Big Data and Education from edX

Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research on learning.

In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will examine the methods being developed by researchers in the educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You'll also gain experience with standard data mining methods frequently applied to educational data. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications.

The course will discuss how to use each method to answer education research questions, and to drive intervention and improvement in educational software and systems. Methods will be covered at a theoretical level, and in terms of learning how to apply them in Python or using software tools like RapidMiner. We will also discuss validity and generalizability; establishing how trustworthy and applicable the analysis results.

What's inside

Learning objectives

Key methods for educational data mining
How to apply methods using python's built-in machine learning library, scikit-learn

How to apply methods using standard tools such as rapidminer
How to use methods to answer practical educational questions

Key methods for educational data mining
How to apply methods using python's built-in machine learning library, scikit-learn
How to apply methods using standard tools such as rapidminer
How to use methods to answer practical educational questions

Syllabus

Week 1: Prediction ModelingRegressors Classifiers

Week 2: Model Goodness and ValidationDetector Confidence Diagnostic Metrics* Cross-Validation and Over-Fitting

Week 3: Behavior Detection and Feature EngineeringGround Truth for Behavior Detection Data Synchronization and Grain SizeFeature Engineering Knowledge Engineering

Week 4: Knowledge InferenceKnowledge Inference Bayesian Knowledge Tracing (BKT)Performance Factor Analysis Item Response Theory

Week 5: Relationship MiningCorrelation Mining Causal MiningAssociation Rule Mining Sequential Pattern Mining* Network Analysis

Week 6: VisualizationLearning Curves Moment by Moment Learning GraphsScatter Plots State Space Diagrams* Other Awesome EDM Visualizations

Week 7: Structure Discovery Clustering Validation and SelectionFactor Analysis Knowledge Inference Structures

Week 8: Discovery with ModelsDiscovery with Models Text Mining* Hidden Markov Models

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches data mining methods used by practitioners in educational data mining, learning analytics, learning-at-scale, student modeling, and AI

Explores data mining methods with Python and tools like RapidMiner

Involves Python coding for various applications, such as prediction modeling and identifying learner behavior

Teaches data mining methods to answer research questions and improve educational software and systems

Can be useful to enhance skills for tasks in industry or academia

Provides opportunities to learn with experienced instructors in the field

Reviews summary

Data mining for beginners

Learners say Big Data and Education is a beginner-friendly introduction to data mining, but recommend learners be familiar with Machine Learning first. While Professor Baker speaks quickly and in "bullet points", the course includes RapidMiner, a data mining tool. Learners should allocate time for extra reading to understand the concepts better and use the helpful forum posts as support.

Forum posts provide helpful support.

"Forum posts are very helpful though"

RapidMiner is used for data mining.

"RapidMiner is the data mining tool used for this class."

Good for learners new to data mining.

"If you're not famiar with data mining, I would recommend to take Machine Learning with A.Ng first."

Professor speaks quickly, using "bullet points."

"Prof. Baker speaks fast and in "bullet points"."

Requires extra time for reading to supplement instruction.

"Allocate an extra time for understanding the concept"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Big Data and Education with these activities:

Study notes from previous courses

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Revisiting notes from previous courses on prediction modeling, regressors, and classifiers will provide a solid foundation for your learning in this course.

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Identify relevant notes from previous courses
Review notes on prediction modeling techniques
Refresh your understanding of regressors
Review notes on classifiers
Seek clarification on any concepts that need reinforcement

Participate in study groups with other students

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Engaging in study groups will provide opportunities for collaboration, peer learning, and reinforcement of concepts.

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Identify or form a study group with other students enrolled in the course
Set regular meeting times and establish study goals
Review course materials together and discuss concepts
Work on assignments or projects collaboratively

Explore tutorials on Python's scikit-learn library

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Going through tutorials on Python's scikit-learn library will equip you with the necessary skills to apply methods from this course using Python.

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Identify relevant tutorials on scikit-learn
Follow tutorials to install scikit-learn and its dependencies
Work through examples using scikit-learn
Experiment with different algorithms and data sets

Five other activities

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Solve practice exercises on educational data mining

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Solving practice exercises will reinforce your understanding of educational data mining concepts and improve your problem-solving skills.

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Find online resources or textbooks with practice exercises
Attempt to solve the exercises independently
Review solutions and identify areas for improvement
Seek help from instructors or peers if needed

Develop a data visualization tool for educational data

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Creating a data visualization tool will enhance your understanding of data visualization techniques and their application in educational settings.

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Identify a specific educational data set to work with
Choose appropriate data visualization techniques
Develop the data visualization tool using a programming language or software
Test and refine the tool to ensure its accuracy and usability

Assist fellow students as a mentor

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Mentoring others will strengthen your understanding of the course material and enhance your communication and teaching skills.

