CrowdCamp Report: Collaborative Learning in a Video Lecture

During CrowdCamp at HCOMP 2014, we developed a project called “The Dancing Professor” that earned its name from the motions professors make when trying to explain visual ideas with physical body movements instead of making an illustration. While participating in online courses, students are often confused by ideas presented in a video lecture. How can we aid learning and improve the illustration of concepts and ideas for online courses? With an augmented web-learning interface, students can give and receive help at certain time intervals throughout a lecture.

To test this approach, we built a prototype that wraps a YouTube video and interacts with the YouTube API to present events and interactions at certain timestamps of the video. Users have three options to interact with the video aside from the normal YouTube controls: “I’m Confused,” “I Know This!” and “Proceed, I’m Good.”


When a user clicks “I’m confused,” a request is sent to see if there is any help available at the current timeframe. If help is available, it is presented as a learning opportunity with multiple choice answers, ranging from good answers to bad answers. A user can see explanations of both good and bad answers and selects an answer they would like to learn about. When selecting an answer, feedback is given about the answer as to whether it is good or bad, and how it relates to the best answer. If no data is available, users are notified that no data is available and asked if they can contribute information about the timeframe.

Users can contribute new learning experiences for a portion of video through an “I Know this!” link. They can provide their own questions or answers and view or vote on input from other students. Clicking either of the “I’m Confused” or “I Know This!” links will pause the video. Clicking “Proceed, I’m Good!” will play the video and clear the help interface below.

Juho Kim’s prior research on identifying confusion, interest, and importance in videos was integrated into this interface. To ensure understanding during confusing timeframes, the interface anticipates confusion and automatically pauses the video immediately after a confusing concept is covered. The learning experience is shown and the user can choose to either interact with it or continue on.

Feedback for the project at CrowdCamp included improving the design of the question-answer interface, improving the incentives to provide explanations, and re-framing the help to be less of a question-answer interaction and more of a scenario-explanation interaction. Ideas for future work include allowing instructors to moderate and improve learning experiences in the interface, recording user interactions with the interface to track whether learning is being improved, and abstracting the interface for reuse on other video-learning platforms.

Josh Hibschman, Northwestern University
Juho Kim, MIT
Kanya (Pao) Siangliulue, Harvard University
Michael Richardson, Carnegie Mellon University

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Josh Hibschman

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