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[1] [3] Data-driven decision-making is commonly used in the context of business growth and entrepreneurship. [4] [5] Many educators have access to a data system for analyzing their students' data. [6] These data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system ...
Additional problems associated with perceptions of data driven instruction include the limitations of quantitative data to represent student learning, not considering the social and emotional needs or the context of the data when making instructional decisions, and a hyperfocus on the core areas of literacy and mathematics while ignoring the ...
Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]
The goal of this method is to summarize and present the information in a useful, interactive and visually appealing way in order to understand the large amounts of education data and to support decision making. [9] In particular, this method is beneficial to educators in understanding usage information and effectiveness in course activities. [9]
Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. [7] ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor data for self-driving cars and robotics, identity data for security systems, demographic and ...
Johns (1936 – 2009) pioneered data-driven learning and coined the term. It first appeared in an article, Should you be persuaded: Two examples of data-driven learning (1991). [ 1 ] His paper, From Printout to Handout, [ 2 ] is reprinted and discussed at length in Volume 2 of Hubbard's Computer-Assisted Language Learning . [ 3 ]
Action research is an interactive inquiry process that balances problem-solving actions implemented in a collaborative context with data-driven collaborative analysis or research to understand underlying causes enabling future predictions about personal and organizational change.
An example of data mining related to an integrated-circuit (IC) production line is described in the paper "Mining IC Test Data to Optimize VLSI Testing." [12] In this paper, the application of data mining and decision analysis to the problem of die-level functional testing is described. Experiments mentioned demonstrate the ability to apply a ...