Predictive Modeling in Learning Analytics
date_range Débute le 13 août 2018
event_note Se termine le 27 janvier 2019
list 3 séquences
assignment Niveau : Intermédiaire
chat_bubble_outline Langue : Anglais
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Les infos clés

credit_card Formation gratuite
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timer 15 heures de cours

En résumé

This course will introduce you to the tools and techniques of predictive models as used by researchers in the fields of learning analytics and educational data mining. It will cover the concepts and techniques that underlie current educational “student success” and “early warning” systems, giving you insight into how learners are categorized as at-risk through automated processes.

You will gain hands-on experience building these kinds of predictive models using the popular (and free) Weka software package. Also, included in this course is a discussion of supervised machine learning techniques, feature selection, model fit, and evaluation of data based on student attributes. Throughout the course, the ethical and administrative considerations of educational predictive models will be addressed.

  • How to use the Weka toolkit to analyze educational data and make predictions about student outcomes 
  • Techniques underlying supervised machine learning, including decision trees and naïve Bayes modeling
  • How to apply feature selection to identify relevant attributes in the data
  • How to rigorously evaluate educational predictive models
  • The state of the practice in current generation educational predictive models

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Les prérequis

We highly recommend that you take the previous course in this series before beginning this course:
Cluster Analysis

This course is intended for those who have a bachelor’s degree and are interested in developing learning and data science skills for employment in education, corporate, nonprofit, and military sectors. Experience with programming and statistics will be beneficial to participants.

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Le programme

Week 1: Prediction

  • Predictive models vs. explanatory models
  • The predictive modeling lifecycle
  • Predictive models of student success
  • Ethical considerations with predictive models
  • Overview of the state of the practice in educational predictive models

Week 2: Supervised Learning

  • Supervised machine learning techniques, including Decision Trees and Naive Bayes

Week 3: Model Evaluation

  • Making predictions
  • Model evaluation and comparison
  • Practical considerations
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Les intervenants

Christopher Brooks
Research Assistant Professor, School of Information, University of Michigan
University of Michigan

Craig Thompson
Learning Analytics Research Analyst at the Centre for Teaching, Learning and Technology
University of British Columbia

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Le concepteur

University of Texas at Arlington
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La plateforme

EdX est une plateforme d'apprentissage en ligne (dite FLOT ou MOOC). Elle héberge et met gratuitement à disposition des cours en ligne de niveau universitaire à travers le monde entier. Elle mène également des recherches sur l'apprentissage en ligne et la façon dont les utilisateurs utilisent celle-ci. Elle est à but non lucratif et la plateforme utilise un logiciel open source.

EdX a été fondée par le Massachusetts Institute of Technology et par l'université Harvard en mai 2012. En 2014, environ 50 écoles, associations et organisations internationales offrent ou projettent d'offrir des cours sur EdX. En juillet 2014, elle avait plus de 2,5 millions d'utilisateurs suivant plus de 200 cours en ligne.

Les deux universités américaines qui financent la plateforme ont investi 60 millions USD dans son développement. La plateforme France Université Numérique utilise la technologie openedX, supportée par Google.

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