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.
Designing Engaging Learning Experiences in Digital EnvironmentsAbelardo Pardo
Talk about how to address the design of learning experiences in the current digital environments and how to take into account the student perspective, motivation, feedback, and other various aspects.
Provision of personalized feedback at scale using learning analyticsAbelardo Pardo
The increasing presence of technology mediation offers an unprecedented opportunity to use detailed data sets about the interactions that occur while a learning experience is being enacted. Areas such as Learning Analytics or Educational Data Mining have explored numerous algorithms and techniques to process these data sets. Additionally, technology now offers the opportunity to increase the immediacy of interventions. However, not much emphasis has been placed on how to extract truly actionable knowledge and how to bring it effectively as part of a learning experience. In this talk, we will use the concept of feedback as the focus to establish a specific connection between the knowledge derived from data-analysis procedures and the actions that can be immediately deployed in a learning environment. We will discuss how there is a trade-off between low-level automatic feedback and high-level complex feedback and how technology can provide efficient solutions for the case of large or highly diverse cohorts.
Articulating the connection between Learning Design and Learning AnalyticsAbelardo Pardo
Learning analytics is a discipline that uses data captured by technology during a learning experience to increase our level of understanding, increase its quality, and improve the environment in which it occurs. But these experiences need to be designed first. In this talk we start from the statement that there is no such thing as a neutral design. In the era of increasing technology mediation Learning experiences need to be designed considering the capacity to capture data, the possibility of making sense and derive knowledge from the data, and the need to act on that knowledge. In this talk we will explore some initiatives to make these connections explicit in a learning design. Using a flipped learning experience, we will explore how to embed data and data analysis as part of the design tasks.
The role of data in the provision of feedback at scaleAbelardo Pardo
Technology mediation allows to capture comprehensive data sets about interactions occurring in learning experiences. Although these data sets have the potential of increasing the insight on how learning occurs, their use strongly depends on two aspects: the data has to be properly situated in the learning design, and the insights derived need to be translated into actions. In this talk we will explore how to establish this connection for the case of the provision of feedback. We will approach the problem from the point of view of intelligence amplification, that is, how data can support instructors to provide better support to learners through feedback. The talk will discuss some preliminary results from the Ontasklearning.org project.
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 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.
James buckingham dreaded group work - 23 nov 2013.keyTAEDTECH Sig
The document discusses strategies for making group work less dreaded for students. It begins by examining common issues with group work, such as unclear roles and expectations, lack of structure and accountability. The document then presents strategies for addressing these issues, such as using collaboration tools like Etherpad and Trello to structure work and ensure accountability. It suggests the role of the teacher is important in implementing these strategies and coaching students on interpersonal skills for successful group work. Overall, the document argues that with the right strategies and tools, group work can be a valuable learning experience for students.
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 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.
Using data to support active learning experiencesAbelardo Pardo
This document discusses using data and learning analytics to support active learning experiences. It explains that institutions already collect significant data through LMS interactions, student information systems, and other sources. Learning analytics is the measurement, collection, analysis and reporting of this data to understand and optimize learning. The document provides an example of how data from an electronics course was analyzed to identify students at risk and guide interventions to support active learning. It argues that data can help improve active learning by providing insights but accessing and applying data at scale remains a challenge.
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.
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.
Using Learning Analytics to Help Flip the ClassroomAbelardo Pardo
The document discusses using learning analytics to help flip the classroom. It describes collecting data from students' online activities using tools like forums and tracking resource usage. This data is then analyzed to identify factors that correlate with performance and inform just-in-time interventions. The goal is to observe the learning process, provide feedback, and optimize teaching approaches based on students' needs.
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?
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.
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.
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.
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.
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.
Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partn...Simon Bates
Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partners in Learning and Teaching. In this keynote, I will consider the role of students as partners in learning with reference to what current research can tell us about how people learn, what students have to say about what supports their learning, and where technology can help.
Learning analytics : foundation of mass personalized educationSander Latour
1) Learning analytics is the foundation for mass personalized education by measuring and analyzing data about learners and their learning environment.
2) Data from online learning activities can be analyzed to provide recommendations to students and teachers to optimize learning, such as predicting risks, finding bottlenecks, and suggesting interventions.
3) Potential issues with learning analytics include privacy concerns, lack of data, and focusing too much on tools rather than learners.
