URLs Compare with
course notes
Detect new
resources
(Romero-Zaldívar et al., 2011)
22. Abelardo Pardo Analytics to understand learning environments 22
AdventuresinLibrarianshipflickr.com
Detect engagement
while writing
(Calvo et al., 2011, Liu et al., 2013)
30. Abelardo Pardo Analytics to understand learning environments 30
QuinnAnyaflickr.com
Learning
Analytics
Observe and
Collect
View and Act
Self-motivation
31. “Transition” for patients with a chronic illness
Abelardo Pardo Analytics to understand learning environments 31
(Calvo et al., 2013)
32. Self-determination theory and types of
motivation
Abelardo Pardo Analytics to understand learning environments 32
(Ryan and Deci, 2000)
33. Explore the most adequate channels to dialogue
with the patient.
Abelardo Pardo Analytics to understand learning environments 33
(Yu Zhao, ASWEC Doctoral Consortium 2014)
35. Abelardo Pardo Analytics to understand learning environments 35
KeoniCabralflickr.com
Tech for high stakes finite time periods
36. Abelardo Pardo Analytics to understand learning environments 36
QuinnAnyaflickr.com
Learning
Analytics
Observe and
Collect
View and Act
Self-motivation
Flipped
classroom
37. Increasing use of videos
Abelardo Pardo Analytics to understand learning environments 37
38. What is missing?
Annotation
Highlight areas of importance
Collaboration
Integration in learning environment
Abelardo Pardo Analytics to understand learning environments 38
39. Students “comment” on video with some
guidance
Abelardo Pardo Analytics to understand learning environments 39
(OLT Project, Using video annotation software to develop student self-regulated learning)
40. Abelardo Pardo Analytics to understand learning environments 40
KreativeKewlflickr.com
Flipped Classroom
does not exist!
(Bergmann & Sams, 2012)
41. Abelardo Pardo Analytics to understand learning environments 41
MITCHELLflickr.com
Old idea, but revived due to two reasons
42. Abelardo Pardo Analytics to understand learning environments 42
Krugazorflickr.com
(Mazur, 2009)
Simple transfer of information is not optimal!
56. Data back to the students
Abelardo Pardo Analytics to understand learning environments 56
57. Your activity with respect to the rest of the class
Abelardo Pardo Analytics to understand learning environments 57
58. Abelardo Pardo Analytics to understand learning environments 58
Eleafflickr.com
• Will students use data
about their events?
• Will they act on what they
see?
• Will the tips have any
effect?
59. Abelardo Pardo Analytics to understand learning environments 59
(OLT Project, Radical transformation: re-imagining engineering
education through flipping the classroom in a global learning
partnership)
60. r2hoxFlickr
Analytics to understand learning
environments
Chai Seminar, 14 April 2014
Dr Abelardo Pardo (@abelardopardo)
School of Electrical and Information Engineering
slideshare.net/abelardo_pardo
61. References
Bichsel, J. (2012).
Analytics in Higher Education Benefits, Barriers, Progress, and Recommendations.
EDUCAUSE Center for Applied Research
Hattie, J. A. C. (2009).
Visible learning: A synthesis of over 800 meta-analyses related to achievement.
New York: Routledge.
Pardo, A., Delgado Kloos, C., 2011,
Stepping out of the box. Towards analytics outside the Learning Management System
International Conference on Learning Analytics and Knowledge, pp, 163-167, ACM New York, USA
Romero-Zaldívar, V.A., Pardo, A., Burgos, D., Delgado Kloos, C., 2012
Monitoring Student Progress Using Virtual Appliances: A Case Study
Computer & Education, 58(4):10580–1067, 2012
Romero-Zaldívar, V.A., Crespo García, R., Burgos, D., Delgado Kloos, C., Pardo, A., 2012
Automatic Discovery of Complementary Learning Resources
European Conference on Technology Enhanced Learning, 2011
Abelardo Pardo Analytics to understand learning environments 61
62. References II
Liu M., Calvo, R.; Pardo, A., 2013
Visualizing the effect of actions in personal informatics systems
Workshop on Personal Informatics, CHI’13
Calvo, R., O’Rourke, S.T., Jones, J., Yacef, K., Reimann, P., 2011
Collaborative Writing Support Tools on the Cloud
IEEE Transactions on Learning Technologies, 4(1):88–97
Monkaresi, H., Hussain, M. S., Calvo, R., 2012
A dynamic approach for detecting naturalistic affective states from facial videos during HCI
Australasian Joint Conference on Artificial Intelligence, Sydney, Australia. LNAI, Springer 2012, pp.170-181
Crespo García, R.M., Pardo, A., Delgado Kloos, C., Niemann, K., Scheffel, M., Wolpers, M., 2012
Peeking into the black box: visualising learning activities.
International Journal on Technology Enhanced Learning, 4(1/2), 2012
Leony, D., Pardo A., De la Fuente Valentín, L., Sánchez de Castro, D., Delgado Kloos, C. 2012
GLASS : A Learning Analytics Visualization Tool
International Conference on Learning Analytics and Knowledge, pp. 162-163, ACM Press.
Abelardo Pardo Analytics to understand learning environments 62
63. References III
Calvo, R. A., Pardo, A., Zhao, Y., Klineberg, E., Lam, M., and Steinbeck, K. 2013.
TransitionMate: a mobile application for chronic illness transition support.
In C. Röcker, M. Ziefle, A. Holzinger, K. McGee, S. Hansen, and J. Meyer (Eds.), Fifth International
Workshop on Smart Healthcare and Wellness Applications (SmartHealth’13) - OzCHI 2013, Nov 25-26.
Adelaide, South Australia: ACM.
Ryan, R., and Deci, E. (2000).
Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being.
American Psychologist, 55(1), 68–78.
Bergmann, J., Sams, A., (2012)
Flip Your Classroom: Reach Every Student in Every Class Every Day
Hawker Brownlow Education
Mazur, E., (2009)
Farewell, Lecture?
Science, 323, pp. 50-51.
Abelardo Pardo Analytics to understand learning environments 63