MIT has been a leader in online education for more than a decade, and many leading thinkers in the evolution of education technology have emerged from the Cambridge, Massachusetts campus. MOOC leader, edX, is run by Anant Agarwal, who is a professor at MIT, for example.
The path of progress has not proceeded unabated, however, and there have been many reason. This year EdX, the platform co-run by MIT and Harvard, gave researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) data on the second-by-second viewing habits of more than 100,000 learners perusing over 6.9 million video sessions. The team found that the following factors weigh heavily in the success or failure of online learning platforms:
- brevity (viewers tune out after six minutes)
- informality (professors seated at a desk, not standing behind a podium)
- lively visuals (rather than static PowerPoint slides)
- fast talkers (the most engaging professors spoke 254 words a minute)
- BUT more pauses (so viewers can soak in complex diagrams)
- web-optimized lessons (existing videos retroactively broken into shorter chunks are less effective than ones created with online audiences in mind)
Juho Kim is a Ph.D. candidate in the User Interface Design Group at MIT CSAIL. He designs interactive technologies for online education, with a focus on leveraging collective learner activities to enhance the video learning experience. His research introduces learnersourcing, a set of methods and tools that collect, process, and visualize large-scale learner activities. He is interested in applying established learning theories beyond small, in-person classrooms. As a result of these findings, he has been working with a team at MIT to develop a better learning platform. The result is LectureScape, which has the ambition of becoming the “YouTube for MOOCs.” I caught up with Kim to ask him about the vision for LectureScape, the needs it hopes to address, and how he anticipates it will grow.
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Peter High: Please provide an overview of LectureScape?
Juho Kim: LectureScape is an enhanced video player for educational content online, powered by data on learners’ video watching behavior. LectureScape dynamically adapts to thousands of learners’ collective video watching patterns to make it easier to rewatch, skim, search, and review. By analyzing the viewing data as well as the content itself, LectureScape presents educational videos in a more lively and dynamic way: a timeline shows which parts other users have most frequently watched; an interactive transcript lets users enter keywords to find relevant segments; a mechanism automatically creates word clouds and summaries of individual sections, as well as the whole presentation; and content from popular slides automatically appears in the following slide, as users will likely want to refer back to that information.
High: How was it developed?
Kim: There are heated debates online about the usefulness and pedagogical value of MOOCs, but not many people have actually studied what works and what doesn’t work with MOOCs. As researchers, we thought this was an important question to answer for a healthier discussion.
We chose videos because studies show that learners spend a majority of their time on MOOCs watching videos, and yet, the videos and the interfaces for watching them turn learners into passive consumers rather than into active participants. I think MOOCs opened doors to quality content for many people who otherwise wouldn’t have had a chance, but that’s not enough. Moving forward, I think MOOCs should strive to make the learning experience as rich and interactive as being in a classroom. Unlike the classroom where you get to naturally see if other students are engaged or confused, all the rich signals are lost in MOOCs because instructors and learners are disconnected. For instructors, it’s almost like they are talking to the wall, and for learners, the experience is like studying alone in the classroom. We thought we could fix this. What if the video player knew about how other learners watch the video and leverage these signals to help the learner feel more engaged and connected?
In reaching this goal, we thought exploring the data on how learners are using MOOC videos was the first step. We looked at clickstream data from four of the edX MOOCs to gain insights on learners’ video watching patterns. As we started taking these insights into the design of LectureScape, we were careful not to solely rely on data, because data doesn’t tell us everything. That’s why we took an iterative design approach common in human-computer interaction (HCI) research by closely observing, talking to, and pilot testing with real people. I think this approach gives us a more balanced view.
High: One of the biggest issues MOOCs have experienced has been student attrition. The CSAIL team noted the following observations on how to optimize MOOC content to help make them stickier. How have you taken these learnings into consideration in developing LectureScape?
Kim: The guidelines we identified from our data analysis were meant to encourage instructors and video editors to consider them as they make new videos. But we also thought the same data can improve the way existing videos are presented and delivered via the video player. Improving the content as well as the interface for watching it will have more impact on the learner’s experience. In designing LectureScape, we sought to turn an existing video into a “model” video we identified in the analysis work.
High: What was the process of developing the “model” videos?
Kim: We first brainstormed a set of dimensions that might affect learners’ engagement in a MOOC video, such as length, presentation style, speaking rate, etc. Then we took a three-step process to design LectureScape features.
First, we watched hundreds and hundreds of MOOC videos to gain insights and to see if video watching data accurately reflects the learner’s realistic interaction patterns. Second, we tested a number of hypotheses around each dimension we came up with, using real data. This process also helped us identify diverse learner goals such as re-watching a segment, searching for a specific slide, and skimming to get an overview. Finally, we built interactive technologies into our video player to reflect the factors that positively affect engagement and the diverse learner intents beyond passively watching through. The steps of manually watching and carefully verifying our assumptions matter, because simply relying on data can be misleading if we don’t have confidence in what we’re seeing.
High: Are there other issues or opportunities that you have identified to be solved or explore?
Kim: Even if videos are created and presented in a more engaging manner, there are still many other ways video learning can become more personalized. For example, a MOOC platform can compare your own learning trajectory against the collective trajectory, and recommend videos and modules that are tailored to your needs. The hope is that these techniques can help learners stay more engaged in a course.
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