1. The document discusses strategies for integrating technology, pedagogy, and content across disciplines. It provides examples of tools and frameworks that can be used to enhance collaboration, research, writing, presentation, and organization skills across subject areas.
2. Key strategies discussed include flipped classrooms, formative assessment, project-based learning, differentiation, and shifting away from solely using grades for assessment. Brain-based learning research and developing meaningful homework are also covered.
3. Specific techniques and tools are suggested for each area, such as using graphic organizers, Google Docs for collaborative writing, and Prezi or Google Slides for digital presentations. The document emphasizes finding ways to develop consistent skills across classes.
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.
This document summarizes a workshop on linking learning analytics, learning design, and MOOCs. It discusses how learning analytics can provide actionable intelligence for learners and educators. Group activities involved analyzing MOOCs to identify learning outcomes, assessments, and how analytics could support learning. The document suggests learning design tools like templates, planners, and maps can help identify useful analytics and frame analytics questions. The goal is to use analytics to facilitate learning, identify struggles, engagement, and address problems by starting with pedagogy.
EMMA Summer School - Rebecca Ferguson - Learning design and learning analytic...EUmoocs
This hands-on workshop will work with learning design tools and with massive open online courses (MOOCs) on the FutureLearn platform to explore how learning design can be used to influence the choice and design of learning analytics. This workshop will be of interest to people who are involved in the design or presentation of online courses, and to those who want to find out more about learning design, learning analytics or MOOCs. Participants will find it helpful to have registered for FutureLearn and explored the platform for a short time in advance of the workshop.
This presentation was given during the EMMA Summer School, that took place in Ischia (Italy) on 4-11 July 2015.
More info on the website: http://project.europeanmoocs.eu/project/get-involved/summer-school/
Follow our MOOCs: http://platform.europeanmoocs.eu/MOOCs
Design and deliver your MOOC with EMMA: http://project.europeanmoocs.eu/project/get-involved/become-an-emma-mooc-provider/
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.
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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.
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This document discusses using data and learning analytics to support active learning experiences. It explains that institutions already collect significant data through LMS interactions, student information systems, and other sources. Learning analytics is the measurement, collection, analysis and reporting of this data to understand and optimize learning. The document provides an example of how data from an electronics course was analyzed to identify students at risk and guide interventions to support active learning. It argues that data can help improve active learning by providing insights but accessing and applying data at scale remains a challenge.
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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.
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.
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The document discusses using learning analytics to help flip the classroom. It describes collecting data from students' online activities using tools like forums and tracking resource usage. This data is then analyzed to identify factors that correlate with performance and inform just-in-time interventions. The goal is to observe the learning process, provide feedback, and optimize teaching approaches based on students' needs.
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?
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.
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.
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.
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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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.
Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partn...Simon Bates
Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partners in Learning and Teaching. In this keynote, I will consider the role of students as partners in learning with reference to what current research can tell us about how people learn, what students have to say about what supports their learning, and where technology can help.
Learning analytics : foundation of mass personalized educationSander Latour
1) Learning analytics is the foundation for mass personalized education by measuring and analyzing data about learners and their learning environment.
2) Data from online learning activities can be analyzed to provide recommendations to students and teachers to optimize learning, such as predicting risks, finding bottlenecks, and suggesting interventions.
3) Potential issues with learning analytics include privacy concerns, lack of data, and focusing too much on tools rather than learners.
1. The document discusses strategies for integrating technology, pedagogy, and content across disciplines. It provides examples of tools and frameworks that can be used to enhance collaboration, research, writing, presentation, and organization skills across subject areas.
2. Key strategies discussed include flipped classrooms, formative assessment, project-based learning, differentiation, and shifting away from solely using grades for assessment. Brain-based learning research and developing meaningful homework are also covered.
3. Specific techniques and tools are suggested for each area, such as using graphic organizers, Google Docs for collaborative writing, and Prezi or Google Slides for digital presentations. The document emphasizes finding ways to develop consistent skills across classes.
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.
This document summarizes a workshop on linking learning analytics, learning design, and MOOCs. It discusses how learning analytics can provide actionable intelligence for learners and educators. Group activities involved analyzing MOOCs to identify learning outcomes, assessments, and how analytics could support learning. The document suggests learning design tools like templates, planners, and maps can help identify useful analytics and frame analytics questions. The goal is to use analytics to facilitate learning, identify struggles, engagement, and address problems by starting with pedagogy.
