"Why Online Education Won't Replace College — Yet" I Disagree And Here Is Why

Recently, I read an article titled, Why Online Education Won’t Replace College–Yet. The article is most likely a response the recent success of Massive Open Online Courses (MOOCs) such as those offered by Udacity and Coursera. The author, David Youngberg an Assistant Professor of Economics, presents 5 reasons why online education won’t replace college. I disagree with his reasons, so I thought I should share more details. I will go through each of his 5 reasons.

  • It’s too easy to cheat. Cheating has always been an issue in education, and I think it always will be. Students even manage to cheat in ivory tower institutions. Online Colleges such as University of Phoenix have been very successful and cheating can easily exist in that scenario. I do think online classes make cheating easy, but I don’t really see that stopping the success of online education.
  • Star students can’t shine. This is just simply not true. The star students are the ones answering questions in the forums and getting the assignments done first. This is very similar to the star students in a regular college setting. The brightest students have their work done first and are frequently found helping their peers. Udacity has even hired one of the former star students.
  • Employers avoid weird people. Just because a person takes an online course does not make him/her weird. Taking an online course means a person is willing to find cheaper and easier ways to solve old problems. It also means the person has the initiative to go out and complete something. All of those traits are attractive to companies. The problem here is credentials. MOOCs have not yet solved the credential problem. MOOCs don’t offer degrees or widely-acknowledged certifications yet. Companies want to hire people with degrees, not people with a piece of paper stating “I completed an online course.” I think MOOCs will quickly figure out this problem. Also, many of the Coursera and Udacity students are former college graduates. Why are they now weird for taking an online course?
  • Computers can’t grade everything. Not so fast. Earlier this year, Kaggle and the Hewlett Foundation sponsored a competition to see if technology could be created to automatically grade standardized test essays. Well, the competition was a big success. See the full press release. The competition results will probably not generalize to all essays, but the technology to automatically grades papers is not that far away. Also, Coursera is experimenting with crowd-sourced grading of papers. One student grades the papers of 4 unknown classmates, then a final score is calculated by a computer. See the Peer Assessments section on the Coursera website. This technique may even be more effective than grading by a single highly-trained person.
  • Money can substitute for ability. The author argued that students will pay for tutors, buy dishwashers or anything else to help get better grades. I do not think banks are going to start handing out loans for dishwashers, so students can have more time for homework. I think MOOCs will allow students to learn without building massive amounts of debt.

Now, I cannot say with certainty whether or not MOOCs will replace traditional colleges. I just did not believe the above reasons are what will determine the outcome.

On a side note, this blog is focused on material about learning to become a data scientist. I think MOOCs are going to be hugely helpful for people wishing to obtain data science skills.

Map of Kaggle Submissions

See this interactive map of Kaggle Submissions. The map is a nice example of data visualization. The data is much easier to see on a map than in a data table. Nice work by Ramzi Ramey of Kaggle.

Big Data for Organization (Infographic)

Here is a nice infographic about some challenges of big data. It covers the problems that organizations face when dealing with the “three Vs” of big data.

  1. Volume
  2. Variety
  3. Velocity

Data Science in the NFL

Data Science in the NFL

This is an excellent blog post about a unique use of data science.  Data science can help American football teams select better team-mates.

Free Textbook: Mining of Massive Datasets

A few professors from Stanford University have released version 1.1 of their textbook, Mining of Massive Datasets. The book has been created from materials used for a couple of Stanford computer science classes including large-scale data-mining and web mining. The book looks excellent and really focuses on the analysis of data at a large scale. Some people would use the word bigdata. Below is a list of some of the topics covered in the textbook.

  • data mining
  • map-reduce
  • clustering
  • recommender systems
  • and more

The book is free for download, or available from Cambridge University Press.

Olympic Numbers Infographic

As the Olympics are coming to a close, here is one more infographic. There are a lot of nice numbers here. The athlete caloric intake section is fun. Michael Phelps must be eating all the time. There is also a section about Acer computers. Acer installed 11,000 computers and 900 servers. Other than competition results, what other data was being collected? I would love to hear more about that Do you have any idea about what other data is collected at the olympics?

Strata Healthcare Conference

O’Reilly just announced the creation of a new conference focusing on data science and healthcare. It is named StrataRx 2012. The conference will take place in San Francisco on October 16-17, 2012.

Social Media and The Olympics Infographic

New Web Intelligence and Big Data Coursera Class

Thanks to Ed for leaving the comment yesterday. I have reposted the comment here because I thought it was so good.

Looks like Coursera added a new data science course entitled “Web Intelligence and Big Data” while nobody was looking! Plus, it starts at the end of the month, for those who can’t wait until the UW Intro to Data Science course to be scheduled.

Here is a link to the Coursera Web Intelligence and Big Data Course. The course is looking to focus on map-reduce and parallel programming applied to data problems.

Neo4j and Bioinformatics Webinar

Neo Technology, the company behind the graph database Neo4j, is hosting a webinar on Thursday. Pablo Pareja from the Bio4j project will provide an overview of bioinformatics and neo4j, as well as some applications.

Bioinformatics can be viewed as data science for biology. Bioinformatics was cool before data science was even a term.

If you are interested in learning more about bioinformatics and graph databases, the register for this webinar and start learning.