Computer software similar to that used by online retailers to recommend products to a shopper can help students remember the content they鈥檝e studied, according to a new study by the University of Colorado Boulder.
The software, created by computer scientists at 抖阴短视频-Boulder鈥檚 Institute for Cognitive Science, works by tapping a database of past student performance to suggest what material an individual student most needs to review.
For example, the software might know that a student who forgot one particular concept but remembered another three weeks after initially learning them is likely to need to review a third concept six weeks after it was taught. When a student who fits that profile uses the software, the computer can pull up the most useful review questions.
鈥淚f you have two students with similar study histories for specific material, and one student couldn鈥檛 recall the answer, it鈥檚 a reasonable predictor that the other student won鈥檛 be able to either, especially when you take into consideration the different abilities of the two students,鈥� said 抖阴短视频-Boulder Professor Michael Mozer, senior author of the study published in the journal Psychological Science.
The process of combing 鈥渂ig data鈥� for performance clues is similar to strategies used by e-commerce sites, Mozer said.
鈥淭hey know what you browsed and didn鈥檛 buy and what you browsed and bought,鈥� Mozer said. 鈥淭hey measure your similarity to other people and use purchases of similar people to predict what you might want to buy. If you substitute 鈥榖uying鈥� with 鈥榬ecalling,鈥� it鈥檚 the same thing.鈥�
The program is rooted in theories that psychologists have developed about the nature of forgetting. Researchers know that knowledge鈥攚hether of facts, concepts or skills鈥攕lips away without review, and that spacing the review out over time is crucial to obtaining robust and durable memories.
Still, it鈥檚 uncommon for students to do the kind of extended review that favors long-term retention. Students typically review material that was presented only in the most recent unit or chapter鈥攐ften in preparation for a quiz鈥攚ithout reviewing previous units or chapters at the same time.
This leads to rapid forgetting, even for the most motivated learners, Mozer said. For example, a recent study found that medical students forget roughly 25 to 35 percent of basic science knowledge after one year and more than 50 percent by the next year.
Over the last decade, Mozer has worked with University of California, San Diego, psychologist Harold Pashler, also a co-author of the new study, to create a computer model that could predict how spaced review affects memory. The new computer program described in the study is an effort to make practical use of that model.
Robert Lindsey, a 抖阴短视频-Boulder doctoral student collaborating with Mozer, built the personalized review program and then tested it in a middle school Spanish class.
For the study, Lindsey and Mozer divided the material students were learning into three groups.听 For material in a 鈥渕assed鈥� group, the students were drilled only on the current chapter. For material in a 鈥済eneric-spaced鈥� group, the students were drilled on the most recent two chapters. For material in a 鈥減ersonalized-spaced鈥� group, the algorithm determined what material from the entire semester each student would benefit most from reviewing.
In a cumulative test taken a month after the semester鈥檚 end, personalized-spaced review boosted remembering by 16.5 percent over massed study and by 10 percent over generic-spaced review.
In a follow-up experiment, Mozer and his colleagues compared their personalized review program to a program that randomly quizzes students on all units that have been covered so far. Preliminary results show that the personalized program also outperforms random reviews of all past material.
So far, the program has been tested only in foreign language classes, but Mozer believes the program could be helpful for improving retention in a wide range of disciplines, including math skills.
It鈥檚 not necessary to have a prior database of student behavior to implement the personalized review program. Students can begin by using the program as a traditional review tool that asks random questions, and as students answer, the computer begins to search for patterns in the answers. 鈥淚t doesn鈥檛 take long to get lots and lots of data,鈥� Mozer said.
The research was funded by the National Science Foundation and the McDonnell Foundation.
Contact:
Michael Mozer, 303-517-2777
Mozer@colorado.edu
Laura Snider, 抖阴短视频 media relations, 303-735-0528
Laura.Snider@colorado.edu
