Adaptive Recommended Learning April 2012

Adaptive Recommended LearningAn overview

Tuesday, April 17, 2012 ()

Overview

1. Start: Login, Portal

2. Student: Timeline, Learning Material

3. Teacher: Students, Objectives, Results

4. Process: Adaptive Friction


1/4 Start


integration with school authentication

server


every school has its own landing page


2/4 Student


load other apps to your dashboard

change your personal settings

in the Me app

subject specific content


achievement timeline

personalized list of assignments for

the studentstudent’s learning

objectives

history of assignments


student 1 student 2 student 3

profile- style: reproduction- modality: video- freetime: horses

profile- style: applying- modality: text- freetime: motorcycles

profile- style: deep learning- modality: audio- freetime: Ajax Amsterdam


question type example

“associate”


question type example

“fill in the blanks”


student receives direct feedback


3/4 Teacher


teacher has an extended dashboard


overview of the results (answers) given by

students

list with assignments


answer to question 5 of an assignment


integration with school administration systemstudent list


details of a student


group progress over time

circle size corresponds with

study timeline: target level


learning objectives, sub-goals and dependancies between goals


4/4 Process


teacher

student

system

Adaptive Friction

profile

personalize

score

analyse


smart learning objects

has metadatalike semantic density

student monitor

filtering of learning objects based on the student’s profile and object’s metadatastudent

profile

- educational statistics

- personal information

- educational profile

learning objectives

- achievements

personalized learning arrangement

sequence of learning objects to achieve objectives

selection of objectives that achieve the higher goals in the formal curriculum

matching of the learning objectives and learning objects


The End


Education

Adaptive Recommended Learning April 2012