Curriculum Directors Update

4/16/08

Vision: 100% Reliability - No Bottlenecks

Statewide Network

High Capacity & High Reliability OAWAN

Digital Learning Commons

http://www.learningcommons.org/

Vision: Single Sign On – Transparent Data Movement – Common Learning Objects (and context)

Middleware (Identity and Privilege Management)

SIF (School Interoperability Framework)

SCORM (Shareable Content Object Reference Model)

Vision: Single Sign On – Transparent Data Movement – Common Learning Objects (and context)

Middleware (Identity and Privilege Management)

www.sifinfo.org Copyright © SIF Association

SIS

Network Accounts

Cafeteria

Library

Transportation

Challenge – Schools Traditional Setup

?

?

?

?

?Instructional Applications

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Horizontal Questions

Data Warehousing

K12 Data Model

Food

Serv

ice

Gra

de B

ook

HR

/ Finance

Libra

ry

SIS

Tra

nsp

orta

tion

Instru

ction

al S

erv

ices

Voice

Tele

phony

Accountability, Reporting, Planning, etc…

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Components Working TogetherHorizontal Interoperability

Network Account

H.R. &Finance

Data Analysis

& Reporting Instructional

Services

Library Automation

Student Information

Services

GradeBook

FoodServices

– Zone Integration Server (ZIS)

– SIF Agents

– Applications

– SIF Data Objects

“SIF Zone”

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SIF Specification

* Implementation 2.1 – Data Model and Infrastructure

* Web Services 1.0

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CompositionComposition

SCORM Example Sharable Content

Impact of Impact of Policy Policy Decisions in Decisions in WWIIWWII

GovernmentGovernment

Compare/ Compare/ Contrast Contrast CulturesCultures

Each course uses the same module on the writing process, because the content is sharable and reusable

English IIIEnglish III

Sharable Content ObjectSharable Content Object

School Language Arts Content

Chinese IIChinese II

The Writing The Writing ProcessProcess

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Conformant content will work with every conformant LMS.

Being SCORM conformant does not

affect the instructional content

SCORM Interoperability

Sharable Content Object

Vision: Enterprise IT Operation

Standards-based Operations Frameworks

OAISD Technology Services

MISSION

We provide and enrich educational opportunities for students, schools, and

communities.

Are we positioned from an infrastructure and staffing perspective to best meet the mission of OAISD for the next 5-10 years (and beyond)?

Learners

(within OAISD service area)

Technology Services

Other OAISD Depts.

Local Schoo

ls

OAISD Schoo

ls

OAISDOtherDirect

“Self” Learnin

g

tech

deliv

ere

dte

ch

sup

port

ed

Mission

Interventions(tools)

Informatics(informed decisions)

(actionable intelligence)

+

Mission

Implements(tools)

Informatics(informed decisions)

+

• ERP Systems• SIS, Financials, HR, etc. (management and/or coordination)• Enterprise Portal

• Database / Application Development and Support• Business Intelligence (business and academics)

• Data Warehouse• Access & Reporting

• Standard Reports (What happened?)• Ad hoc reports (How many, how often, where?)• Query/drill down (Where exactly is the problem?)• Alerts (What actions are needed?)

• Analytics • Statistical Analysis (Why is this happening?)• Forecasting/extrapolation (What if these trends continue?)• Predictive modeling (What will happen next?)• Optimization (What’s the best that can happen?)

• Classroom Management / Learning Systems – Moodle, wikis, etc.• Distance Learning Coordination (from a technical standpoint)• Assessment Systems and Academic Data Gathering (feed to analytical systems)• Digital Asset Management

• Digital / streaming access and collection (in conjunction with IS)• ITV• Internet 2

• Computer / Network Assisted Learning support, e.g., KeyTrain• Specialized Projects, e.g., MiBLISI technology coordination

Vision: JIT Information for Decision Making

Information Infrastructure- Data Collection & Processing

-OLTP Systems- Other

- Data Stores & Retrieval- OLTP Systems- Ad Hoc Data Stores- Data Warehouse

- Data Reporting- Standard- Ad Hoc- Data Marts

- Data Analysis- Performance Monitoring- Data Mining- Predictive Analytics

National

State

Research Community

OAISD

Evolution of EducationEvolution of Education

• 21st century students

• 21st century goals/skills

• 21st century pedagogy and content

• 21st century assessment

• 21st century research

Emerging TechnologiesAre Shaping All of These

Distributed Work,Distributed Work,Cognition, and LearningCognition, and Learning

Cognition is distributed across human minds, tools/media, groups of people, and space/ time

Dispersed physically, socially, and symbolically

Distributed learning: collaborative, mediated, scaffolded, and data-generating

Datamining:Datamining: Next Next GenerationGenerationEducational AnalyticsEducational Analytics

• The process of selecting, exploring, and modeling large amounts of data to uncover previously unknown patterns

• An iterative learning-based process similarto other knowledge generation processes,such as scientific discovery

• Routine in the business world: Decisions around database marketing, credit risk management, and fraud detection are all influenced through predictive modeling

Illustrative Uses of Illustrative Uses of Datamining in Higher Datamining in Higher EducationEducation

• Which prospective students will accept?

• What courses will students want,as they mature through their experiences?

• Which students will drop out?

