a Qstream to be delivered to you
Courses cover a wide variety of topics and more are being added every day. (By the way, if you have a great idea for a Qstream you could earn money by creating it yourself.)
through answering questions
They are sent to you in small amounts (typically 1 or 2 a day) on a regular schedule via email, the Web or RSS. They can be accessed from a desktop browser or a mobile device such as an iPhone or BlackBerry.
and master the material
The sequence of questions will adapt based on your answers. To improve retention, questions typically repeat several times. Get a question wrong and it repeats sooner. Get it right one or more times in a row and it is retired from the Qstream. Retire all questions to complete the Qstream.
In addition to the material presented here, you can also view our list of frequently asked questions for answers to common questions.
For more information on the peer-reviewed research behind spaced education, please visit our Research section.
Qstream is a platform designed to allow learners and teachers to harness the educational benefits of spaced education. Spaced education is a novel method of online education developed and rigorously investigated by Dr. B. Price Kerfoot (Associate Professor, Harvard Medical School).
In addition, spaced education is extremely well-accepted by learners.
The spacing effect refers to the psychology research finding that information which is presented and repeated over spaced intervals is learned and retained more effectively, in comparison to traditional bolus ('binge-and-purge') methods of education.
The testing effect refers to the research finding that the long-term retention of information is significantly improved by testing learners on this information. Testing is not merely a means to measure a learner's level of knowledge, but rather causes knowledge to be stored more effectively in long-term memory.
The spaced education methodology is content-neutral and thus can be utilized to learn most anything. Potential applications range from teaching chemistry concepts to high school students to reinforcing Arabic language skills among health workers in the Middle East. It can also be used to reinforce educational material which was initially presented in the classroom. The full multi-media capabilities of the Internet can be harnessed to create a rich and effective learning experience.
Our method has been proven to work. Data from over 10 large randomized controlled trials (RCTs) demonstrate that it is effective, efficient, addictive and changes behavior. Some key highlights are:
|Effective||Generates improved long-term retention of knowledge in only minutes a day.||
|Changes Behavior||Proven to positively impact on-the-job performance and change even engrained behavior.||
|Efficient||Adaptive algorithm reduces time to acquire same amount of knowledge as non-adaptive methods||
|Addictive||Learning pushed on a daily basis requiring no more than 5 minutes a day and delivering instant feedback on progress is proven to be addictive.||
For more information on the randomized controlled trials and other peer reviewed research, please visit our Research section.
Spaced education combines the educational benefits of both the spacing and testing effects. Qstream material is delivered electronically to learners at regular intervals in a test-question format. Upon submitting answers to questions, learners receive immediate feedback and educational material on that topic. The Qstream material is then reinforced over spaced intervals to take advantage of the spacing effect. The delivery intervals, the spacing intervals and the number of reinforcements can be personalized to meet the specific needs of the learners.
Qstreams use an adaptive reinforcement algorithm to customize the spacing and content of the Qstream material for each learner based on his or her demonstrated knowledge level. By reducing unnecessary reinforcement of already-mastered content among learners with high baseline knowledge levels, Qstream's adaptive reinforcement has been shown to improve learning efficiency by 38%.
Here is an example how a Qstream is structured: