Educational Data Sciences and the Need For Hermeneutic Principles

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

2. Educational Data Sciences and the Need For Hermeneutic Principles Applied Epistemology An Interdisciplinary Perspective

3. Educational Data Sciences and the Need For Hermeneutic Principles Applied Epistemology Interpretive Skills An Interdisciplinary Perspective

4. Educational Data Sciences and the Need For Interpretive Skills An Interdisciplinary Perspective

5. 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?

6. Our Sociotechnical Thesis Data exist inside a social context; shaped by and shaping that context.

7. 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.

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

9. Overview • Quantitative shifts in evidentiary artifacts (a digital ocean) in education • Qualitative shifts in educational focus • Some contributing/relevant disciplines • How to approach analysis, what kind of science/craft/skill/briciolage, etc. is it?

10. Quantitative and Qualitative Shifts in Educational Evidence

11. Dramatic Growth in Artifacts 1850s 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 1850s 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Personal Computing Devices Cloud Technology Services Computing Technology Central “Mainframe“ Computing Tabulating Technology The Digital Ocean Digital ClassroomTechnology Classroom Technology Analog Paper-based (Textbooks, worksheets, and manual classroom tools) Distributed Integrated Assessment Systems Traditional fixed response, short task assessments Assessment Technology Analog Portfolio Computer Adaptive testing

12. 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

13. Studying Oceans

14. Studying Oceans

15. Studying Oceans Structures & Interrelationships Variations in Affordance Diachronic/Change Processes

16. Qualitative Shift in Emphasis Institutional Center Individual Student Nexus Social Networks &Teams LearningCommunities. SocialNetworks LearningNetworks Mobile Technology Families Open Ed.Resources Institution Focus Teacher Control Evidence and Transparency ExpertSources Related to the Educational Data Movement

17. Qualitative Shift in Emphasis • Cognitive • Cognitive processes and strategies • Knowledge • Creativity • Intrapersonal • Intellectual openness • Work ethic and conscientiousness • Positive core self-evaluation • Interpersonal • Teamwork and collaboration • Leadership • Critical thinking • Information literacy • Reasoning • Innovation • Flexibility • Initiative • Appreciation for diversity • Metacognition • Communication • Collaboration • Responsibility • Conflict resolution WHAT DO STUDENTS KNOW? Artifacts Digital Mediation

18. Qualitative Shift in Emphasis • Danny Hillis story; Oscon July 2012 Black Boxes Model

19. Qualitative Shift in Emphasis • Danny Hillis story; Oscon July 2012 Black Boxes Model • Sociotechnical way of thinking about an educational system Explicit and Interrelated Components Model

20. Six Adjoining Disciplines The Data Sciences

21. Educational Data Sciences • A new field with growing interest from leading universities, foundations, USED • Journals, conferences, and programs now emerging • What is the disciplinary focus, what counts as rigor and success? EducationData Sciences

22. Statistical Data Analysis • 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 Statistical Data Analysis EducationData Sciences

23. 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 Statistical Data Analysis Classroom/ Learning Technology EducationData Sciences

24. 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 Statistical Data Analysis Classroom/ Learning Technology EducationData Sciences Learning Sciences

25. Information Sciences • Visualizations and Human Computer Interface • The information architectures that undergird data systems • Codes, classifications • Infrastructures and boundary objects • Media centers and educational resources Statistical Data Analysis Classroom/ Learning Technology EducationData Sciences Learning Sciences Information Sciences

26. Organization/Management 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 Classroom/ Learning Technology EducationData Sciences Learning Sciences Organization & Mgmt Sciences Information Sciences

27. Educational Data 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. Statistical Data Analysis Classroom/ Learning Technology Decision Sciences EducationData Sciences Learning Sciences Organization & Mgmt Sciences Information Sciences

28. The Seventh and Generative Discipline The Data Sciences

29. Computer Science and EDM Educational Data Sciences Statistical Data Analysis Classroom/ Learning Technology Decision Sciences Learning Sciences Organization & Mgmt Sciences Information Sciences

30. Computer Science and EDM Educational Data Sciences Statistical Data Analysis Classroom/ Learning Technology Decision Sciences Learning Sciences Organization & Mgmt Sciences Information Sciences Computer Science

31. Computer Science and EDM Educational Data Sciences Statistical Data Analysis Classroom/ Learning Technology Decision Sciences Learning Sciences Organization & Mgmt Sciences Information Sciences Computational Statistics Computer Science Hum-Comp. Interaction & Visualization Data Mining Machine Learning Natural Language Processing

32. Reasoning from Digital Age Evidence

33. 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 ?

34. 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.

35. Three Research Traditions Evidence Centered Design (ECD) Exploratory Data Analysis (EDA) Linked Activity Systems Framework (LASF)

36. 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

37. Some Insights from CHAT/LASF 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

38. Some Insights from EDA Underlying Heuristics (4Rs) 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. • Revelation (graphics) • Residuals (models) • Resistance /robustness • Re-expression: scale

39. What Kinds of Skills/Aptitudes? • Broad fluency with a range of qualitative/quantitative methods • Ethics, privacy, and confidentiality (FERPA+) • Technology accumen

40. The Educational Data Movement • Systemic viewpoint: across silos and inter-organizational understanding

41. Though a famous and successful statistician, Tukey wanted to create a field that dealt with all data, even when it came in such poor shape that it was not amenable to statistical analysis. He called it “data analysis” and created the field called “Exploratory Data 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.Din 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 of statistical graphics and wrote about the logical foundations of data analysis. When I wrote such a chapter called “Data and Data Analysis” [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 to understand that linkage? Like very good scientists, philosophers question the obvious. Such questioning may not be essential for what 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 intimately familiar with the data. The first error is caused by lack of contextual knowledge. Studying the learning sciences, education, and related disciplines will help. 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, the anthropology 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

42. Drivers of Educational Data Mining College Going Human Capital • Personalized Learning

43. 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

44. 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