The document discusses the future of learning and how data can be leveraged to improve learning for most people. It outlines using data to recognize excellence in teaching and learning, provide real-time support, and identify effective collaborations. A case study is described that used an intelligent tutoring system to construct student models and provide feedback based on past student data. Guiding principles of respect, beneficence, and justice are presented for developing learning systems.
Open Learning Analytics panel at Open Education Conference 2014Stian Håklev
The past five years have seen a dramatic growth in interest in the emerging field of Learning Analytics (LA), and particularly in the potential the field holds to address major challenges facing education. However, much of the work in the learning analytics landscape today is closed in nature, small in scale, tool- or software-centric, and relatively disconnected from other LA initiatives. This lack of collaboration, openness, and system integration often leads to fragmentation where learning data cannot be aggregated across different sources, institutions only have the option to implement "closed" systems, and cross disciplinary research opportunities are limited. Beyond the immediate concerns this fragmentation creates for educators and learners, a closed approach dramatically limits our ability to build upon successes, learn from failures and move beyond the "pockets of excellence (and failures)? approach that typifies much of the educational technology landscape.
The potential benefits of openness as a core value within the learning analytics community are numerous. Learning initiatives could be informed by large scale research projects. Open-source software, such as dashboards and analytics engines, could be available free of licensing costs and easily enhanced by others, and OERs could become more personalized to match learners' needs. Open data sets and reproducible papers could rapidly spread understanding of analytical approaches, enabling secondary analysis and comparison across research projects. To realize this future, leaders within the learning analytics, open technologies (software, standards, etc.), open research (open data, open predictive models, etc.) and open learning (OER, MOOCs, etc.) fields have established a "network of practice" aimed at connecting subject matter experts, projects, organizations and companies working in these domains. As an initial organizing event, these leaders organized an Open Learning Analytics (OLA) Summit directly following the 2014 Learning Analytics and Knowledge (LAK) conference this past March as means to further the goal of establishing "openness' as a core value of the larger learning analytics movement. Additional details on the Summit and those involved can be found at: http://www.prweb.com/releases/2014/04/prweb11754343.htm.
This panel session will bring together several thought leaders from the Open Learning Analytics community who participated in the Summit to facilitate an interactive dialog with attendees on the intersection of learning analytics and open learning, open technologies, open data, and open research. The presenters represent a broad range of experience with institutional analytics projects, an open source development consortium, the sharing of open learner data, and academic research on open learning environments.
This presentation to the MoodleMoot UK/I 2017 provides an overview of Learning Analytics for VLE/LMS data and lessons learned in practice from using this data to model student risk and other characteristics. The findings come from fundamental research and application of Blackboard's X-Ray Learning Analytics application.
Learning Analytics: What is it? Why do it? And how?Timothy Harfield
Presentation delivered to graduate students at Emory University as part of a TATTO (Teaching Assistant Training and Teaching Opportunity) brown bag session.
ABSTRACT
Learning analytics is the 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. Data driven approaches to teaching and learning are rapidly being adopted within educational environments, but there is still much confusion about what learning analytics is, what it can do, and how it is best employed.
This talk will provide a general overview of the field of learning analytics, its terminology and methods, as well as contemporary ethical debates. It will also introduce several open source and Emory-supported analytics tools available to students and instructors to facilitate the achievement of various learning outcomes.
Open Learning Analytics panel at Open Education Conference 2014Stian Håklev
The past five years have seen a dramatic growth in interest in the emerging field of Learning Analytics (LA), and particularly in the potential the field holds to address major challenges facing education. However, much of the work in the learning analytics landscape today is closed in nature, small in scale, tool- or software-centric, and relatively disconnected from other LA initiatives. This lack of collaboration, openness, and system integration often leads to fragmentation where learning data cannot be aggregated across different sources, institutions only have the option to implement "closed" systems, and cross disciplinary research opportunities are limited. Beyond the immediate concerns this fragmentation creates for educators and learners, a closed approach dramatically limits our ability to build upon successes, learn from failures and move beyond the "pockets of excellence (and failures)? approach that typifies much of the educational technology landscape.
