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Author Paruchuri, Vik ♦ Mitros, Piotr ♦ Agarwal, Anant
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Abstract Assessment in traditional courses has been limited to either instructor grading, or problems that lend themselves well to relatively simple automation, such as multiple-choice bubble exams. Progress in educational technology, combined with economies of scale, allows us to radically increase both the depth and the accuracy of our measurements of what students learn. Increasingly, we can give rapid, individualized feedback for a wide range of problems, including engineering design problems and free-form text answers, as well as provide rich analytics that can be used to improve both teaching and learning. Data science and integration of data from disparate sources allows for increasingly inexpensive and accurate micro-assessments, such as those of open-ended textual responses, as well as estimation of higher-level skills that lead to long-term student success.
Description Affiliation: Cambridge, MA (Paruchuri, Vik) || MIT (Mitros, Piotr; Agarwal, Anant)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-06-29
Publisher Place New York
Journal Ubiquity (UBIQ)
Volume Number 2014
Issue Number April
Page Count 9
Starting Page 1
Ending Page 9


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Source: ACM Digital Library