Nora Sabelli, NSF What could data mining and retrieval contribute to the study of education?

Nora Sabelli, NSF What is my ‘home’ perspective? NSF EHR CISE BIO, etc. Undergraduate education K-12 Graduate education Research (& evaluation) ITR EPSCOR HRD SOL?

Nora Sabelli, NSF What incentives can be brought to play to integrate technology advances and a technological infrastructure, with education reform and 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

Nora Sabelli, NSF Brain mechanisms Cognitive and behavioral studies Complex systems and systemic reform Education Cognitive neuroscience Social sciences: e.g. economics, anthropology Learning Social sciences: e.g. policy, organization, economics The ROLE organization: Q4 Q1 Q2 Components of contexualized practice Q3

Nora Sabelli, NSF Education Research : organizing scheme Biological Basis Learning Education Cognitive Basis Components of Practice Systemic Issues Implementation Research Data from ongoing / new efforts

Nora Sabelli, NSF How people learn What people learn Why people learn Organizational support Pedagogical supportSocial/political support cognition content context institutionalization pedagogy alignment

Nora Sabelli, NSF What people learn (Content) How people learn (Cognition) Content standards instructional 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

Nora Sabelli, NSF Student Outcomes Engagement Learning Achievement Student Experiences Class activities Homework Use of computers Student Background Demographics Family background Academic background School Outputs Engagement Learning Achievement School Processes Decision-making (using technology) Academic &Social Climate School Inputs Structural characteristics Student composition Resources (technology) SCHOOL LEVEL CLASSROOM LEVEL Classroom Inputs Student composition Teacher background Resources (technology) Classroom Processes Curriculum Instructional strategies (using technology) Classroom Outputs Engagement Learning Achievement STUDENT LEVEL From Rumberger Conceptual Framework for Analyzing Education as a Multi-Level Phenomenon

Nora Sabelli, NSF Why we need to anticipate the future? l Doing more of the same is not always the solution l The types of science and mathematics needed have changed l Because we learn from our past mistakes and successes

Nora Sabelli, NSF What advances should we consider? l Advances in science and mathematics methodologies l Complexity of the problems that can be solved and thus of the decisions that need to be made l Advances in our understanding of cognition and learning l 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 l Proper partnerships whomever “owns” the problem must “own” the solution l 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 SRI Technology Evaluation Design Meeting Web Site

Nora Sabelli, NSF Research Research on Learning and Education NSF Interagency Education Research Initiative (NSF, NICHD, DoED) NSF Finbarr (Barry) Sloane Eric Hamilton Nora Sabelli