Educational Data Sciences and the Need For Hermeneutic Principles An Interdisciplinary Perspective

Educational Data Sciences and the Need For

Hermeneutic Principles

An Interdisciplinary Perspective


Hermeneutic Principles Applied Epistemology



Hermeneutic Principles Applied Epistemology

Interpretive Skills


Educational Data Sciences and the Need For Interpretive Skills


Some Driving Questions

• What counts as good learning analytics?• What kind of profession will data sciences

be?• What are its ancestor, sister, and adjoining

disciplines? • Which kinds of skills and dispositions are

important for preparing future practitioners and scholars?

Our Sociotechnical Thesis

• Data exist inside a social context; shaped by and shaping that context.



• Interpretation is not technical. It is itself socially situated with goals, predispositions/ biases, and norms.



• Interpretation is not technical. It is itself socially situated with goals, predispositions/ biases, and norms.

• Professional communities have developed valuable ways to reason from imperfect evidence. We can leverage/translate them to this new sociotechnical terrain.

Overview

1. Quantitative shifts in evidentiary artifacts (a digital ocean) in education

2. Qualitative shifts in educational focus

3. Some contributing/relevant disciplines

4. How to approach analysis, what kind of science/craft/skill/briciolage, etc. is it?

QUANTITATIVE AND QUALITATIVE SHIFTS IN EDUCATIONAL EVIDENCE

Computer

Adaptive testing

Assessment Technology

Computing Technology

Central “Mainframe“ Computing

Personal Computin

g

Devices

Tabulating Technology

Cloud Technology

Services

Traditional fixed response, short task assessments

Analog Paper-based (Textbooks, worksheets, and manual classroom tools)

Analog Portfolio

Classroom Technology

Th

e D

igit

al

Ocean

Distributed Integrated Assessment

Systems

Dramatic Growth in Artifacts

Digital Classroom

Technology

1850s 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 20101850s 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

The Digital Ocean• Test scores• Interim assessments• In class, formative assessments• Growth models• Student collaboration• Conversation records from

classroom talk and online tools • Student work, including rich and

multimodal demonstrations of knowledge and competency (essays, presentations, etc.)• Records of after-school

experiences• Records of informal learning • Activity traces from digital media

(in school, out of school, etc.)

• Demographics• Student-teacher relationships (TSDL) • School improvement plans/goals• Classifications (ex: proficiency

groups)• Video records of teaching• Annotated/evaluated records of

teaching• Teacher evaluations• Individual Education Plans (IEPs) and

personalized learning maps• Geospatial information

(mapping and trends)• Attendance and rosters (more

important than you think!)• FERPA/privacy blocks

Studying Oceans

Studying Oceans

Studying Oceans

Structures & Interrelationships

Diachronic/Change Processes

Variations in Affordance

Social Networks &Teams

Mobile Technology

Evidence and Transparency

Institution Focus

Teacher Control

Institutional Center Individual Student Nexus

Qualitative Shift in Emphasis

Related to the Educational Data Movement

SocialNetworksLearning

Networks

LearningCommuni

ties.

ExpertSources

Open Ed.Resources

Families


WHAT DO STUDENTS KNOW?

Cognitive• Cognitive processes

and strategies• Knowledge• Creativity

Intrapersonal• Intellectual openness• Work ethic and

conscientiousness• Positive core self-

evaluation

Interpersonal• Teamwork and

collaboration• Leadership

Dig

ital M

edia

tion

• Critical thinking• Information literacy• Reasoning• Innovation

• Flexibility• Initiative• Appreciation for

diversity• Metacognition

• Communication• Collaboration• Responsibility• Conflict resolution

Artifacts


Black Boxes Model

• Danny Hillis story; Oscon July 2012


Black Boxes Model

• Danny Hillis story; Oscon July 2012

Explicit and Interrelated Components Model

Sociotechnical way of thinking about an educational system

THE DATA SCIENCES

Six Adjoining Disciplines

Educational Data Sciences

1. A new field with growing interest from leading universities, foundations, USED

2. Journals, conferences, and programs now emerging

3. What is the disciplinary focus, what counts as rigor and success?

EducationData

Sciences

Statistical Data Analysis

Statistical Data

Analysis

EducationData

Sciences

• Much of the digital ocean is compatible with statistical analysis.

• Exploratory data analysis (ex: Tukey with satellite data in 70s asked many questions that are being asked today about “big data”

• Already established (entrenched) in educational power structures

• Can produce strong claims

Learning Technology

Statistical Data

Analysis

EducationData

Sciences

Classroom/ Learning

Technology

• This is where the data we want most often come from…

• This area is seeing an explosion in media/learning resources and classroom management tools

Learning Sciences

Statistical Data

Analysis

EducationData

Sciences

Classroom/ Learning

Technology

Learning Sciences

• What does the data mean for multimodal, sociotechnical learning?

• How do socio-cultural and cognitive theories influence and be informed by data technologies?

• A design science for educational practice with iterative experiment, evaluate, refine process

Information Sciences

Statistical Data

Analysis

EducationData

Sciences

Classroom/ Learning

Technology

Learning Sciences


• Visualizations and Human Computer Interface

• The information architectures that undergird data systems• Codes, classifications• Infrastructures and

boundary objects• Media centers and

educational resources

Organization/Management Sciences

Statistical Data

Analysis

Organization & Mgmt Sciences

EducationData

Sciences

Classroom/ Learning

Technology

Learning Sciences


• Education is full of processes that can be designed

• Blended learning models are essentially re-structuring of organizational practices

• Inter-organizational functions are changing:• States-districts• Special education


Statistical Data

Analysis


EducationData

Sciences

Classroom/ Learning

Technology

Learning Sciences


Decision Sciences

• Established field that uses large bodies of information to support organizational decisions

• As the volume and quality of educational data increase, more situations where decision sciences can be applied will emerge.