The potential benefits of openness as a core value within the learning analytics community are numerous. Learning initiatives could be informed by large scale research projects. Open-source software, such as dashboards and analytics engines, could be available free of licensing costs and easily enhanced by others, and OERs could become more personalized to match learners' needs. Open data sets and reproducible papers could rapidly spread understanding of analytical approaches, enabling secondary analysis and comparison across research projects. To realize this future, leaders within the learning analytics, open technologies (software, standards, etc.), open research (open data, open predictive models, etc.) and open learning (OER, MOOCs, etc.) fields have established a "network of practice" aimed at connecting subject matter experts, projects, organizations and companies working in these domains. As an initial organizing event, these leaders organized an Open Learning Analytics (OLA) Summit directly following the 2014 Learning Analytics and Knowledge (LAK) conference this past March as means to further the goal of establishing "openness' as a core value of the larger learning analytics movement. Additional details on the Summit and those involved can be found at: http://www.prweb.com/releases/2014/04/prweb11754343.htm.
This panel session will bring together several thought leaders from the Open Learning Analytics community who participated in the Summit to facilitate an interactive dialog with attendees on the intersection of learning analytics and open learning, open technologies, open data, and open research. The presenters represent a broad range of experience with institutional analytics projects, an open source development consortium, the sharing of open learner data, and academic research on open learning environments.
This presentation to the MoodleMoot UK/I 2017 provides an overview of Learning Analytics for VLE/LMS data and lessons learned in practice from using this data to model student risk and other characteristics. The findings come from fundamental research and application of Blackboard's X-Ray Learning Analytics application.
Learning Analytics: What is it? Why do it? And how?Timothy Harfield
Presentation delivered to graduate students at Emory University as part of a TATTO (Teaching Assistant Training and Teaching Opportunity) brown bag session.
ABSTRACT
Learning analytics is the 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. Data driven approaches to teaching and learning are rapidly being adopted within educational environments, but there is still much confusion about what learning analytics is, what it can do, and how it is best employed.
This talk will provide a general overview of the field of learning analytics, its terminology and methods, as well as contemporary ethical debates. It will also introduce several open source and Emory-supported analytics tools available to students and instructors to facilitate the achievement of various learning outcomes.
Learning Analytics: Seeking new insights from educational dataAndrew Deacon
CPUT Fundani TWT - 22 May 2014
Analytics is a buzzword that encompasses the analysis and visualisation of big data. Current interest results from the growing access to data and the many software tools now available to analyse this data in Higher Education, through platforms such as Learning Management Systems. This seminar provides an overview of current applications and uses of learning analytics and how it can help institutions of learning better support their learners. The illustrative examples look at institutional and social media data that together provide rich insights into institutional, teaching and learning issues. A few simple ways to perform such analytics in a context of Higher Education will be introduced.
Educational Data Mining/Learning Analytics issue brief overviewMarie Bienkowski
An overview of the Draft Issue Brief prepared by SRI International for the US Department of Education on Educational Data Mining and Learning Analytics
Speakers:
David Lewis, senior analytics consultant, Jisc
Martin Lynch, learning systems manager, University of South Wales
An opportunity to find out about how an institution has been implementing learning analytics to support the student journey with and opportunity to discuss issues and possibilities that the use of learning analytics may create.
The Impact of Open Textbooks in the USA and South Africa: When? Why? How?OER Hub
These slides accompanied the OER Research Hub webinar "The Impact of Open Textbooks in the USA and South Africa: When? Why? How?" on 28 May 2014. Speakers: Megan Beckett (Siyavula), Beck Pitt (The Open University, OER Research Hub) and Daniel Williamson (OpenStax College). The session was chaired by Martin Weller (The Open University, OER Research Hub).
You can watch a recording of the webinar here: http://tinyurl.com/p926br2
'Learning design & learning analytics – building the links', presented by Rebecca Ferguson at 'What the Research Says' seminar held at the London Knowledge Lab on 28 November 2014.
A Systematic Analysis And Synthesis of the Empirical MOOC Literature Publishe...George Veletsianos
A deluge of empirical research became available on MOOCs in 2013-2015 and this research is available in disparate sources. This paper addresses a number of gaps in the scholarly understanding of MOOCs and presents a comprehensive picture of the literature by examining the geographic distribution, publication outlets, citations, data collection and analysis methods, and research strands of empirical research focusing on MOOCs during this time period. Results demonstrate that: more than 80% of this literature is published by individuals whose home institutions are in North America and Europe; a select few papers are widely cited while nearly half of the papers are cited zero times; and researchers have favored a quantitative if not positivist approach to the conduct of MOOC research, preferring the collection of data via surveys and automated methods. While some interpretive research was conducted on MOOCs in this time period, it was often basic and only a handful of studies were informed by methods traditionally associated with qualitative research (e.g., interviews, observations, focus groups). Analysis shows that there is limited research reported on instructor-related topics, and that even though researchers have attempted to identify and classify learners into various groupings, very little research examines the experiences of learner subpopulations.
