Invention of the Year Nominee: New Testing Method to Assess Foreign Language Aptitude

Invention of the Year Nominee: New Testing Method to Assess Foreign Language Aptitude

New foreign language aptitude tests invented by researchers at the University of Maryland can predict how advanced someone can become in gaining proficiency at a foreign language after the age of 12.

The invention, one of nine nominees for the Invention of the Year Award, is a battery of tests created by Area Director-Second Language Acquisition Catherine Doughty of UMD’s Center for Advanced Study of Language (CASL), along with research team members Anita Bowles, Michael Bunting, Susan Campbell, Meredith Hughes, Scott Jackson, Joel Koeth, Jared Linck, Meredith Mislevy, Noah Silbert, Benjamin Smith, and Medha Tare.

Doughty describes the new battery of tests as a gold-standard that can predict which individuals will be able to attain advanced proficiency. The battery of tests, known as Hi-LAB, or High-Level Language Aptitude Battery, makes use of the recent models on second language acquisition and the working memory system.

“Only the Hi-LAB directly engages the cognitive processes comprising language aptitude,” said Doughty. “Other solutions use multiple-choice proxies rather than direct testing.”

The device also presents the results in a structured manner as an aptitude profile, with the scores and recommendations for how teaching methods could be modified to leverage the candidate’s cognitive strengths.

“Other solutions simply report one overall score,” Doughty explained.

The Hi-LAB actually ensures that each individual receives personalized instructions matched to their cognitive strengths. Hi-LAB was funded by the Research Directorate at the National Security Agency (NSA).

Hi-LAB’S government and military users include Human Resources and the Associate Directorate of Education and Training at the NSA.

Describing the Hi-LAB’s wide variety of uses, Doughty said that if a company has already hired personnel, the test results can help select individuals from the existing workforce who will have the best potential to benefit from language training, which can be costly for companies in terms of training expenses and the time employees spend away from the job.

“Hi-LAB aptitude profiles inform the matching of the most effective instructional materials and techniques to each and every individual so that they all have the best chance of succeeding,” Doughty said.

Results from Hi-LAB could also have secondary uses as part of the Language Management Systems (LMS) that are already being used to boost the adaptability levels of individuals, extending beyond mastery.

Doughty said that if Hi-LAB is inserted into the LMS, it can identify the activities and materials that will be most effective for students, “…and they will not waste any time in modes of learning that are mismatched to their aptitude,” she said.

Learn more about this invention and others at: research.umd.edu or www.otc.umd.edu.

 

 

 

April 20, 2015


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