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 zzosang@keris.or.kr FB: /zzosang Twitter: @zzosang