Teaching in MOOCs: Unbundling the roles of the educatorRebecca Ferguson
Teaching in MOOCs: Unbundling the roles of the educator, a presentation given at the design4learning conference at The Open University, Milton Keynes, UK by Rebecca Ferguson (co-authored with Denise Whitelock) on 26 November 2014.
Using Data to Drive Personalized Math Learning NeedsDreamBox Learning
Technologies to support data-driven decision-making hold great promise for increasing the effectiveness of teaching and learning activities, accelerating student achievement, and improving organizational performance. To access what students are learning and how they are progressing, educators can now use a continuous improvement framework for data-driven decision-making to organize people and processes to reach education objectives.
Join us for this webinar and discuss topics including:
• Building a sustainable data analysis framework
• Common challenges involved in establishing data-driven practices
• Incorporating blended learning environments to meet school goals
Learning Analytics: Seeking new insights from educational dataAndrew Deacon
CPUT Fundani TWT - 22 May 2014
Analytics is a buzzword that encompasses the analysis and visualisation of big data. Current interest results from the growing access to data and the many software tools now available to analyse this data in Higher Education, through platforms such as Learning Management Systems. This seminar provides an overview of current applications and uses of learning analytics and how it can help institutions of learning better support their learners. The illustrative examples look at institutional and social media data that together provide rich insights into institutional, teaching and learning issues. A few simple ways to perform such analytics in a context of Higher Education will be introduced.
Educational Data Mining/Learning Analytics issue brief overviewMarie Bienkowski
An overview of the Draft Issue Brief prepared by SRI International for the US Department of Education on Educational Data Mining and Learning Analytics
Speakers:
David Lewis, senior analytics consultant, Jisc
Martin Lynch, learning systems manager, University of South Wales
An opportunity to find out about how an institution has been implementing learning analytics to support the student journey with and opportunity to discuss issues and possibilities that the use of learning analytics may create.
The Impact of Open Textbooks in the USA and South Africa: When? Why? How?OER Hub
These slides accompanied the OER Research Hub webinar "The Impact of Open Textbooks in the USA and South Africa: When? Why? How?" on 28 May 2014. Speakers: Megan Beckett (Siyavula), Beck Pitt (The Open University, OER Research Hub) and Daniel Williamson (OpenStax College). The session was chaired by Martin Weller (The Open University, OER Research Hub).
You can watch a recording of the webinar here: http://tinyurl.com/p926br2
'Learning design & learning analytics – building the links', presented by Rebecca Ferguson at 'What the Research Says' seminar held at the London Knowledge Lab on 28 November 2014.
A Systematic Analysis And Synthesis of the Empirical MOOC Literature Publishe...George Veletsianos
A deluge of empirical research became available on MOOCs in 2013-2015 and this research is available in disparate sources. This paper addresses a number of gaps in the scholarly understanding of MOOCs and presents a comprehensive picture of the literature by examining the geographic distribution, publication outlets, citations, data collection and analysis methods, and research strands of empirical research focusing on MOOCs during this time period. Results demonstrate that: more than 80% of this literature is published by individuals whose home institutions are in North America and Europe; a select few papers are widely cited while nearly half of the papers are cited zero times; and researchers have favored a quantitative if not positivist approach to the conduct of MOOC research, preferring the collection of data via surveys and automated methods. While some interpretive research was conducted on MOOCs in this time period, it was often basic and only a handful of studies were informed by methods traditionally associated with qualitative research (e.g., interviews, observations, focus groups). Analysis shows that there is limited research reported on instructor-related topics, and that even though researchers have attempted to identify and classify learners into various groupings, very little research examines the experiences of learner subpopulations.
Teaching in MOOCs: Unbundling the roles of the educatorRebecca Ferguson
Teaching in MOOCs: Unbundling the roles of the educator, a presentation given at the design4learning conference at The Open University, Milton Keynes, UK by Rebecca Ferguson (co-authored with Denise Whitelock) on 26 November 2014.
