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Title: Learning false friends across contexts
Authors: Abou-Khalil, Victoria
Ogata, Hiroaki  kyouindb  KAKEN_id
Author's alias: 緒方, 広明
Keywords: Learning Analytics
Ubiquitous learning
False Friends
Computer Supported Language Learning
Issue Date: 2018
Publisher: Association for Computing Machinery (ACM)
Journal title: Companion Proceedings 8th International Conference on Learning Analytics & Knowledge (LAK18)
Abstract: False friends are words in two languages that look or sound similar but differ significantly in meaning in some or all contexts. False friends are confusing for language students and could result in frustration and communication problems. This paper proposes a method to diagnose and prevent false friends mistakes based on students’ past learned words, current location and time. The proposed method uses records from the SCROLL system (System for Capturing and Reminding Of Learning Log) to analyze the previous activity of students. We assume that the past activity of a student can be used to predict the meaning intended by the student when looking up a polysemous word. The identification of the intended meaning in the student's current context is then used to provide the student with the appropriate translation, warnings and quizzes, possibly improving the learning process and avoiding false friends future mistakes.
Description: LAK’18: 8th International Learning Analytics and Knowledge (LAK) Conference, SMC Conference & Function Centre in Sydney, Australia on March 5–9, 2018.
Rights: This work is published under the terms of the Creative Commons Attribution- Noncommercial-ShareAlike 3.0 Australia Licence. Under this Licence you are free to: Share - copy and redistribute the material in any medium or format.
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