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.
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.
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.
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.
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.
Generating Actionable Predictive Models of Academic PerformanceAbelardo Pardo
Exploring predictive models that are closer to action by instructors. The talk proposes the use of hierarchical partitioning algorithms to produce decision trees that can be used to divide students into groups and simplify how feedback is provided.
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?
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.
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.
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.
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.
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.
Generating Actionable Predictive Models of Academic PerformanceAbelardo Pardo
Exploring predictive models that are closer to action by instructors. The talk proposes the use of hierarchical partitioning algorithms to produce decision trees that can be used to divide students into groups and simplify how feedback is provided.
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?
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
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.
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.
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.
A quick look at 9 design strategies for Learning Experiences. How to support both the cognitive and emotional sides of learning as well as design and measure for learning impact. For UX/UI designers.
Presentation for IPTV on March 11, 2009 - 3:30 - 5:00. The Role of Teacher Librarians and the Iowa Core Curriculum. (Updated with some background notes in the PPT, March 12)
ALA 2015 Invited Research Talk: Youth Collaborative Information Practices Dur...Rebecca Reynolds
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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.
This is my slideshow for my ULearn11 breakout:
We have been using e-Portfolios with Year 1 to 6 students at Elm Park School since 2007 and shortly afterwards made the decision to use our e-Portfolios as our sole method of reporting to parents. During this presentation we will discuss our ongoing journey to implement e-Portfolios school-wide, our purpose behind the decision to start the journey, the successes and the challenges - warts and all! We’ll have a look at some e-Portfolios examples and share the professional development, resources, equipment and web 2.0 tools that we have found most useful to help us along the way.
We use KnowledgeNET’s Learning Journals at Elm Park School to create our e-Portfolios but this workshop will also be of interest to those using other applications.
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.
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.
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
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.
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.
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.
A quick look at 9 design strategies for Learning Experiences. How to support both the cognitive and emotional sides of learning as well as design and measure for learning impact. For UX/UI designers.
Presentation for IPTV on March 11, 2009 - 3:30 - 5:00. The Role of Teacher Librarians and the Iowa Core Curriculum. (Updated with some background notes in the PPT, March 12)
ALA 2015 Invited Research Talk: Youth Collaborative Information Practices Dur...Rebecca Reynolds
This presentation was delivered as part of an ALA Conference 2015 special research session, "Out of the Library School and Into the School Library," sponsored by the Institute for Museum and Library Services. The session featured presentations of research findings stemming from the work of recent Early Career Development Grant awardees.
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.
This is my slideshow for my ULearn11 breakout:
We have been using e-Portfolios with Year 1 to 6 students at Elm Park School since 2007 and shortly afterwards made the decision to use our e-Portfolios as our sole method of reporting to parents. During this presentation we will discuss our ongoing journey to implement e-Portfolios school-wide, our purpose behind the decision to start the journey, the successes and the challenges - warts and all! We’ll have a look at some e-Portfolios examples and share the professional development, resources, equipment and web 2.0 tools that we have found most useful to help us along the way.
We use KnowledgeNET’s Learning Journals at Elm Park School to create our e-Portfolios but this workshop will also be of interest to those using other applications.
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.
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.
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.
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.
Human-Centered Learning Analytics and Artificial Intelligence in Education: H...Yannis
Although Artificial Intelligence (AI) and Learning Analytics (LA) have shown their potential in Education, stakeholders’ agency seems to be threatened. On the other hand, multiple issues regarding FATE (Fairness, Accountability, Transparency and Ethics) have been raised when AI or LA-based solutions are designed and implemented. These issues have been especially acute since the emergence of Large Language Models and Generative AI.
This talk discusses the quest for an optimal balance between human and computational agents, when LA tools and services are employed in a Technology Enhanced Learning (TEL) ecosystem. Through the discussion of relevant conceptual models and examples, it argues for Human-Centered Learning Analytics (HCLA) and Human-Centered Artificial Intelligence (HCAI) approaches, where agency and FATE principles are essential design parameters.
The talk focuses especially on LA/AI solutions that may position teachers as designers of effective interventions and orchestration actions. Selected Human-Centered Design (HCD) principles are discussed and illustrated, and directions for future research and development are formulated to overcome the main obstacles for adoption of human-centered approaches for LA and AI in education.
Keynote Presentation: Implementing learning analytics and learning design at ...Bart Rienties
The University of the Roller Coaster
How can Higher Education function in a world struggling to save itself from climate change, pandemics and war? How can it drive innovation and shape the future as the pace of technological change constantly increases? How can it re-invent itself to respond imaginatively to the new challenges facing humanity?
We are living in an uncertain, unpredictable world with no “back to normal” any more. So, how can we re-imagine higher education when nothing can be taken for granted? What kind of technologies can help universities to adapt? What lessons can we learn from recent successes and failures? What 'best practice' examples point the way into the future? How can we shape the development of institutions, so that they are neither “ivory towers” nor “competence factories"? How can we encourage future-oriented universities in which both pedagogy and research are fit for the challenges ahead?