• Which graduates will default on loans?

• Which alumni will donatesubstantial money?

The Promise ofThe Promise ofDatamining for LearningDatamining for Learning

Formative, diagnostic informationthat provides real-time feedback to teachers

Summative assessments about what each student has mastered, based on authentic performances

Insights about student behavior and learning related to individual characteristics

A better understanding of collaborativeproblem solving and team learning processes

Insights about the microgenetics of learning by examining patterns and relationships between students’ behavioral patterns and learning outcomes

2121stst Century Research: Century Research:Types of DataTypes of Data

Assessment DataAssessment Data Pre-post content and affective measures Embedded Assessments Performance Assessments

Contextual DataContextual Data Demographics of students, teacher, school

Active DataActive Data Students’ actions and behaviors as they learn

via “mediated interactions”

2121stst Century Research: Century Research:Data AnalyticsData Analytics

How to make sense of all this active data(too much rather than too little)?

How to cross-reference and synthesize these various types of datato improve student learning?

How to use active data to improve instructional design?

River City MUVE?River City MUVE?

An “Alice in Wonderland” experience where users enter a virtual space that has been configured for learning

Learners represent themselves through graphical avatars to communicate with others’ avatars and computer-based agents, as well as to interact with digital artifacts and virtual contexts

Student’s Role in theStudent’s Role in theRiver City MUVERiver City MUVE

Travel back in time 6 times between 1878-79

Bring 21st century skills and technologyto address 19th century problems

Help town understand and solve part ofthe puzzle of why so many residentsare becoming ill Work as a research team Keep track of clues that hint at causes of

illnesses Form and test hypotheses in

a controlled experiment Make recommendations based on

experimental data

Capturing Data on Change over Time

Fall, 1878 Winter, 1879 Spring, 1879 Summer, 1879

Students visit the same places and see how things change over time. They spend an entire class period in an individual season, gathering data.

Visit 1 Visit 2 Visit 3 Visit 4

Evidence of Student Evidence of Student WorkWork

Assessment data: Pre-post content Pre-post affective Embedded assessments

(formative) Performance

assessment (summative)

Contextual Data: Attendance records Demographic data School data Observations Interviews

Active Data: Team chat Notebook entries Tracking of in-world

activities: Data gathering

strategies Pathways Inquiry processes

Traditional Evaluation of Traditional Evaluation of QualityQuality

Inferential methods:

On average, students in the River City treatment scored .2 points higher on the post self-efficacy in general science inquiry section of the affective measure (t=2.22, p<.05).

On average, students in this sample who saw higher gains in self efficacy in general science inquiry scored higher on the post test. These gains were higher for students in the River City project. (n=358)

Yet these results tell us nothing about patterns, behaviors,and processes that lead to inquiry. We are also limitedby # of variables we can build into our model.

Event Logs as Event Logs as Observational DataObservational Data

Indicates with Timestamps Where students went With whom they communicated

and what they said What artifacts they activated What databases they viewed What data they gathered

using virtual scientific instruments What screenshots and notations they

placed in team-based virtual notebooks

unobtrusive observational data

Shorty’s (1169) Team, Session 2 Shorty’s Team, Session 3

Datamining MethodsDatamining Methods

• Sophisticated graphical user interface (GUI) data exploration and plotting tools to better display relationships among variables

• Variable selection methodologies to identify the most important variables to include in models

• Advanced modeling techniques, such aslinear models with interactions

• Nonlinear neural networks and tree models• Assessment techniques to assist analysts in

selecting the best performing model

find additional variables, identify interaction terms,

and detect nonlinear relationships

Potential Insights for Potential Insights for StudentsStudents

Evolution over time of: Engagement Information-Seeking

Sources: context, agents, artifacts, databases, virtual scientific instruments, hints…

Collaboration, includinguse of virtual notebook

Content Mastery Inquiry strategies

Potential Insights for Potential Insights for TeachersTeachers

Diagnostic, formative information aboutindividual students Engagement Level and types of hints accessed Skewed information-gathering patterns

Diagnostic, formative information aboutstudents collectively Level of collaboration Degree to which types of hints are needed Degree to which some kinds of information

resources are underutilized Patterns of scores on embedded content

assessments

Vision: JIT Information for Decision Making

Information Infrastructure- Data Collection & Processing

-OLTP Systems- Other

- Data Stores & Retrieval- OLTP Systems- Ad Hoc Data Stores- Data Warehouse

- Data Reporting- Standard- Ad Hoc- Data Marts

- Data Analysis- Performance Monitoring- Data Mining- Predictive Analytics

National

State

Research Community

OAISD

Thursday, May 1 – 8:30am – 11:30amJenison High School (Media Center)

KISD Data Warehouse Demonstration

Contact: mrohwer@oaisd.org if interested.

Vision: Tools Teachers/Learners Need

Internet2

ELAR

Digital Asset Management System

Classroom AV

Online learning….

Vision: Chief Technology Officers

Ottawa Area Technology Directors

CoSN Michigan Chapter

Michigan IT Executive Forum

Advocacy

• Leadership & Vision• Planning & Budgeting• Team Building & Staffing• Systems Management• Information Management• Business Leadership• Education & Training• Ethics & Policies• Communications Systems

Curriculum Directors Update

4/16/08