EMMA Summer School - Rebecca Ferguson - Learning design and learning analytic...EUmoocs
This hands-on workshop will work with learning design tools and with massive open online courses (MOOCs) on the FutureLearn platform to explore how learning design can be used to influence the choice and design of learning analytics. This workshop will be of interest to people who are involved in the design or presentation of online courses, and to those who want to find out more about learning design, learning analytics or MOOCs. Participants will find it helpful to have registered for FutureLearn and explored the platform for a short time in advance of the workshop.
This presentation was given during the EMMA Summer School, that took place in Ischia (Italy) on 4-11 July 2015.
More info on the website: http://project.europeanmoocs.eu/project/get-involved/summer-school/
Follow our MOOCs: http://platform.europeanmoocs.eu/MOOCs
Design and deliver your MOOC with EMMA: http://project.europeanmoocs.eu/project/get-involved/become-an-emma-mooc-provider/
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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.
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Ultimate VMware 2V0-11.25 Exam Dumps for Exam SuccessMark Soia
Boost your chances of passing the 2V0-11.25 exam with CertsExpert reliable exam dumps. Prepare effectively and ace the VMware certification on your first try
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Algebra 1 is often described as a “gateway” class, a pivotal moment that can shape the rest of a student’s K–12 education. Early access is key: successfully completing Algebra 1 in middle school allows students to complete advanced math and science coursework in high school, which research shows lead to higher wages and lower rates of unemployment in adulthood.
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The *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responThe *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responses*: Insects can exhibit complex behaviors, such as mating, foraging, and social interactions.
Characteristics
1. *Decentralized*: Insect nervous systems have some autonomy in different body parts.
2. *Specialized*: Different parts of the nervous system are specialized for specific functions.
3. *Efficient*: Insect nervous systems are highly efficient, allowing for rapid processing and response to stimuli.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive in diverse environments.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive
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This visual and beginner-friendly guide simplifies the APA referencing style (7th edition) for academic writing. Designed especially for commerce students and research beginners, it includes:
✅ Real examples from original research papers
✅ Color-coded diagrams for clarity
✅ Key rules for in-text citation and reference list formatting
✅ Free citation tools like Mendeley & Zotero explained
Whether you're writing a college assignment, dissertation, or academic article, this guide will help you cite your sources correctly, confidently, and consistent.
Created by: Prof. Ishika Ghosh,
Faculty.
📩 For queries or feedback: [email protected]
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The Pala kings were people-protectors. In fact, Gopal was elected to the throne only to end Matsya Nyaya. Bhagalpur Abhiledh states that Dharmapala imposed only fair taxes on the people. Rampala abolished the unjust taxes imposed by Bhima. The Pala rulers were lovers of learning. Vikramshila University was established by Dharmapala. He opened 50 other learning centers. A famous Buddhist scholar named Haribhadra was to be present in his court. Devpala appointed another Buddhist scholar named Veerdeva as the vice president of Nalanda Vihar. Among other scholars of this period, Sandhyakar Nandi, Chakrapani Dutta and Vajradatta are especially famous. Sandhyakar Nandi wrote the famous poem of this period 'Ramcharit'.
Understanding P–N Junction Semiconductors: A Beginner’s GuideGS Virdi
Dive into the fundamentals of P–N junctions, the heart of every diode and semiconductor device. In this concise presentation, Dr. G.S. Virdi (Former Chief Scientist, CSIR-CEERI Pilani) covers:
What Is a P–N Junction? Learn how P-type and N-type materials join to create a diode.
Depletion Region & Biasing: See how forward and reverse bias shape the voltage–current behavior.
V–I Characteristics: Understand the curve that defines diode operation.
Real-World Uses: Discover common applications in rectifiers, signal clipping, and more.
Ideal for electronics students, hobbyists, and engineers seeking a clear, practical introduction to P–N junction semiconductors.
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The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include:
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- Accessibility: Designed for easy reading, wide distribution, and interactive use.
- Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.
CURRENT CASE COUNT: 817 (As of 05/3/2025)
• Texas: 688 (+20)(62% of these cases are in Gaines County).
• New Mexico: 67 (+1 )(92.4% of the cases are from Eddy County)
• Oklahoma: 16 (+1)
• Kansas: 46 (32% of the cases are from Gray County)
HOSPITALIZATIONS: 97 (+2)
• Texas: 89 (+2) - This is 13.02% of all TX cases.
