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Related Concept Videos

Case Studies01:22

Case Studies

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There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
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Types of Records II: Educational and Administrative Records01:18

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Maintaining nurses' educational and administrative records in healthcare settings, including hospitals and nursing schools, is paramount. Here's a breakdown of the types of academic records mentioned:
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Reliability and Validity01:29

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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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Surveys02:16

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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Archival Research01:40

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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Impact of Schemas01:30

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Schemas are cognitive structures that provide a framework for interpreting and organizing social information. They help individuals navigate complex environments by offering expectations about people, events, and behaviors. Schemas influence attention, encoding, and retrieval processes, thereby shaping the entire trajectory of information processing in social contexts.Attention and Cognitive LoadDuring initial attention, schemas function as filters that prioritize schema-consistent information,...
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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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The Structural Consequences of Big Data-Driven Education.

Elana Zeide1,2,3

  • 11 Center for Information Technology Policy, Princeton University , Princeton, New Jersey.

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|June 21, 2017
PubMed
Summary
This summary is machine-generated.

Big data in education shifts pedagogical decision-making from teachers to private tech companies, impacting classroom privacy and defining educational content without public oversight.

Keywords:
MOOCsbig datacompetency-based educationlearning analyticspersonalized learningsmart tutors

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Area of Science:

  • Educational Technology
  • Data Science in Education
  • Sociology of Education

Background:

  • Big data-driven learning environments raise questions about outcomes, privacy, and equality.
  • This article focuses on structural shifts in pedagogical decision-making caused by these technologies, rather than specific technology effects.

Purpose of the Study:

  • To examine how big data tools alter the structure of pedagogical decision-making in schools.
  • To highlight three significant structural shifts accompanying reliance on data-driven instructional platforms.

Main Methods:

  • Qualitative analysis of the structural impacts of big data in education.
  • Overview of shifts in teaching, assessment, and credentialing functions.

Main Results:

  • Virtual learning environments create infrastructures for constant data collection and algorithmic assessment, undermining classroom privacy.
  • Pedagogical decision-making shifts from educators to private providers, reducing teacher autonomy and stakeholder participation.
  • Big data tools increasingly define educational content and outcomes, ceding control to private entities without public scrutiny.

Conclusions:

  • Reliance on data-driven platforms fundamentally alters educational decision-making structures.
  • The adoption of education technologies often occurs without adequate public scrutiny or pedagogical examination.
  • Educators and policymakers must proactively address the implications of data-driven education.