Big Data in Education

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7/10 stars
based on  3 reviews
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Cost FREE
Start Date TBA

Course Details

Cost

FREE

Upcoming Schedule

  • TBA

Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4724 reviews

Course Description

The emerging research communities in educational data mining and learning analytics are developing methods for mining and modeling the increasing amounts of fine-grained data becoming available about learners. In this class, you will learn about these methods, and their strengths and weaknesses for different applications. You will learn 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 both at a theoretical level, and in terms of how to apply and execute them using standard software tools. Issues of validity and generalizability will also be covered, towards learning to establish how trustworthy and applicable the results of an analysis are.
Reviews 7/10 stars
3 Reviews for Big Data in Education

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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

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Hamideh Iraj profile image
Hamideh Iraj profile image
8/10 starsDropped
  • 70 reviews
  • 60 completed
4 years, 11 months ago
I skimmed the course slides to get a general understanding of educational data mining and decide whether to pursue it or not. Course content advantages: 1- Being right to the point and informative 2- Referring to and explaining related articles (a research facilitator) Course content disadvantages: 1- Lacking a high level view of educational data mining 2- Does not include which type of questions are asked in educational data mining and learning analytics. The examples are inadequate and they are too much distributed across weeks and does not give a general understanding of educational data mining and learning analytics 3- Lack of information about what kinds of datasets are available in education and how to integrate them for research
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Jeff Winchell profile image
Jeff Winchell profile image
7/10 starsCompleted
  • 91 reviews
  • 66 completed
4 years, 9 months ago
The professor talks too fast and makes a lot of claims about statistical techniques but gives no explanation what those techniques are.Still, the topic is of great interest to me, so I finished the course. Yes, the professor speaks too fast and references lots of items that are unexplained, but the class is worth it. I also like his sense of humor. He spent a lot of time monitoring the forums which was nice (and unusual in MOOCs). For those who have little background, you could consider this an Intro course to just be exposed to many techniques. I plan to dig further into the resources, now that the class is over. Also, there isn't much about Big Data in this course. They should drop that from the title. However, data mining, analytics, machine learning techniques are all covered. The professor is clearly at the forefront of applying this to educational data, so if you already know the main techniques, but don't know what works best ... The professor talks too fast and makes a lot of claims about statistical techniques but gives no explanation what those techniques are.Still, the topic is of great interest to me, so I finished the course. Yes, the professor speaks too fast and references lots of items that are unexplained, but the class is worth it. I also like his sense of humor. He spent a lot of time monitoring the forums which was nice (and unusual in MOOCs). For those who have little background, you could consider this an Intro course to just be exposed to many techniques. I plan to dig further into the resources, now that the class is over. Also, there isn't much about Big Data in this course. They should drop that from the title. However, data mining, analytics, machine learning techniques are all covered. The professor is clearly at the forefront of applying this to educational data, so if you already know the main techniques, but don't know what works best in education data, this is an excellent class to take.
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Nikhil sarpotdar profile image
Nikhil sarpotdar profile image
8/10 starsCompleted
  • 4 reviews
  • 4 completed
5 years, 11 months ago
Its true. The professor does talk very fast. But this is the first MOOC. I think the professor will slow down next time. I took this class to learn more about statistical/data analysis concepts. The professor does a touch on a lot of stuff but does not go into depth into most of them. However he is very very accessible and approachable. He participated in almost all the forum posts and was very helpful to students. One of his research associates also took the course with us and helped us with homework questions when we were stuck. The forum quality was exceptional. The course cohort was very small (only about 700 people or so were completing the course at the end) but most of them were doing very well in the quizzes (and completed all 8 of them even if they needed to complete only 6 to get 100% points.) This should tell you how involved they were. The professor also has a great sense of humor and is obviously very well known and well... Its true. The professor does talk very fast. But this is the first MOOC. I think the professor will slow down next time. I took this class to learn more about statistical/data analysis concepts. The professor does a touch on a lot of stuff but does not go into depth into most of them. However he is very very accessible and approachable. He participated in almost all the forum posts and was very helpful to students. One of his research associates also took the course with us and helped us with homework questions when we were stuck. The forum quality was exceptional. The course cohort was very small (only about 700 people or so were completing the course at the end) but most of them were doing very well in the quizzes (and completed all 8 of them even if they needed to complete only 6 to get 100% points.) This should tell you how involved they were. The professor also has a great sense of humor and is obviously very well known and well regarded in this particular field. Anybody curious about this field will do well to take this course. And if you decide you want to learn in depth you can do a master's at his school.
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  • 5 stars
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  • 3 stars
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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.