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What could data mining and retrieval contribute to the study of  education?

What could data mining and retrieval contribute to the study of  education? . What is my ‘home’ perspective?. SOL?. NSF. ITR. EHR. BIO, etc. CISE. Undergraduate education. K-12. EPSCOR. Graduate education. Research (& evaluation). HRD. What incentives can be brought to play

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What could data mining and retrieval contribute to the study of  education?

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  1. What could data mining and retrieval contribute to the study of  education?

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

  3. What incentives can be brought to play to integrate technology advances and a technological infrastructure, with education reform and improvement goals?

  4. Aristotelian causes: Material cause: because of the nature of their elements ·paradigmatic science: physics Efficient cause: because ofthe 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

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

  6. Education Research : organizing scheme Learning Cognitive Basis Systemic Issues Implementation Research Biological Basis Components of Practice Education Data from ongoing / new efforts

  7. cognition How people learn Social/political support Pedagogical support alignment pedagogy Why people learn What people learn context Organizational support content institutionalization

  8. Policy level School/district level Teacherlevel Student level How is learning organized (Education System(s)) Content standards Coherence across levels & incentives instructional workforce capacity Why people learn (Context) How people learn (Cognition) What people learn (Content)

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

  10. 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

  11. 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

  12. 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)

  13. 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

  14. Knowledge Discovery and Learning from Data • 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?

  15. 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

  16. http://www.sri.com/policy/designkt/found.html SRI Technology Evaluation Design Meeting Web Site

  17. http://www.nsf.gov Research on Learning and Education NSF 00-17 Research Interagency Education Research Initiative (NSF, NICHD, DoED) NSF 00-74 Finbarr (Barry) Sloane fsloane@nsf.gov Eric Hamilton ehamilto@nsf.gov Nora Sabelli nsabelli@nsf.gov

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