Using Data to Drive Personalized Math Learning NeedsDreamBox Learning
Technologies to support data-driven decision-making hold great promise for increasing the effectiveness of teaching and learning activities, accelerating student achievement, and improving organizational performance. To access what students are learning and how they are progressing, educators can now use a continuous improvement framework for data-driven decision-making to organize people and processes to reach education objectives.
Join us for this webinar and discuss topics including:
• Building a sustainable data analysis framework
• Common challenges involved in establishing data-driven practices
• Incorporating blended learning environments to meet school goals
The study examines the efficacy of the free software Socrative in:
- Enhancing attendance taking routines
- Improving engagement and participation
- Improving learning outcomes
- Enhancing process of course preparation
- Underscore the importance of the 7 Principles of Undergraduate Teaching and Learning
Best Practices in Higher Education - Role of Commerce & Management Teachersgpsudhakaar
Workshop on Best Practices in Higher Education - Role of Commerce & Management Teachers for the Commerce and Teachers Association of the Women's University Vijayapura
This presentation was given at the 2010 Leadership for Equity and Excellence Forum - Reinvesting in Equity: Building Bridges and Tearing Down Walls in Phoenix, AZ
From theory to practice blending the math classroom and creating a data cultu...DreamBox Learning
Transitioning your school to a fully blended model that leverages data to inform school wide goals, drive classroom instruction, and form small groups takes time and buy-in. Whether you’re in the beginning stages of your blended journey, or are several years into it, it’s important to stay dynamic and reflective to ensure your blended initiative is having a positive impact on student success. Hear how Aldeane Comito Ries Elementary was able to take data beyond the classroom and continue to successfully incorporate it into their school’s infrastructure.
Join the staff at Aldeane Comito Ries Elementary to hear about how they:
• Received buy-in from their staff at all levels
• Specifically use data in their day-to-day
• Continue to transform classroom teaching and learning
Many institutions see technology as a strategy to increase revenues and decrease campus-bases classrooms and resources. However, as emerging technologies shift the course from teaching-centered to learning-centered, historically effective strategies may no longer provide the same return on investment. This session examines how we can maximize the return on value of technology to increase learner engagement, add instructional options, and improve faculty efficacy.
Similar to Tiffany Barnes "Making a meaningful difference: Leveraging data to improve learning for most of people most of the time" (20)
Keynote 1: Teaching and Learning Computational Thinking at ScaleCITE
Title: Teaching and Learning Computational Thinking at Scale
Speaker:
Prof. Ting-Chuen PONG, Professor, Computer Science & Engineering Department, The Hong Kong University of Science and Technology
Time:
09:45-10:45, 9 June 2018 (Saturday)
Venue:
Rayson Huang Theatre, The University of Hong Kong
Sub-theme:
Computational Thinking
Chair:
Prof. Nancy Law, Deputy Director, CITE, Faculty of Education, The University of Hong Kong
http://citers2018.cite.hku.hk/program-highlights/keynote-pong/
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Title: Social Epistemic Cognition in Engineering Learning: Theory, Pedagogy, and Analytics
Speaker:
Prof. Rosanna Yuen-Yan Chan, Member-at-Large, Board of Governors, IEEE Education Society
Department of Information Engineering, The Chinese University of Hong Kong
Time:
14:15-15:15, 9 June 2018 (Saturday)
Venue:
Rayson Huang Theatre, The University of Hong Kong
Sub-theme:
Learning design and learning analytics
Chair:
Dr. Gary Wong, Faculty of Education, The University of Hong Kong
http://citers2018.cite.hku.hk/program-highlights/keynote-chan/
Prof. Gerald KNEZEK: Implications of Digital Generations for a Learning Society CITE
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Speaker: Prof. Gerald Knezek, University of North Texas
Time: 14:30 – 15:30, 29 May 2015 (Friday)
Venue: Room 408A, 409A & 410, 4/F, Meng Wah Complex, The University of Hong Kong
citers2015.cite.hku.hk/keynote-knezek/
Invited Talk: Open Access: Promises and Reality
Speakers: Mr. Peter E SIDORKO, University Librarian, HKU; Mr. Fred CHAN, Research and Data Services Librarian, HKU
Time: 10:00-10:30, 29 May 2015 (Friday)
Venue: Room 408A, 409A & 410, 4/F, Meng Wah Complex, The University of Hong Kong
http://citers2015.cite.hku.hk/program-highlights/talk-sidorko/
Invited Talk:
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Speaker: Dr. David Gibson, Curtin University