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Identify opportunities to assist other students, such as in discussion forums or study groups
Prepare to answer questions and provide guidance to fellow students
Engage in respectful and constructive communication
Seek feedback from students to improve your mentoring skills

Develop a blog post on a topic related to educational data mining or learning analytics

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Creating a blog post will encourage you to synthesize and communicate your understanding of a chosen topic in educational data mining or learning analytics.

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Identify a specific topic within educational data mining or learning analytics to focus on
Research and gather relevant information from credible sources
Organize and structure your content in a logical and engaging manner
Write and edit your blog post, ensuring clarity and accuracy
Publish your blog post on a suitable platform and promote it to your target audience

Attend a workshop on advanced topics in educational data mining or learning analytics

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Attending a workshop will provide you with exposure to cutting-edge developments and best practices in educational data mining or learning analytics.

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Identify relevant workshops in your area or online
Register for the workshop and make necessary arrangements
Attend the workshop and actively participate in discussions
Follow up with speakers or attendees to expand your network and knowledge

Career center

Learners who complete Big Data and Education will develop knowledge and skills that may be useful to these careers:

Educational Data Analyst

Educational Data Analysts use data to improve educational outcomes. They work with educators and administrators to identify trends, patterns, and other useful information in educational data. The skills you will learn in this course could be applied directly to this role, as it concerns analyzing educational data.

See salaries and explore the career path for Educational Data Analyst

Researcher

Researchers conduct research on a variety of topics, including education. They use research methods to collect and analyze data, and they write reports and articles about their findings. The skills you will learn in this course could be applied directly to this role, as it concerns using data to conduct research in the field of education.

See salaries and explore the career path for Researcher

Educational Researcher

Educational Researchers use research methods to study educational issues and improve educational practices. They conduct research on a variety of topics, including teaching and learning, student assessment, and educational policy. The skills you will learn in this course could be applied directly to this role, as it concerns using data to inform educational research.

See salaries and explore the career path for Educational Researcher

School Administrator

School Administrators oversee the operation of schools. They work with teachers, staff, and parents to create a positive learning environment for students. The skills you will learn in this course could be applied directly to this role, as it concerns using data to inform decision-making in educational settings.

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Program Evaluator

Program Evaluators assess the effectiveness of educational programs. They work with program staff to collect and analyze data on program outcomes. The skills you will learn in this course could be applied directly to this role, as it concerns using data to evaluate educational programs.

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Learning Scientist

Learning Scientists study how people learn. They conduct research on a variety of topics, including cognition, motivation, and learning environments. The skills you will learn in this course could be applied directly to this role, as it concerns using data to inform research on learning.

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Instructional Designer

Instructional Designers develop and deliver learning experiences. They work with subject matter experts to create instructional materials and activities that meet the needs of learners. The skills you will learn in this course could be applied directly to this role, as it concerns designing and delivering educational experiences using data.

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Policy Analyst

Policy Analysts develop and evaluate public policies. They work with policymakers to identify and solve problems. The skills you will learn in this course could be applied directly to this role, as it concerns using data to inform policy decisions in the field of education.

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Teacher

Teachers develop and deliver instruction to students. They work with students to create a positive learning environment and help them reach their full potential. The skills you will learn in this course could be applied directly to this role, as it concerns using data to inform teaching practices.

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Education Consultant

Education Consultants provide guidance to schools and other educational organizations. They work with educators and administrators to improve educational outcomes. The skills you will learn in this course could be applied directly to this role, as it concerns using data to inform educational consulting.

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Machine Learning Engineer

Machine Learning Engineers help build and implement machine learning models. They collaborate with Data Scientists to design and develop machine learning solutions to business problems. The skills you will learn in this course may be useful in roles related to data analysis and data science.

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Data Analyst

Data Analysts examine data and use their expertise in statistics and data analysis to find trends, patterns, and other useful information. They create visualizations and reports that aid in decision-making and problem-solving. The skills you will learn in this course may be useful in roles related to data analysis and data science.

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Data Engineer

Data Engineers design, build, and maintain data systems. They work with data in a variety of formats and develop solutions for storing, processing, and analyzing data. The skills you will learn in this course may be useful in roles related to data analysis and data science.

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Data Scientist

Data Scientists help organizations understand data and make data-driven decisions. They combine knowledge of statistics and programming to build predictive models, analyze data, and draw meaningful conclusions from data. The skills you will learn in this course may be useful in roles related to data analysis and data science.

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Business Analyst

Business Analysts use data to understand business processes and identify opportunities for improvement. They work with stakeholders to define business requirements and develop solutions that meet those requirements. The skills you will learn in this course may be useful in roles related to data analysis and business intelligence.

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