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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Correlation analysis using teaching and learning analytics.

P A N Prestes1, T E V Silva1, G C Barroso1

  • 1Department of Teleinformatics (DETI), Federal University of Ceará (UFC), Fortaleza, CE, Brazil.

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Summary

Data analytics in education revealed non-linear correlations in school environments. Teaching and Learning Analytics (TLA) showed complex relationships between school factors and student learning outcomes.

Keywords:
CorrelationEducational data miningLearning analyticsTeaching and learning analytics

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Area of Science:

  • Education
  • Data Science
  • Educational Management

Background:

  • Data analytics is increasingly applied in education.
  • The Organization for Economic Development Cooperation (OECD) collects data on school environments.
  • Key areas assessed include school environment, professional development, leadership, and management.

Purpose of the Study:

  • To analyze OECD questionnaire data using data analytics.
  • To investigate relationships between school environment factors and teaching-learning dynamics.
  • To explore student learning through specific analyses.

Main Methods:

  • Utilized Teaching and Learning Analytics (TLA).
  • Employed correlation analysis to identify relationships in raw data.
  • Examined data related to school environment, professional development, leadership, and management.

Main Results:

  • School environment data showed no moderate or solid linear correlation.
  • Analysis revealed dichotomous observations and insecurities regarding school practices.
  • Direct validation and integration of theme-related answers were not possible.

Conclusions:

  • The study highlights the complex and non-linear nature of school environment factors.
  • Findings suggest significant controversies and uncertainties in current educational practices.
  • Further research is needed to understand effective school scenarios and practices.