MOOCs: The Myth Of Online Classrooms

by Sandra Helsley, Ajeeta Dhole, Kate Rushton

In The Myth of the Paperless Office, Sellen and Harper write, “For each limit, for each set of actions that paper prevents, there is a set of actions that it enables. In other words, each limitation is also an affordance.” In a previous post, Ignacio, Andrew, and Sydney wrote about the impersonal nature of online education. In this post, we apply the concept of affordance to MOOCs, or Massive Open Online Courses, and consider how online education does and does not afford certain types of learning behavior, just as paper does and does not afford certain types of work processes. As with the paper vs. digital debate, the opportunities and limitations of MOOCs may be understood by comparing affordances of an online course experience with an in-person course at a university.

MOOCs such as Coursera or Udacity provide open and (typically) free access to high-quality educational resources to anyone with a computer and an internet connection. Online courses are designed to afford autonomy to students. Students are not held to instructor-defined deadlines, and are able to self-pace their progress. Students are free to explore online content in a non-linear fashion, unlike a physical classroom setting, where the flow of the course is choreographed by instructors, the flexibility and autonomy allows each student to personalize their own educational experience to meet their specific learning needs and abilities. Additionally, the “massive” numbers of student subscribers in each class afford access to a much larger and diverse peer network to learn from and collaborate with.

However, analyses of MOOCs reveal many problems with the online format that may, in part, be attributed to their open-ended affordances. Online courses have notoriously poor completion rates: studies estimate that fewer than 1 in 10 students who sign up for a course complete it, and in at least one case the number was lower than 5 percent. In contrast, although a comparison cannot be made for specific classes, UC Berkeley has a retention rate of around 95%.  Part of the issue is certainly economic: most MOOCs are free and open to all, and the cost of signing up is low, but so is the penalty for failing to finish. Completion rates are low even for paid online courses (somewhere in the neighborhood of 30%).

Aside from economics, the difference in completion rates between online and offline classes may be attributed in large part to the affordances of a physical classroom. Face-to-face interactions have two main benefits for learning: interactivity and accountability. Online courses may offer some proxy for attendance (e.g., a “who’s online now” feature), but there is no real replacement for the transparency of face-to-face interactions. In a physical classroom, students are expected to attend classes for the duration of the lesson, pay attention to the instructor, answer questions when asked, and complete assignments by instructor-defined deadlines. None of the above applies online. When a student’s motivation flounders, classroom norms compel him to retain some semblance of participation, but the online student may simply log off.

The lack of physical presence also has implications for the quality of learning: in a traditional class, instructors may gauge the effectiveness of their teaching based on students’ facial expressions and body language. This also helps the instructor identify students who are struggling. In contrast, MOOC students are anonymous and too numerous to customize for different learning styles. Additionally, lectures are usually pre-recorded, so spontaneous one-on-one attention is nearly impossible.

MOOCs offer a standardized, one-size-fits-all approach to education that demands a high level of self-motivation and independent learning. Given these constraints, it’s not that surprising that only a subset of students succeed. In fact, the students who do well are autodidacts who have the least need of a free university education.

What does this spell (pun intended) for the future of online education? MOOCs have great potential to disrupt traditional educational expectations. With flexibility, autonomy, and open access to top-notch educators, the benefits of online learning would seem to outweigh its inadequacies. Yet the low completion rates suggest that so far, MOOCs are a bit of a bust.

Perhaps if MOOCs are to be fully successful, society needs to take a different approach to how we think about online courses. As with DanTech “going paperless,” online education requires a paradigm shift: we need to change the underlying work processes of taking a class online, instead of pretending to take a class as per normal.

There are a number of ways this has been done successfully. Websites like Codecademy offer programming courses through comparatively short, guided modules. Students who are interested in learning can sign up for free and go through any number of hands-on technical exercises, with less time spent watching lectures than on websites such as Udacity or Coursera.

This shorter, step-by-step method affords a smaller time requirement, while still providing the restrictive affordances that the more open-ended courses lack. Added gamification encourages students to earn points for completed modules, and unlock achievement badges when certain tasks are completed.