Time: 9:15 – 10:00, 29 May 2015 (Friday)
Venue: Room 408A, 409A & 410, 4/F, Meng Wah Complex, The University of Hong Kong
http://citers2015.cite.hku.hk/program-highlights/talk-gibson/
Analogy, Causality, and Discovery in Science: The engines of human thoughtCITE
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12:45 pm – 2:00 pm
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http://sol.edu.hku.hk/analogy-causality-discovery-science-engines-human-thought/
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12:45 pm – 2:00 pm
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17 January 2015, Saturday
2:30 pm – 4:00 pm
Rayson Huang Theater, HKU
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http://sol.edu.hku.hk/petitto-2015/
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Tiffany Barnes "Making a meaningful difference: Leveraging data to improve learning for most of people most of the time"
1. Making a meaningful
difference: Leveraging data
to improve learning for most
of people most of the time
LASI 2014 Keynote, Dr.Tiffany Barnes, NCSU
(Presenter: Dr. Chi-Un Lei, HKU)
1
2. Outline
The Future of Learning
Getting there
Case Study: Intelligent tutoring system (2009-2013)
Skipped technical discussions
Guiding Principles
2
3. The Future of Learning
Recognizing and promoting excellence in teaching and
learning
Non-intrusive model to recognize mastery, commitment,
engagement, mentoring and teaching potential in learners
Combined with detectors that recognize student needs
Real time support for effective culture
Identified when/where potential collaborators are working
on similar tasks and pairs them according to maximum
likelihood of a beneficial peer learning relationship
Hints on how the interaction can be most helpful
4. The Future of Learning
Blurring the boundary between teachers and
learners
Learner promoted to become tutors and content
creators
Knowledge modeling to constantly maintain flow during
learning while detecting the needs of learning
4
5. Getting There
Achievements
Knowledge models portable, sharable, transparent to
students
Integrate with learning systems like those with games
Detectors constantly updating achievements
Diverse learning environments
Forum, wikis, labs, assessments, tutorials, readings
EDM models informed from all
6. Getting There
Relationships
Detecting features that predict effective
teacher/learner or peer tutor/mentee relationships
Providing scaffolds to continually support these
Focused around learning activities of current interest
to users
But allowing for off-task activities that strengthen
relationships and recognize that learners and teachers
are people
6
7. Case Study
CAREER: Educational Data Mining for Student
Support in Interactive Learning Environments
NSF-IIS (2009-2013)
Intelligent tutoring system
Use student data to construct models that represent
student solutions
Trace student behavior in the model
Provide feedback and hints based on past records from
students
7
8. Technical Methodology
Data-derived model tracer
Graph answer of students
Calculate transition probabilities
Reward good solutions and penalize errors
What does this give us?
Likely paths students take
A value for each state - This value is important
Use to provide help in the form of hints
8
9. What types of tutors?
General: Problem solving
Maths. (Algebra, Geometry, Logic, Induction)
Science (Chemistry and Physics)
Language and reading
Field test: Over 200 students per year
Discrete Math
Logic and Algorithms
Students have difficulty developing strategies to solve
proofs
9
12. How to Generate a Hint Sequence
To generate hints
Suggest the next state with the highest value
Generate hints from the state features of that state
To create a hint sequence
Indicate a goal expression to derive
Indicate the statements that should be used
Indicate the rule to apply next
12
13. How Often Will a Hint Be Available?
Experiment of four semesters of past data
523 valid student attempts
381 (73%) were complete, 142 (27%) were partially
complete
Over all steps, hints only available for 45% of moves
However, 90% of Hint Requests were successful
More problems were completed with hints
13
15. How to Determinate Master Learning?
Assume that students who complete a system
have mastered it
Break down the system data into intervals
Model learning at the end of each interval
Compare new students to exemplar model to
determine mastery
15
17. Guiding Principles
Respect
Personalized models and adaptive contents
“People can offer to the learning environment and to one another”
Beneficence
Look for practical effect sizes
Move towards standardized data models and methods
Maximize potential of research to result in positive changes in
educational systems
Justice
Consider equality in developing and deploying systems
Many ways to demonstrate (and measure) proficiency/mastery