In the Academic Plenary, our experts will examine the threats and opportunities facing higher education today and ask how we can design new approaches that prepare staff and students to thrive in the University of the Roller Coaster.
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
Keynote presentation of Yannis Dimitriadis at Intelligent Tutoring Systems 2022: Human-Centered Learning Analytics: Designing for balanced human and computational agency
Data2U: Scalable Real time Student Feedback in Active Learning EnvironmentsAbelardo Pardo
Active learning environments require sustained student engagement in learning scenarios. Can we use data to provide feedback in real time about this participation?
Presentation at Networked Learning Conference 2014 in Edinburgh:
Crossing Professional Thresholds with Networked Learning? An Analysis of Student E-Portfolios Using the Threshold Concept Perspective
Learning analytics: An opportunity for higher education?Dragan Gasevic
Slides used in my keynote at the Annual Conference of the European Association of Distance Teaching Universities - The open, online, flexible higher education conference - #OOFHEC2015
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My presentation on how we conducted an online blended class involving Python programming for business management students amidst the COVID-19 lockdown. This was presented at Globe FOREFRONT: The 2020 National Conference on Digital Learning.
Seminario eMadrid/SHEILA sobre "Analítica del Aprendizaje". ¿Cómo llegamos al...eMadrid network
Seminario eMadrid/SHEILA sobre "Analítica del Aprendizaje". ¿Cómo llegamos allí? Pasos hacia la adopción sistémica de la analítica de aprendizaje. Dragan Gasevic. Universidad de Edimburgo. 21/10/2016.
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Provision of personalized feedback at scale using learning analytics
1. Provision of personalized feedback at
scale using learning analytics
Centre for the Enhancement of Teaching and Learning
Faculty of Education
Hong Kong University 19/May/2017
Abelardo Pardo (@abelardopardo)
Faculty of Engineering and IT
slideshare.net/abelardo_pardo
Antoniettaflickr.com
2. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 2
MaureenBarlinflickr.com
New
Design
Mindset
The role
of data
and feedback
Personalised feedback
at scale
3. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 3
MaureenBarlinflickr.com
New
Design
Mindset
4. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 4
Simple information transfer is not working
Mazur, E. (2009). Farewell, lecture. Science, 323(5910), 50-51.
Krugazorflickr.com
6. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 6
Active Learning Works
Engage students in
the learning process
Bonwell, C. C., & Eison, J. A. (1991). Active Learning: Creating Excitement in the Classroom ASHEERIC Higher Education Report No. 1.
Washington, DC, USA: George Washington University.
7. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 7
“… robust correlations between student
involvement in a subset of ‘educationally
purposive activities’, and positive outcomes of
student success and development, including
satisfaction, persistence, academic achievement
and social engagement”
Trowler, V. (2010). Student engagement literature review. York, UK: The Higher Education Academy.
shuaGandersonflickr.com
8. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 8
Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: beliefs, techniques, and illusions. Annu Rev Psychol, 64,
417-444. doi:10.1146/annurev-psych-113011-143823
ChristianWeidingerflickr.com
We, as learners may
• Not know how to promote
comprehension, retention, transfer.
• Not assess properly our own
learning
• Be biased when judging our
learning
• Rely too much on social beliefs
9. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 9
JansonHewsflickr.com
Frontier between physical and virtual spaces is blurring
10. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 10
Beware of technology creating the illusion of rational thinking
JenRflickr.com
11. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 11
“… teaching in higher education will
necessarily shift the balance of its efforts
towards a greater investment in design
as a way of coping with otherwise
intolerable pressures on staff and
resources.”
Goodyear, P. (2015). Teaching as Design. HERDSA Review of Higher Education, 2, 27-50.
12. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 12
"There is no such thing as a neutral design"
JeremyBrooksflickr.com
Thaler, R. H., & Sunstein, C. R. (2008). Nudge. Great Britain: Yale University Press.
“People make good choices in contexts in which
they have experience, good information, and
prompt feedback"
13. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 13
“38 meta-analyses investigating 105 correlates of achievement, based on
3,330 effect sizes from almost 2 million students”
Schneider, M., & Preckel, F. (2017). Variables Associated With Achievement in Higher Education: A Systematic Review of Meta-
Analyses. Psychological Bulletin. doi:10.1037/bul0000098
• The effectivity of courses is strongly related to what teachers do.
• The effectivity of teaching methods depends on how are implemented
• Teachers can improve the instructional quality of their courses by
making a number of small changes
- providing detailed task-focused and improvement-oriented
feedback
• The combination of teacher-cantered and student-cantered
instructional elements is more effective than either form of instruction
alone
Variables associated with achievement
14. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 14
MaureenBarlinflickr.com
New
Design
Mindset
The role
of data
and feedback
15. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 15
Students are less likely to engage in pre-class activities if they
are not interactive, do not provide formative feedback, and
not coherently linked with the face-to-face activities
O'Flaherty, J., & Phillips, C. (2015). The use of flipped classrooms in higher education: A scoping review. The Internet and Higher
Education, 25, 85-95. doi:10.1016/j.iheduc.2015.02.002
DanKlimkeflickr.com
16. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics
If you could choose one…
• Over 800 meta-analyses of student
achievements
• 100 factors with potential influence
• Feedback in top five
• (74 meta-analyses) Most effective
form: video, audio, computer-
assisted instructional feedback,
and/or related goals
16
Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses related to achievement. New York: Routledge.
17. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 17
Boud, D., & Molloy, E. (Eds.). (2013). Feedback in Higher and Professional Education: Understanding it and doing it well. London
and New York: Routledge.
FarukAteşflickr.com
“Feedback is a process whereby learners obtain information about
their work in order to appreciate the similarities and differences
between the appropriate standards for any given work, and the
qualities of the work itself, in order to generate improved work”
18. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 18
Black, P., & Wiliam, D. (1998). Assessment and Classroom Learning. Assessment in Education: Principles, Policy & Practice, 5(1),
7-74. doi:10.1080/0969595980050102
GarethChristopherflickr.com
Innovations designed to strengthen the frequent
feedback that students receive about their
learning yield substantial learning gains
19. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 19
Hounsell, D. (2007). Toward more sustainable feedback to students. In D. Boud & N. Falchikov (Eds.), Rethinking Assessment in
Higher Education: Learning for the Longer Term. London and New York: Routledge.
Perceived as an administrative chore
instead of a pedagogical necessity
MarcinWicharyflickr.com
20. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 20
IanRansleyflickr.com
How to scale sustainable practices?
Carless, D., Salter, D., Yang, M., & Lam, J. (2011). Developing sustainable feedback practices. Studies in Higher Education, 36(4),
395-407. doi:10.1080/03075071003642449
• Multi-stage assignments
• Dialogic feedback
• Technology supported
• Self-evaluation
21. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 21
Baker, R. S. (2016). Stupid Tutoring Systems, Intelligent Humans. International Journal of Artificial Intelligence in Education. doi:
10.1007/s40593-016-0105-0
From artificial intelligence to intelligence amplification
Instructors are informed about student engagement
but it is up to them to decide if/when/how to act
Tecnaliaflickr.com
22. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 22
DmitryGrigorievflickr.com
Learning Analytics: measure, collect, analyse data
about learners to understand and improve their
learning and the environment in which it occurs
1st International Conference on Learning Analytics and Knowledge [Online]. https://tekri.athabascau.ca/analytics. [Accessed 2017].
23. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 23
• Collect data about how students
engage in a learning experience
• Interpret the observations in the
context of the instructional design
• Translate knowledge into
personalised student support
actions
24. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 24
Bartimote-Aufflick, K., Reimann P., Pardo, A. The perspective theory brings to learning analytics in the classroom: A realist approach
Manuscript in preparation.
Learning Analytics Model
25. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 25
MaureenBarlinflickr.com
New
Design
Mindset
The role
of data
and feedback
Personalised feedback
at scale
26. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 26
Example of Highly Instrumentalized Learning Design
27. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 27
Weekly real time feedback
28. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 28
No statistically significant difference in the rating of feedback (2013 edition,
M=3.25, SD=0.97; 2014 edition, M=3.35, SD=1.03); t(389.78) = -0.97, p <0.17
29. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 29
You should take a more
careful look at how symbols
are encoded in the video.
Would you be able to encode/
decode UAL symbols without
looking at the video?
Good initial work. However,
did you understand the trick
to handle encoding with a
variable number of bits?
Would you be able to provide
an example?
Good work. Would you be
able to come up with your
own machine language and
your encoding scheme?
Remember that it has to be
unambiguous.
Thorough work with the task
about machine language
encoding. Give it a quick
review before the midterm.
Q1 Q2 Q3 Q4
Instructor
30. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 30
Algorithm
31. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 31
Automatic
Email
32. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 32
Helpful feedback
Effect size (Cohen’s d) = 0.49.
Medium positive effect
Midterm Scores
Effect size (Cohen’s d) = 0.21.
Small positive effect
Pardo, A., Jovanovic, J., Dawson, S., Gašević, D. & Mirriahi, N. (2017). Using Learning Analytics to Scale the Provision of Personalised
Feedback. Manuscript in preparation
33. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 33
ontasklearning.org
34. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 34
• Support instructors to
create personalised
feedback
• Simple rule-base
knowledge encoding
• Provide appropriate
view of data sources
• Scale to large and highly
diverse cohorts
• Will be released as
open-source project
Q3/4 2017
• First pilots in Q1/2 2017
• Tutorial in LAK 2017
• Contact us if interested
ontasklearning.org
35. Abelardo Pardo Provision of Personalised Feedback at Scale Using Learning Analytics 35
MTC0316flickr.com
Conclusions
• New L&T design mindset required
• Feedback is effective to promote
student engagement
• Learning analytics has the
potential to support students at
scale
• Use data to provide personalised
student support
37. Provision of personalized feedback at
scale using learning analytics
Abelardo Pardo (@abelardopardo)
Faculty of Engineering and IT
slideshare.net/abelardo_pardo
Antoniettaflickr.com
Centre for the Enhancement of Teaching and Learning
Faculty of Education
Hong Kong University 19/May/2017