Educational Technologies

What is coming next?Tuesday, 1 October 13

Dr Jason Zagamijason.zagami.info

Tuesday, 1 October 13

LecturerGriffith University

School of Education and Professional StudiesGold Coast

Educational Technologies

• Clouds / Virtual Labs / 3D Printing / Publishing

• Online / Mobile / VWorlds / AR / VR / Wearable

• Tablets / Surfaces / Gestures / Neural Interfaces

• Gaming / Gamification / Badging

TechnologicalInnovation

Lee Shulman

Pedagogical Knowledge

TechnologicalKnowledge

ContextTuesday, 1 October 13

Content Knowledge

Pedagogical Content Knowledge(PCK)

Content Knowledge

Matthew Koehler Punya Mishra

Content Knowledge

Technological Content

Knowledge(TCK)

Content Knowledge

Knowledge(TCK)

Technological Pedagogical Knowledge

Content Knowledge

Knowledge(TCK)

Technological Pedagogical Knowledge

Technological Pedagogical

Content Knowledge

(TPCK)

Learning Analytics

Pedagogical Diversity

LearningAnalytics

Technology/Pedagogy/Learning Analytics

• Boundaries of school based learning

• Ethics of crossing these boundaries

• Implications for life long learning

• Individual vs aggregated analysis

www.nmc.orgHorizon Reports

Cloud ComputingTechnological

Innovation

Currently 42% of schools 35% of K-12 IT Budgets, Savings of 20%

Innovation

• Collaboration• Conferencing• File storage• Virtualization• Processing Power• Scalability• Desktop - Laptop - Tablet - Mobile• Private cloud computing

Innovation

Cloud ComputingPedagogical

Diversity

• Multiple Authors• Activity Trails (Time and Content)• Autosave• Ownership (Content)• Ownership (Environment)

Virtual Laboratories• Students can make mistakes and repeat experiments• Record and replay experiments to analyse for areas of

improvement and acknowledge excellence• Often developed and linked with current research labs• Laboratory/Workshops work not limited to a schools

physical labs/workshops

Virtual LaboratoriesTechnological

Innovation

genome.edu.auTuesday, 1 October 13

Virtual LaboratoriesTechnological

Innovation

3D PrintingTechnological

Innovation

3D Printing

www.playmaker.org

3D Printing

diy.org

GamificationPedagogical

Diversity

BadgingPedagogical

Diversity

Online Learning

• Canada (Compulsory Offering)

• USA (Compulsory Completions)

• Khan Academy (4300 micro lectures)

• Computer Education ACS report

Online LearningPedagogical

Diversity

LMS’s vs PLN’s

• Teacher/Institution Centred vs Learner Centred

• Blackboard/Moodle vs Social Media

• SCORM -> Tin Can API

• Platform Reliance

LMS’s vs PLN’sPedagogical

Diversity

MOOCs• cMOOC vs xMOOC

• Global market and commercialisation

• MOOC’s vs Communities of Practice

• Publishers/BB

Learning AnalyticsSocial Networks Adapting Pedagogical Practice

LearningAnalytics

Learning AnalyticsWolfram|Alpha Analytics for Facebook

LearningAnalytics

PanopticonLearningAnalytics

Automated Feedback

• aka Assessment

• Targeted/Dynamic Assessment

• Peer Assessments

• Essay Grading

• Feedback Analysis

LearningAnalytics

Publishing• ePub

• iBooks -> OSX

• CC SA and closed LMS’s

• Teacher published texts

• Publishing companies / Academic publishing

• Integration of texts with LMS/LA access/quizzes

Publishing

• Reading time

• Reading rate

• Eye tracking

• Pupil dilation

LearningAnalytics

Open ContentPedagogical

Diversity

Open ContentPedagogical

Diversity

Open LearningPedagogical

Diversity

Open LearningPedagogical

Diversity

CurationPedagogical

Diversity

Individualised Support

Learning Analytics

LearningAnalytics

Mobile

• Price points <$50 Tablets and netbooks

• Realistically ~$100

• Ubiquitous processing and connectivity

• Magazines, books, all electronic devices

• Video/Audio/Location monitoring

AppsPedagogical

Diversity

• Small applications, low cost

• Learning objects ($$$$ TLF/ESA)

