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Data-driven instruction is an educational approach that relies on information to inform teaching and learning. The idea refers to a method teachers use to improve instruction by looking at the information they have about their students.
The context-input-process-output (CIPO) model is a basic systems model of school functioning, which can be applied to several levels within education, namely system level, school level and classroom level. [1] The model also functions as analytical framework through which the educational quality can be reviewed. [2]
Methodologically, the learning sciences differs from other fields in educational research. It focuses on the study of learners, their localities, and their communities. The design-based research methodology is often used by learning scientists in their inquiries because this methodological framework considers the subject of study to be a complex system involving emergent properties that arise ...
Accurate analysis of data using standardized statistical methods in scientific studies is critical to determining the validity of empirical research. Statistical formulas such as regression, uncertainty coefficient, t-test, chi square, and various types of ANOVA (analyses of variance) are fundamental to forming logical, valid conclusions.
While the analysis of educational data is not itself a new practice, recent advances in educational technology, including the increase in computing power and the ability to log fine-grained data about students' use of a computer-based learning environment, have led to an increased interest in developing techniques for analyzing the large amounts of data generated in educational settings.
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]
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
The function h(V) is effectively the control function that models the endogeneity and where this econometric approach lends its name from. [4]In a Rubin causal model potential outcomes framework, where Y 1 is the outcome variable of people for who the participation indicator D equals 1, the control function approach leads to the following model