• New Mexico: 7 - This is 10.6% of all NM cases.
• Kansas: 1 - This is 2.7% of all KS cases.
DEATHS: 3
• Texas: 2 – This is 0.31% of all cases
• New Mexico: 1 – This is 1.54% of all cases
US NATIONAL CASE COUNT: 967 (Confirmed and suspected):
INTERNATIONAL SPREAD (As of 4/2/2025)
• Mexico – 865 (+58)
‒Chihuahua, Mexico: 844 (+58) cases, 3 hospitalizations, 1 fatality
• Canada: 1531 (+270) (This reflects Ontario's Outbreak, which began 11/24)
‒Ontario, Canada – 1243 (+223) cases, 84 hospitalizations.
• Europe: 6,814
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This is short and accurate description of World war-1 (1914-18)
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GDGLSPGCOER - Git and GitHub Workshop.pptxazeenhodekar
This presentation covers the fundamentals of Git and version control in a practical, beginner-friendly way. Learn key commands, the Git data model, commit workflows, and how to collaborate effectively using Git — all explained with visuals, examples, and relatable humor.
GDGLSPGCOER - Git and GitHub Workshop.pptxazeenhodekar
Ad
Analytics for decision making in Learning Environments
1. Kris Krüg Flickr
Analytics for decision making in
Learning Environments
Dr Abelardo Pardo (@abelardopardo)
The University of Sydney
slideshare.net/abelardo_pardo
IARU EdTech Horizons Workshop
Singapore, 14 November 2014
2. Abelardo Pardo Analytics for decision making in Learning Environments 2
Tristan Martin flickr.com
Learning
Analytics Collecting
Observations
Analysis and
Visualization
Interventions Institutional
Adoption
3. Abelardo Pardo Analytics for decision making in Learning Environments 3
Tristan Martin flickr.com
Learning
Analytics
4. NY Times 24/11/12
Abelardo Pardo Analytics for decision making in Learning Environments 4
5. Abelardo Pardo Analytics for decision making in Learning Environments 5
jbgeronimi flickr.com
Learning is complex and opaque
6. Uncertainty in Education
“Even in education,
however, there are
some propositions
that give you a
great chance of
comming out ahead
if you bet on them
often enough”
(Felder, 2009)
Abelardo Pardo Analytics for decision making in Learning Environments 6
7. Learning Analytics
Measurement, collection, analysis
and reporting of data about learners
and their contexts, for purposes of
understanding and optimizing
learning and the environments in which
it occurs.
(SoLAR, Society for Learning
Analytics Research)
Abelardo Pardo Analytics for decision making in Learning Environments 7
8. Abelardo Pardo Analytics for decision making in Learning Environments 8
http://www.learninganalytics.net/?p=131 (last visited Sep 2013)
(Siemens & Long, 2011)
10. L Jubar flickr.com
Fork in the road:
uptake?
Abelardo Pardo Analytics for decision making in Learning Environments 10
11. Derable flickr.com
High impact area: evolution, revolution, tangible
improvements.
Abelardo Pardo Analytics for decision making in Learning Environments 11
12. Yoakim flickr.com
Fail at large scale and
become marginal
Abelardo Pardo Analytics for decision making in Learning Environments 12
13. Business Week May 01, 2014
400 data points
per student
Teacher in-class
recordings
Abelardo Pardo Analytics for decision making in Learning Environments 13
14. Where should I focus?
Abelardo Pardo Analytics for decision making in Learning Environments 14
visible-learning.org/hattie-ranking-influences-effect-sizes-learning-achievement/hattie-ranking-teaching-effects (Last Accessed Nov. 2014)
15. Mary Witzig flickr.com
Personalized Learning in a Digital Environment
Abelardo Pardo Analytics for decision making in Learning Environments 15
16. Hiking Artist
Learning Technology fails:
hinders, prevents, complicates ...
Abelardo Pardo Analytics for decision making in Learning Environments 16
17. Anthony flickr.com
Tech affordances
must have strong,
realistic
pedagogical
foundations
Abelardo Pardo Analytics for decision making in Learning Environments 17
18. VanessaO flickr.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.”
George Siemens, www.elearnspace.org/blog/2014/04/11/open-learning-analytics/
Abelardo Pardo Analytics for decision making in Learning Environments 18
19. Janson Hews flickr.com
New
Assessments?
