learning analytics - vision of the future
TRANSCRIPT
The Future of Learning Analytics
Tore HOEL | Oslo and Akershus University College of Applied Sciences
Kyushu University, Japan | 17 November 2015#laceproject, @tore
Working at the largest state university college in Norway.
I work mainly with European projectson Learning Analytics and Open Education
LT Standardisation & Interoperability
Who am I?
Tore Hoel
Research interests
• Learning Technology Interoperability Standards – development and governance
• Learning Analytics and Interoperability – scaling up LA
• Data Sharing, Privacy and Data Protection
Some papers:hoel.nu/publications
The LACE project
K12 Workplace
HEI Community-building through events &
communication channels/social media(cross-disciplinary Higher Ed, K12, & Workplace)
Technology transfer & best practice Organizes events, and contributes to
tutorials, workshops, conferences, etc.
European support action aimed at integrate communities working on LA from schools, workplace and universities
LACE Project is supported by the European Commission Seventh Framework Programme, grant 619424.
LACE Goals and objectives
• Objective 1 – Promote knowledge creation and exchange
• Objective 2 – Increase the evidence base
• Objective 3 – Contribute to the definition of future directions
• Objective 4 – Build consensus on interoperability and data sharing
www.laceproject.eu
Learning Analytics – What?
The measurement, collection, analysis and reporting of data about learners and their contexts…
Learning Analytics – Why?
…for purposes of understanding and optimizing learning and the environments in which it occurs.
Implementation of Learning Analytics
Learning environment
Educators
LearnersStudent
Information Systems
Source: Dragan Gašević
Blogs
Videos/slides
Mobile
Search
Educators
Learners
Networks
Student Information
Systems
Learning environment
Source: Dragan Gašević
Blogs
Mobile
Search
Networks
Educators
LearnersStudent
Information Systems
Learning environment
Videos/slidesSource: Dragan Gašević
Learning Analytics Deployment Maturity
Siemens, G., Dawson, S., & Lynch, G. (2014). Improving the Quality and Productivity of the Higher Education Sector – Policy and Strategy for Systems-Level Deployment of Learning Analytics. Canberra, Australia: Office of Learning and Teaching, Australian Government. Retrieved from http://solaresearch.org/Policy_Strategy_Analytics.pdf
Source: http://solaresearch.org/Policy_Strategy_Analytics.pdf
Visions of the Future
Why envisioning the future of learning analytics? We are interested in indications of the future of
learning analytics:− To provide guidance for policy makers− To help coordinate research
We have described possible futures of learning analytics (visions)− Conceivable with current technology, but
challenging in their implications
Read the visions – take part in the survey! The visions:
https://www.surveymonkey.com/r/Lace-Visions
You don't need to do all of the visions!
Vision 1: In 2025, LA are essential tools for educational management
Pic by: Janneke Staaks, https://www.flickr.com/photos/jannekestaaks/14204590229/
• A wide range of data about learner behaviour is used
• This generates good quality, real-time predictions about likely study success
• Learners, teachers, managers and policymakers have access to live information
• You don’t have to wait to see if a course is booming or failing
Vision 2: In 2025, LA analytics support self-directed autonomous learning
Pic by: SparkFun, https://www.flickr.com/photos/sparkfun/4536382170/
• No Curricula anymore• Students create study groups
that decide their learning goals and how to achieve these
• Analytics support exchange of information and group collaborations
• Teachers become MENTORS • Formative assessment is
used to guide future progress towards learning goals
Vision 3: In 2025, analytics are rarely used in education
Pic by: Tara Hunt, https://www.flickr.com/photos/missrogue/94403705
• Courses that are automated by analytics are seen as inferior
• Learners have realised that they can game the system
• There have been major leaks and misuse of sensitive personal data
• All use of data for educational purposes has to be approved not only by the learner but also by new inspectorates.
Vision 4: In 2025, classrooms monitor the physical environment to support learning and teaching
Pic by: SparkFun, https://www.flickr.com/photos/sparkfun/4536382170/
• Furniture, pens, writing pads – almost any tool used during learning – can be fitted with sensors.
• Cameras monitor movements, and record exactly how learners work with and manipulate objects.
• Information is used to monitor learners’ progress.
• Teachers are alerted to signs of individual learner’s boredom, confusion, and deviation from task.
Vision 5: In 2025, most teaching is delegated to computers
Pic by: Charis Tsevis, https://farm6.staticflickr.com/5215/5470451264_c0612f2102_z_d.jpg
• Aggregation of enormous datasets containing information about hundreds of thousands of learners
• It is possible to provide reliable evidence-based recommendations about the most successful routes to learning
• Recommendations are better informed and more reliable than by even the best-trained humans
Vision 6: In 2025, personal data tracking supports learning
Pic by: SparkFun, https://www.flickr.com/photos/sparkfun/4536382170/
• Sensors gather personal information about factors such as posture, attention, rest, stress, blood sugar, metabolic rate, etc.
• This data helps people to master skills as swimming, driving, and passing examinations
• Programmers use this data to optimise learning for different ages and courses
Vision 7: In 2025, individuals control their own data
Pic by: Gideon Burton, https://www.flickr.com/photos/wakingtiger/3157622608
• People are aware of the importance and value of their data.
• Learners control the type and quantity of personal data that they share, and with whom they share it
• If they do not engage with these tools, then no data is shared and no benefits gained.
• Most educational institutions run campaigns to raise awareness of the risks and exposure of data
2025, open systems for learning analytics are widely adopted
Pic by: Gideon Burton, https://www.flickr.com/photos/wakingtiger/3157622608
• Algorithms and models are shared openly
• Educational institutions demand control of the tools they use
• Date sharing according to agreed set of standards
• Well-tested, accessible and standardised visualisation methods are used
http://jasla.jp/
理事長 田村恭久
上智大学理工学部教授1987年上智大学大学院前期課程修了。同年日立製作所システム開発研究所。 1993 年上智大学助手。1996 年博士（工学）。現在同准教授。専門分野はソフトウェア工学、ハードウェアアーキテクチャ、ドメイン分析などを経て、現在教育工学。 e ラーニング、協調学習、自然言語処理を用いた学習支援、プロトタイピング技法を用いたインストラクショナルデザインなどを研究。教育システム情報学会、日本 e ラーニング学会、日本教育工学会、情報処理学会、ヒューマンインタフェース学会等会員。
Thanks to:
Hendrik Drachsler, for sharing his slides about the LACE Vision of the Future study
LACE project is funded by European Commission 619424-FP7-ICT-2013-11
The European LACE project builds a Community of Interest on Learning Analytics – check out laceproject.eu
APSCE (Asian-Pacific Society for Computers in Education) has a Special Interest Group on Learning Analytics – join the community!
[email protected]/torehoel
@tore
This work was undertaken as part of the LACE Project, supported by the European Commission Seventh Framework Programme, grant 619424.
These slides are provided under the Creative Commons Attribution Licence: http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms.
www.laceproject.eu@laceproject