THE DATA SCIENCES

The Seventh and Generative Discipline




Classroom/ Learning Technology

Learning Sciences


Decision Sciences

Computer Science and EDM

Computer Science





Learning Sciences


Decision Sciences


Computer Science





Learning Sciences


Decision Sciences

Machine Learning

Data Mining

Hum-Comp. Interaction &Visualization

Natural Language Processing

Computational Statistics


REASONING FROM DIGITAL AGE EVIDENCE

Approaching Digital Age Data Analysis

• What counts as rigor and success?• Which parts of what disciplines are needed?• What methods are best?• What kinds of processes will make good,

great, and poor educational data analysts?• How much of the requirements are technical

versus attitudinal ?

Core Principles

1. All analytic processes are socially situated and iterative

2. Data is a mediational tool in an iterative process of discovery

3. Data is an imperfect lens for context and for interactions within that context

4. Organizational/systems thinking helps expand the reach of educational data science

5. Ethical as well as legal considerations are important.

Three Research Traditions

Evidence Centered

Design (ECD)

Exploratory Data Analysis

(EDA)

Linked Activity Systems

Framework (LASF)

Some Insights from ECD

• Arguments and Layers

• Domain Analysis: What is important in the domain?

• Domain Modeling: The Structure of Assessment Arguments

• Student, Evidence, and Task Models

Some Insights from CHAT/LASF

District Analytics

•Student Performance Data•Demographics •Rosters and assignments•Logs from technology tools

•Teacher and school characteristics•Home and demographic data

District Data Warehouse

•Census and geospatial data•Comparable data from SLDS

Teachers Teachers Teachers Teachers Teachers Teachers Teachers Teachers Teachers Elem.

Schools

Teachers Teachers Teachers Teachers Teachers Teachers Teachers Teachers

•Programmatic Evaluation•Quality of online tools•Professional Development•School Feedback

• Budgets and operating costs •Student and parent surveys

Traditional Model

Blended Model

Engeström Cultural Historical Activity Theory (CHAT)

Piety and Behrens Linked Activity Systems Framework

• Data analysis generally involves more than one activity system.

• The technology plays a linking/mediating role across contexts as well as within

• Framework allows for conceptualizing privacy/authorized access space

Some Insights from EDA

Underlying Heuristics (4Rs)• Revelation (graphics)• Residuals (models)

• Resistance /robustness

• Re-expression: scale

Broader Meaning• What do we “see” in the data?• What in our data fits/does not fit

with our emergent model?• Do we have a summary that is

not easily fooled by unusual distributions/examples

• How do the explanations we see apply more broadly.

What Kinds of Skills/Aptitudes?

• Broad fluency with a range of qualitative/quantitative methods

• Ethics, privacy, and confidentiality (FERPA+)• Technology accumen

The Educational Data Movement

• Systemic viewpoint: across silos and inter-organizational understanding

Though a famous and successful statistician, Tukey wanted to create a field that dealt with all data, even when it came in suchpoor shape that it was not amenable to statistical analysis. He called it “data analysis” and created the field called “ExploratoryData Analysis”. My undergraduate degree was in Psychology and Philosophy. I thought if I knew the logic of how we know things (epistemology) and understood the human lens through which all perception and thought occurs (psychology) I would have the fundamental layers of knowing from which to acquire more knowledge. After serving as a social worker and studying special education, I sought my Ph.D in Educational Psychology with a cognate called “Measurement, Statistics & Methodological Studies”. I would approach it as applied epistemology: How do we learn from data?

When I discovered Tukey’s writings I knew I had found the right place. I conducted psychological studies on perception ofstatistical graphics and wrote about the logical foundations of data analysis. When I wrote such a chapter called “Data and DataAnalysis” [6] people told me it was a silly title – data wasn’t a subject, it’s only a piece of the background to other sciences.Philosophy is concerned with understanding meaning and the application of logic. The philosopher asks What do we mean by‘data’? What do we mean by ‘analysis’? If data are symbols that point to elements in the world, what kind of logic do we need tounderstand that linkage? Like very good scientists, philosophers question the obvious. Such questioning may not be essential forwhat you do today, but it may open the door to do new ways of thinking you never imagined.

The successful learning analyst will avoid two common errors: Failure to understand the context and failure to become intimatelyfamiliar with the data. 1. The first error is caused by lack of contextual knowledge. Studying the learning sciences, education, and related disciplines will

help. 2. The second is error is caused by a substitution of complex statistical or computational models for detailed mental models. We

only build computational models or display to help our mental models.

Question the assumptions of your work deeply. It is important that analysts understand their work is about “revelation” or “unveiling” the reality of the world. It is a special (at times prophetic) role in society and should be taken very seriously.Do not think of data science as a set of techniques but as a collection of viewpoints (epistemic positions) and habits of mind.To undertake good visualization we need to know the techniques of data display, but also the psychology of perception, theanthropology of semiotics, the mathematics of fluctuation and the philosophy and art of aesthetic engagement. We will always need good technical analysts, but we need them to be

Drivers of Educational Data Mining

• Personalized Learning • College Going• Human Capital

Big Question

• What is the new role for the teacher?• Privacy • Getting designation as school vendors • Funder issues (12-24 months 2,3,5 yrs)

• Speak Gate-ish• Issue of interoperability…• Continuous Improvement from ECD

• Activity theory tensions• Activity systems in coherence/contrast• Innovations that help resolve Activity System

tensions (Engestrom, end of life care). • More data about the context

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Educational Data Sciences and the Need For Hermeneutic Principles An Interdisciplinary Perspective