Abelardo Pardo Analytics for decision making in Learning Environments 19
20. Ashley Fisher flickr.com
21st century skills
Critical thinking
Team work skills
Communication skills
Information literacy
Creativity and Innovation
Abelardo Pardo Analytics for decision making in Learning Environments 20
21. Discourse Analysis
(De Liddo et al., 2011)
Abelardo Pardo Analytics for decision making in Learning Environments 21
22. Paul Mayne flickr.com
Learning Dispositions:
Tendency to behave in
certain way when learning.
(Buckingham Sum, Deaking Crick, 2012)
Abelardo Pardo Analytics for decision making in Learning Environments 22
23. Werner Kunz Flickr.com
Predict
The five
steps of
analytics
Collect
Report
(Campbell, De Blois, Oblinger 2007, Academic Analytics, EDUCAUSE)
Act
Refine
Abelardo Pardo Analytics for decision making in Learning Environments 23
24. Tristan Martin flickr.com
Learning
Analytics Collecting
Observations
Abelardo Pardo Analytics for decision making in Learning Environments 24
25. Example: Moodle Log
• Course name
• Date and time
• IP Address (geolocation)
• User full nane
• Action: discussion mark read, forum add
discussion, forum add post, forum delete
discussion, forum update post, notes view,
resource view, etc.
• Additional information
(Pardo, 2014)
Abelardo Pardo Analytics for decision making in Learning Environments 25
26. Chef Cooke Flickr.com
Observe while
working on
course
activities
Abelardo Pardo Analytics for decision making in Learning Environments 26
29. Track
Track
Abelardo Pardo Analytics for decision making in Learning Environments 29
30. com
flickr.Davies Kevin A clearly identified
environment within
your computer
Abelardo Pardo Analytics for decision making in Learning Environments 30 (Pardo & Delgado Kloos, 2011)
31. Instrumentation
Personal Computer
Virtual Computer
Computer
Tool 1
Computer
Tool 2
Computer
Tool 3
Computer
Tool 4
Authenticated
Computer
Tool
Computer
Tool
Computer
Tool
Computer
Tool
Computer
Tool
Student Instructor
Act
Infer
Report
Event
Dataset
Personal
Data
Encoding
Abelardo Pardo Analytics for decision making in Learning Environments 31
32. deepwarren Flickr
Closer to the student
Abelardo Pardo Analytics for decision making in Learning Environments 32
33. From App to companion
Finished focus group. Potential for student
uptake.
Abelardo Pardo Analytics for decision making in Learning Environments 33
34. puthoOr Photography flickr.com
LMS interactions, quizzes, response times, sessions,
hints requested, questionnaires, interviews, requests,
demographics, high school grades, etc.
(Bienkowski, M., Feng, M., & Means, B., 2012)
Abelardo Pardo Analytics for decision making in Learning Environments 34
35. Naezmi flickr.com
Learning Record Stores. Tin Can API
Abelardo Pardo Analytics for decision making in Learning Environments 35
36. Space & Light Flickr
• Data far away from instructor
• Needs curation process
Abelardo Pardo Analytics for decision making in Learning Environments 36
37. Tristan Martin flickr.com
Learning
Analytics Collecting
Observations
Analysis and
Visualization
Abelardo Pardo Analytics for decision making in Learning Environments 37
38. www.snappvis.org (Last accessed Nov. 2014)
Bookmarklet in your browser
Abelardo Pardo Analytics for decision making in Learning Environments 38
39. SAM
(Govaerts et al., 2012)
Abelardo Pardo Analytics for decision making in Learning Environments 39
41. Student Engagement with Videos
Flipped Classroom: Preparation activities
Abelardo Pardo Analytics for decision making in Learning Environments 41
42. MCQ answers
Abelardo Pardo Analytics for decision making in Learning Environments 42
43. Mr. Velocipede flickr.com
Statistical
analysis
(small)
Predictive
algorithms
Clustering
Algorithms
Relationship
Mining
Data Distillation Discovery
with
Models
(Baker and Yacef, 2009)
Abelardo Pardo Analytics for decision making in Learning Environments 43
44. (Macfadyen & Dawson, 2010)
Abelardo Pardo Analytics for decision making in Learning Environments 44
45. Clustering, Classification, Rule inference
(Romero, Ventura & García, 2008)
Abelardo Pardo Analytics for decision making in Learning Environments 45
46. Tristan Martin flickr.com
Learning
Analytics Collecting
Observations
Analysis and
Visualization
Interventions
Abelardo Pardo Analytics for decision making in Learning Environments 46
47. DullHunk flickr.com
Reduce attrition. Phone 10 students this semester.
