Nora Sabelli, NSF

What could data mining and retrieval contribute to

the study of  education?

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What is my ‘home’ perspective?

NSF

EHR CISEBIO, etc.

Undergraduateeducation

K-12

Graduateeducation

Research (& evaluation)

ITR

EPSCOR

HRD

SOL?

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What incentives can be brought What incentives can be brought

to play to play

to integrate to integrate

technology advances technology advances

and a and a

technological infrastructure, technological infrastructure,

with with

education reform and education reform and

improvement goals?improvement goals?

Nora Sabelli, NSF

Aristotelian causes:

Material cause: because of the nature of their elements

·         paradigmatic science: physics      

Efficient cause: because of the energy that went into making them

·         paradigmatic science: engineering      

  

Formal cause: because of the relations between their parts

·         paradigmatic sciences: biology      

Final cause: because of the desires of an external agent

·         paradigmatic science: social sciences

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Brainmechanisms

Cognitive and

behavioralstudies

Complexsystems and

systemicreform

Education

Cognitive neuroscience

Social sciences:e.g. economics ,anthropology

Learning

Social sciences:e.g. policy,

organization, economics

The ROLE The ROLE organization:organization:

Q4

Q1

Q2

Componentsof

contexualizedpractice

Q3

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Education Research : organizing scheme

BiologicalBasis

Learning

Education

CognitiveBasis

Componentsof

Practice

SystemicIssues

Implementation

Research

Data from ongoing / new efforts

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How people learn

What people learn

Why people learn

Organizational support

Pedagogical supportSocial/political support

cognition

contentcontext

institutionalization

pedagogyalignment

Nora Sabelli, NSFWhat people learn

(Content)

How people learn(Cognition)

Content standardsinstructional workforce capacity

Coherence across levels & incentives

Why people learn(Context)

How is learning organized(Education System(s))

Student level

Teacher level

School/district level

Policy level

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Student OutcomesEngagement

LearningAchievement

 

Student ExperiencesClass activities

HomeworkUse of computers

 

Student BackgroundDemographics

Family backgroundAcademic background

School OutputsEngagement

LearningAchievement

  

School ProcessesDecision-making (using technology)Academic &Social

Climate

School InputsStructural

characteristics Student composition

Resources (technology)

SCHOOL LEVEL

CLASSROOM LEVEL

Classroom InputsStudent compositionTeacher background

Resources (technology)

Classroom ProcessesCurriculum

Instructional strategies (using technology)

 

Classroom OutputsEngagement

LearningAchievement

  

STUDENT LEVEL

From RumbergerConceptual Framework for Analyzing Education as a Multi-Level Phenomenon

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Why we need to anticipate the future?

Doing more of the same is not always the solution

The types of science and mathematics needed

have changed

Because we learn from our past mistakes and

successes

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What advances should we consider?

Advances in science and mathematics methodologies

Complexity of the problems that can be solved and thus of the decisions that need to be made

Advances in our understanding of cognition and learning

Advances in our understanding of complex system dynamics

Nora Sabelli, NSF

Data and data sampling issues:

Limitations of existing data sets (for example, distance between measure and intervention)

Likelihood of gathering streams of data for individual cases

Aggregating data across different populations and/or based on different models (little comparison across models)

Steepness of change is not reflected in data sampling (static vs. non-linear dynamical effects)

Nora Sabelli, NSF

Knowledge Discovery and Learning from Data

Concept of ‘training samples’

Problems with ‘hypothesis verification’ as primary mode of analysis (ensemble learning)

Extracting / modeling more complex relationships

Developing model growth and change in data

Predictions that involve altering the probability distribution of the problem

Similarity detection

Nora Sabelli, NSF

Multiple scales of time and aggregation (mutual constraints and simultaneous analysis)

Integrating qualitative / quantitative analyses (emergence of new qualitative patterns)

Comparison across weightings (validating predictions)

When does sustainability appear (resilience)

Impact of non-causal constraints (I.e. textbooks)

Meta-analytical data mining?

Knowledge Discovery and Learning from Data

Nora Sabelli, NSF

Conditions for Success

Proper partnerships

whomever “owns” the problem must

“own” the solution

The complexity and non-linearity of the

education system

plan for long-term collaborations, not for a

“transfer” or handing down a solution

Nora Sabelli, NSF

http://www.sri.com/policy/designkt/found.html

SRI Technology Evaluation Design Meeting Web Site

Nora Sabelli, NSF

http://www.nsf.gov

Research

Research on Learning and EducationNSF 00-17

Interagency Education ResearchInitiative (NSF, NICHD, DoED)NSF 00-74

Finbarr (Barry) Sloane fsloane@nsf.gov Eric Hamilton ehamilto@nsf.gov Nora Sabelli nsabelli@nsf.gov