Prospect for learning analytics to achieve adaptive learning model
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Transcript of Prospect for learning analytics to achieve adaptive learning model
Prospect for learning analytics to achieve adaptive learning model
Yong-Sang CHO, Ph.D Principal researcher, KERIS
2015-10-16, Seoul, Korea
Table of Contents
• What is an adaptive learning
• Pathway to reach adaptive learning analytics
• Case study for exploring data flow and exchange
• Proof of concept: reference model for learning analytics
• Linked data for curriculum standards
• Future works by 2016
What is an adaptive learning?
Adaptive learning is a “sophisticated, data-driven, and in some cases, nonlinear approach to instruction and remediation, adjusting to a learner's interactions and demonstrated performance level, and subsequently anticipating what types of content and resources learners need at a specific point in time to make progress."
<Bill and Melinda Gates Foundation>
Source: http://educationgrowthadvisors.com/gatesfoundation
Two levels to adaptive learning technologies:
• the first platform reacts to individual user data and adapts instructional material accordingly,
• while the second leverages aggregated data across a large sample of users for insights into the design and adaptation of curricula.
Source: Horizon Report 2015 – Higher Education Edition http://www.nmc.org/publication/nmc-horizon-report-2015-higher-education-edition/
Resources
Analytics Curricula
Adaptive Learning
Pathway to reach Adaptive learning analytics
LMS/VLE Analytics Dashboard
Predictive Analytics
Adaptive Learning Analytics
* LMS/VLE Analytics Dashboard
ü Concept: Until recently, data logs were not in a format that non-technical users could interpret, but these are now rendered via a range of graphs, tables and other visualizations, and custom reports designed for consumption by learners, educators, administrators and data analysts Learners may get basic analytics such as how they are doing relative to the cohort average (e.g. test Learning Analytics scores, forum contributions, webinar participation)
ü Examples: LMS/VLE vendors provide examples and webinars about their analytics dashboards, and the enterprise analytics/BI vendors are contextualizing their products to the education market. Arizona State University reports that it is seeing returns on its investment in academic and learning analytics, including a “Student 360” program that integrates all that the institution knows about a student.
Example of Learning Analytics
<Source: Learning Analytics, UNESCO IITE (2012)>
* Predictive Analytics
ü Concept: One of the more advanced uses of analytics that generates huge interest is the possibility that from the pattern of learners’ static data (e.g. demographics; past attainment) and dynamic data (e.g. pattern of online logins; quantity of discussion posts) one can classify the trajectory that they are on (e.g. “at risk”; “high achiever”; “social learner”), and hence make more timely interventions (e.g. offer extra social and academic support; present more challenging tasks). Currently, one of the most reliable predictors of final exam results is still exam performance at the start of studies.
ü Examples: Work at Purdue University on the Course Signals software is well known. Signals provides a red/amber/green light to students on their progress. Their most recent evaluation reports: “Results thus far show that students who have engaged with Course Signals have higher average grades and seek out help resources at a higher rate than other students.”
Example of Learning Analytics
<Source: Learning Analytics, UNESCO IITE (2012)>
* Adaptive Learning Analytics
ü Concept : Adaptive learning platforms build a model of a learner’s understanding of a specific topic (e.g. algebra; photosynthesis; dental surgical procedures), sometimes in the context of standardized tests which dictate the curriculum and modes of testing. Naturally, dynamic modeling of learner cognition, and preparation of material for adaptive content engines, are far more resource intensive to design and build than conventional ‘dumb’ learning platforms.
ü Examples: Significant research and investment in intelligent tutoring systems and adaptive hypermedia are bringing web platforms to market with a high quality user experience, and this is likely to continue to be a growth area.
Example of Learning Analytics
<Source: Learning Analytics, UNESCO IITE (2012)>
Case Study for exploring Data flow and exchange
xAPI
Transcript/learning data can be delivered to LMSs, LRSs or reporting tools
Experience data
LMS: Learning Management System LRS: Learning Record Store
IMS Caliper
Source: New Architect for Learning (Rob Abel, 2014) http://www.slideshare.net/JEPAslide/day3-edupub-tokyoims?qid=76ce5d4a-1ccf-468f-a428-c652584c395a&v=default&b=&from_search=4
http://www.coursesmart.com/go/institutions/analytics
Proof of Concept: reference model for learning analytics - ISO/IEC 20748 Projects -
We want to see iceberg below to understand
what we didn’t know before!!!
• What is a general process for analytics?
• Do we define workflows beyond xAPI or IMS
Caliper?
• How do we prove the concept?
For exploring
Data Collection
Data Storing & Processing
Analyzing Visualization
Privacy Policy
Secure Data Exchange
Input Data Items for Learning Analytics
Outcome from Learning Analytics
Data Pro
cessing and
Analytics
Learning Activity
- Reading - Lectures - Quiz - Projects - Homework - Media - Tutoring - Research
- Assessment - Collaboration - Annotation - Gaming - Social - Messaging - Scheduling - Discussions
- Lecture (MOOCs, OER) - Material (reading, etc) - Learning tool - Quiz/Assessment Item - Discussion forum - Message - Social Network - Prior Credit - Achievement - System Log - ……
Learning & Teaching Activity
Personalization, Intervention and Prediction, etc
First layer of reference model for LA
(Basic analytics process: dashboard analytics)
1. Student open digital textbook on Readium-JS viewer
2. Data is generated through reading activities by student
3. Data capture API crawl the data and send to event store
4. On the analytics platform check collected data
5. See simple dashboard from collected data (without analysis algorithm)
(Advanced analytics process: predictive and adpative analytics)
6. Design analysis algorithm with data filtering from collected data
7. See advanced dashboard pertaining to customized analysis
8. Calculate personal learning path connected to LOD for curriculum standard
Demo scenario for LA
DEMO
Linked Data for Curriculum Standards
Goal of achievement
School level
Second criteria of science subject (second level)
Curriculum standard per school grade
Achievement statement (third level)
First criteria of science subject (top level)
Curriculum standards – US case
Area of content
Grade group Primary school 3-4 grade group Primary school 5-6 grade group
Middle school 1-3 grade group
Section
Curriculum standards – Korean case
Achievement statement – Korean case
Section of science subject (middle school)
Content of curriculum
Criteria of achievement Core achievement criteria
Reason and explanation for core achievement
CURRICULUM STANDARD
ACHIEVEMENT STATEMENT
hasChild isPartOf
hasChild
isChildOf
Structural model for curricula
Source: ASN Framework & ISO/IEC JTC1 SC36 N2140
CURRICULUM STANDARD
ACHIEVEMENT STATEMENT
hasChild isPartOf
alignFrom
alignTo
alignTo
alignFrom
hasChild
isChildOf
(derivedFrom)
(crossSubjectReference)
Semantic model for curricula
Source: ASN Framework & ISO/IEC JTC1 SC36 N2140
Linked Open Data for achievement statement
과학과 교육과정 (2009)
Future works by 2017
• Complete development for data capture API (beta version) - collaborate with IMS Global & ISO/IEC JTC1 SC36 * to improve efficiency of data sharing format
• Complete design and development for test-bed of reference model - complete test for open source SWs to organize optimized composition - design interface for analysis algorithm based on R
• Complete design for LOD of achievement standards - to connect digital resources with specific topics of curriculum standards * connected digital resources will be used ISO/IEC 19788 MLR
By February 2017
Thank You !!!
Korea Education & Research Information Service Yong-Sang CHO, Ph.D [email protected] FB: /zzosang Twitter: @zzosang