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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Steps in Outbreak Investigation01:18

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Observational Learning01:12

Observational Learning

259
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Updated: Aug 16, 2025

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
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Recent advances in Predictive Learning Analytics: A decade systematic review (2012-2022).

Nabila Sghir1, Amina Adadi1, Mohammed Lahmer1

  • 1Moulay Ismail University, Meknes, Morocco.

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|December 26, 2022
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Machine and Deep learning models are increasingly used for predicting academic outcomes in higher education. This review synthesizes recent research on predictive analytics, covering methods, data, and future directions.

Keywords:
Educational data miningHigher educationLearning analyticsMachine learningPredictive modelling

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Area of Science:

  • Educational Data Mining
  • Learning Analytics
  • Higher Education Research

Background:

  • Predictive modeling is a growing area in educational data mining and learning analytics.
  • Machine and Deep learning models are increasingly utilized to forecast student academic outcomes.
  • The goal is to enhance the learning process through data-driven insights.

Purpose of the Study:

  • To systematically review recent research (2012-2022) on predictive analytics in higher education.
  • To identify commonly predicted academic outcomes and the learning features used.
  • To analyze predictive modeling processes, including data, models, and performance metrics.

Main Methods:

  • Systematic literature review following PRISMA guidelines.
  • Analysis of articles published between 2012 and 2022.
  • Categorization of machine learning models and performance metrics.

Main Results:

  • Identified frequently predicted academic outcomes and associated learning features.
  • Detailed analysis of data sources, preprocessing, machine learning models, and performance metrics.
  • Exploration of the relationships between learning features and predicted outcomes.

Conclusions:

  • The study provides a comprehensive overview of predictive learning analytics in higher education.
  • Identified research gaps and future directions for the field.
  • Offers insights for researchers, educational stakeholders, and decision-makers.