Which students? When?
Abelardo Pardo Analytics for decision making in Learning Environments 47
48. Abelardo Pardo Analytics for decision making in Learning Environments 48
neilspencerbruce flickr.com
What aspects to personalize?
49. colecamp flickr.com
A/B Testing: Space usage after change in
course/curriculum
Abelardo Pardo Analytics for decision making in Learning Environments 49
51. Design Principles for Interventions
• Integrated with the activity. Connected with
concepts and assessment.
• Interventions must support learning, not
become an additional burden.
• Provide measures that are considered
desirable and why.
• Discuss the process with the students.
(Wise, 2014)
Abelardo Pardo Analytics for decision making in Learning Environments 51
52. Tristan Martin flickr.com
Learning
Analytics Collecting
Observations
Analysis and
Visualization
Interventions Institutional
Adoption
Abelardo Pardo Analytics for decision making in Learning Environments 52
53. Secretlondon123 flickr.com
1 Context
2 Questions
3 Data + algorithms
4 Level of intervention
Abelardo Pardo Analytics for decision making in Learning Environments 53
54. measure Joe flickr.com
1 Context: Student
retention
2 Questions: Why do
they leave? Can it be
avoided?
3 Data + algorithms: ?
4 Level of intervention: ?
Abelardo Pardo Analytics for decision making in Learning Environments 54
55. Shaylor flickr.com
1 Context: Student success
2 Questions: How did they
learn?
3 Data + algorithms: ?
4 Level of intervention: ?
Abelardo Pardo Analytics for decision making in Learning Environments 55
56. Salfalko flickr.com
Data Wrangler: someone comfortable handling
statistics, manipulating data for visualisation, and
capable of engaging with academics about the student
experience and course design. It is their job to
experiment with different tools to interpret, visualise
and share information with academics as a basis for
gaining actionable insights.
(Powell & MacNeil, 2012)
Abelardo Pardo Analytics for decision making in Learning Environments 56
57. Kris Krüg Flickr
Analytics for decision making in
Learning Environments
Dr Abelardo Pardo (@abelardopardo)
The University of Sydney
slideshare.net/abelardo_pardo
IARU EdTech Horizons Workshop
Singapore, 14 November 2014
58. References
Baker, R. S. J. D., and Yacef, K., 2009.
The state of educational data mining in 2009: A review and future visions.
Journal of Educational Data Mining, 1(1), 3–17.
Bienkowski, M., Feng, M., & Means, B. (2012).
Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics. US
Department of Education.
Liddo, A. De, Shum, S., Quinto, I. (2011)
Discourse-centric learning analytics.
In Proceedings of the International Conference on Learning Analytics and Knowledge.
Felder, R. M. (2006).
The Way to Bet.
Chemical Engineering Education, 40(1), 38–39.
Govaerts, S., Verbert, K., Duval, E., & Pardo, A. (2012).
The Student Activity Meter for Awareness and Self-reflection.
In ACM SIGCHI International Conference on Human Factors in Computing Systems (pp. 869–884).
Macfadyen, L. P., & Dawson, S. (2010).
Mining LMS data to develop an “early warning system” for educators: A proof of concept.
Computers & Education, 54(2), 588–599
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
Abelardo Pardo Analytics for decision making in Learning Environments 58
59. References II
Powell, S., & MacNeil, S. (2012).
Analytics Series Institutional Readiness for Analytics (Vol. 1, pp. 1–11).
JISC Center for Educational Technology & Interoperability Standards, Analytics Series, 1(8).
Shum, S. B., and Crick, R. D. (2012)
Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics.
In S. Buckingham Shum, D. Gaševi´c, and R. Ferguson (Eds.), International Conference on Learning
Analytics and Knowledge (pp. 92–101). ACM Press.
Siemens, G., & Long, P. (2011).
Penetrating the Fog: Analytics in Learning and Education.
Educause Review, 48(5),
Wise, A., 2014
Designing Pedagogical Interventions to Support Student Use of Learning Analytics.
In A. Pardo & S. D. Teasley (Eds.), Proceedings of the International Conference on Learning Analytics and
Knowledge. ACM Press.
Abelardo Pardo Analytics for decision making in Learning Environments 59