• SCORM -> Tin Can API

• Purchasing and licencing models

• Micro-transactions

Micro PurchasesPedagogical

Diversity

Meganets• Closed Meganets (App stores) but

developers cross networks

• Closing the internet (RSS, country filtering/tariff protections)

• Discriminatory pricing

• Educational restrictions (sector LMS’s / ESA -> country restrictions)

• HTML5 (reduced reliance on Apps)

DataloggingLearningAnalytics

Health MonitoringLearningAnalytics

Remote Health MonitoringLearningAnalytics

LearningAnalytics

Improvement MonitoringLearningAnalytics

Attention/Stress MonitoringLearningAnalytics

RFDI TrackingLearningAnalytics

Activity MonitoringLearningAnalytics

FeedbackLearningAnalytics

Learning Analytics• Prediction (in which data is used to predict future

performance);• Clustering (identify similarities in students/tasks);• Relationship mining (between students, teachers,

concepts, etc.);• Modeling (creating a model and using this for further

prediction or analysis); and• Distillation of data for human judgment (the most

common use by teachers using statistics and visualisations).

LearningAnalytics

Learning Analytics• Extracting and analysing data from learning

management systems;• Building an analytics matrix that incorporates data

from multiple sources (social media, LMS, student information systems, etc);

• Profile or model development of individual learners (across the analytics matrix);

• Predictive analytics: determining at-risk learners;• Automated intervention and adaptive analytics: i.e.

the learner model should be updated rapidly to reflect near real-time learner success and activity so that decisions are not made on out-dated models;

LearningAnalytics

Learning Analytics• Development of "intelligent curriculum" where

learning content is semantically defined;

• Personalisation and adaptation of learning based on intelligent curriculum where content, activities, and social connections can be presented to each learner based on their profile or existing knowledge; and

• Advanced assessment: comparing learner profiles with architecture of knowledge in a domain for grading or assessment.

LearningAnalytics

Learning Analytics• Data can uncover problems that might otherwise

remain invisible

• Data can convince people of the need for change

• Data can confirm or discredit assumptions about students and school practices

• Data can get to the root cause of problems, pinpoint areas where change is most needed, and guide resource allocation

LearningAnalytics

Learning Analytics

• Data can prevent reliance on standardised tests.

• Data can help evaluate program effectiveness and keep the focus on student learning

• Data can prevent one-size-fits-all solutions

• Data can help address accountability questions

• Data can build a culture of inquiry and improvement

LearningAnalytics

Surfaces

• Screen real-estate

• Multiple monitors

Surfaces

• Screen real-estate

• Multiple monitors

Interactive Whiteboards

Interactive SurfacesTechnological

Innovation

TechnologicalInnovationLocation based learning

TechnologicalInnovationAugmented Reality

Augmented RealityTechnological

Innovation

Augmented Classrooms

Wearable CamerasTechnological

Innovation

Google Glass

Google GlassTuesday, 1 October 13

Virtual RealityTechnological

Innovation

Virtual RealityTechnological

Innovation

Interactive ProjectionsTuesday, 1 October 13

Motion CaptureTuesday, 1 October 13

TechnologicalInnovationMotion Capture

Holographic TelepresenceTechnological

Innovation

TechnologicalInnovationTelepresence

Neural InterfacesTechnological

Innovation

Brain Waves

Delta 1-4hz Theta 4-7hz

Alpha 7-13hz Beta 13-30hz

Switch in reality

Wake -> Sleep Theta 4-7hz

Alpha 7-13hz Sleep -> Wake

Synaptic PruningTechnological

Innovation

Interactive ArtTechnological

Innovation

Griffith University

Dr Jason Zagami

jason.zagami.info

Educational Technologies

Education

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