designing data enabled learning platforms

© 2016 Ness SES. All Rights Reserved 1

Designing for online learning environments in the age of big data. James&Williams,&&Director&of&Digital&Services&@jwilliams_uk&

Sawad&Brooks,&&Lead&User&Experience&Designer&@Rrrgggbbb&


Market overview – online learning today Education Theory How to leverage data Design approach > prototyped example Design principles


Online Learning Environments


Online learning is currently not engaging enough and loses people’s attention.

35%

56%

4% 5%

HarvardX and MITx - First Year Engagement (after sign-up)

Never engaged

Viewed less than half course content

Explored half or more of course content w/o certification

Earned certificates


When used as a tool to compliment traditional teaching in schools and universities it can have a negative effect…

30% of students in a class cannot be depended upon to have reviewed online material on their own …Teacher time is then spent “catching them up,” delaying the rest of students..


Online experiences are not as interactive as classrooms

Online When a student doesn’t understand a concept they replay the content. Repeating it does not necessarily make it more understandable.

vs

Classroom If a student has a question about a concept, the teacher will often attempt to reframe the concept in a different way.


Evolving online learning environments by leveraging data


- Trigger and drive improved learning experiences. - Provide Information on how students progress, to course correct them. - Enrich their study and present similar material in a different way.

Data and machine learning techniques can:


Length of dwell time Touches Verbal responses

Affective signs Initial assessment Bitesize assessment

Data analytics leads to finding behavioral patterns… which can improve the learning speeds of everyone

Data studied


“You don’t understand anything until you learn it more than one way.” – Marvin Minsky, Legendary AI scientist and Turing Award winner

We need to evolve understanding From linear content like books… To networked content like classroom discussion. Enabling Multimodal Learning increases cognitive processing ability.


Enable uploaded materials and their constituent components to relate or connect with each other and create a hierarchy of relatedness: Paragraphs, images, graphics, chapters, notes, videos, podcasts become closely linked and accessible. Students use what they prefer to learn from.

Componentizing networked content can enable multi modal learning over the linear experience


Within-document

Within-course

Cross-courses External Systems

Cross-documents

Networked componentized content uses different sources to enrich the learning experience.


Focus on learner needs to understand their ideal personalized learning journey


Focus on the needs of the learner to design the experience

Nancy Learner Needs

•  Clear outline of progression and achievement.

•  Real time understanding of knowledge strengths and gaps.

•  The right content and activity focus at the right time, tailored to me.

•  Different ways of processing the content

•  Access to variety of sources that enrich my learning.

Use proto personas


Enhance learner outcomes with a data enabled platform

Learning results Explore and process Assessment Immersive content consumption

Initial Learning assessment Stage


Focus on the learner goals and needs at each stage


Initial Learning assessment

•  The best way to learn for me

•  Learn and process •  Tips, hints and

guidance helpful

• Understand errors •  Tested on what is needed • Understand my learning status • Guidance to research content

• Create a depth of knowledge • Solidify learnings

• Understand status

• Plan course of action

Learner goals and

needs

Stage


Explore the opportunities that your data sources provide





guidance helpful





Learner goals and

needs

Stage

Data driven platform

opportunities

Personalized order of learning OPTIONS

Live content highlighting nudges connected to

your scoring

Real-time scoring results

Personalized assessments

Progression breakdown

Real-time learning recommendations

Related course content

Webcast event

Youtube/ 3rd party videos

User gen content recommendations

Blog posts/ Podcasts wiki

Topic forums

Personalized course next steps OPTIONS

Detailed course progression breakdown



Self evaluations Learning journey breakedown


Explore the different routes a personalized journey can take





guidance helpful





Learner goals and

needs

Stage

Data driven platform

opportunities

Personalized order of learning OPTIONS

Live content highlighting nudges connected to

your scoring

Real-time scoring results

Personalized assessments

Progression breakdown


Related course content

Webcast event

YouTube/ 3rd party videos


Blog posts/ Podcasts wiki

Topic forums

Personalized course next steps OPTIONS

Detailed course progression breakdown

Learner journey

Learner assessment

Personalized Course options 2

Personalized course option 1

Option 2

Option 1

Option 3

Assessment & scoring

Review progression



View related content 1

View related content 2




Personalized next steps option 2



Self evaluations Learning journey breakdown


Prototype the experience


Assessment to calibrate the system. Learners takes quick bitesize tests to customize their journey


Personalized content recommendations and learning routes based on aggregated data sources of information


A transparent learning platform Shows what information is gathered and clearly improves effectiveness with time with help from learner inputs.


Enable peer to peer communication between learners, teachers & subject matter experts to ask for help and support and promote engagement


Visualizing their progress students can see their success and where more effort is recommended and where to go to learn more


Principles of Designing for Learning Environments



Talk with the teachers and learners to understand their motivations, needs, goals and individual and collective behaviors. Fit the design learning model to the users, not the users to the model. Research before and test after. Create user facing stories for each algorithm to capture the motivation of the user and what action they might take from the information - so that the solution is always user-centric.


Make the actions and decisions of the learning product transparent so the learner understands the logic for what is happening. Remember that learning is fun – the engagement factor is paramount. From the start, create meaningful categories that let you make sense of the data, tell a story about the experience, and ensure you develop a way to share and discuss data in your organization; start by defining the basics together.