In a few cases, lessons from the online Khan Academy are assigned to students as homework, allowing teachers to spend less time lecturing, and devote more time in class providing customized one-on-one feedback, especially to those who might be struggling and need more help. In this way, MOOCs can be used to augment in-class education, rather than supplant it entirely.

Online education is still a nascent innovation. It seems unlikely that the effectiveness and stature of traditional classroom education will be superseded by online education in the near future. But there is no denying that MOOCs have the potential to disrupt the privileged nature of classroom education and democratize education for the masses.


Big Data and Qualitative Research

By Isha Dandavate, Sophie Barness, Seema Hari

Big data analysis has become a widely used tool– Netflix used it to inform the creation of “House of Cards,” Nate Silver used it to predict the results of the presidential elections, and Grameen Foundation uses it to better tailor financial services for poor households. However, big data comes with its own set of problems. This New York Times op-ed piece, by David Brooks, discusses situations in which data doesn’t work; and coincidentally these are situations in which the strengths of qualitative research could be leveraged.

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Digital Humanities: Leveraging Quantitative Methods for Literary Analysis

by Christine Petrozzo, Vaidyanath Venkitasubramanian, Sayantan Mukhopadhyay

Novels, Authors and The Big Data Analysis

Man has always been looking for ways to discover insights about culture. Today, one of the biggest tools at our disposal is the large volume of data available through technological methods. Up until recent times, analyzing a handful of texts did not yield much, namely regarding the context in which the text was written or additional interesting details explaining the reasons influencing a particular author in history. However, with the advent of big data and computational techniques, the fields of humanities and social sciences have evolved, helping these researchers identify statistical patterns and create data-driven hypothesis testing.

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Crowdfunding : The Kickstarter Phenomenon

by Bharathkumar Gunasekaran, Divya Anand, Eungchan Kim

Kickstarter is the world’s largest crowdfunding platform for funding creative projects. It was launched in 2009 and has gained a lot of traction since then. Given the amount of media and user attention this website has been garnering, we believe that it would be interesting to look at this crowdfunding phenomenon through the lens of E.Roger’s ‘Diffusion of Innovation’ theory. The key elements of this analysis include an overview of: (i) The innovation (ii) Communication Channels (iii) Time and (iv) Social System

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Negotiating Sentiment Analysis

by Scott Martin, Luis Aguilar, Jenton Lee

In an information based ecosphere actors can create, retrieve, and analyze vast amounts of data with increasing efficiency.  Just as individuals struggle to maintain control of personal information and communications, organizations face pressure to protect their Trade Secrets and Intellectual Property. IBM has developed a new business tool–IBM Security Intelligence with Big Data–to identify disgruntled employees who present a greater risk of leaking sensitive corporate information.  This tool collects and compares employee corporate communication and public statements (such as comments on social networks) and conducts a sentiment analysis identifying individuals who are presenting divergent point-of-views internally and externally.

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Online Education, Impersonal Meanings

By: Ignacio Perez, Andrew Win, Sydney Friedman

Online education is becoming an extremely popular means of obtaining an education. In reading The New York Times piece, “Revolution Hits the Universities”, various questions come into fruition that relate to the sociological analyses Fischer explores with regards to the telephone. The article points out that “last May, about 300,000 people were taking 38 courses taught by Stanford professors and a few other elite universities. Today, they have 2.4 million students, taking 214 courses from 33 universities, including eight international ones,” which makes us wonder if online education has the potential to reach the status of “commonplace” as described by Fischer.

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Call Me, Maybe … But Not on That Device

Call Me, Maybe … But Not on That Device
by: Deb Linton, Corey Hyllested, Lazar Stojkovic

Today, savvy consumers are aware of the symbiotic relationship between users, technology platforms, and their producers. This wasn’t always the case. In “America Calling,” the nascent Bell telephone network and its proprietors didn’t appreciate the emergent behaviors in its users until it understood how to monetize them. Bell saw its recommended use of the new technology as the only one valid to be supported and endorsed. While we may like to think of this problem as limited to early 20th century, we see divergent views of how to incorporate user behavior even today.

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