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An Effective Learning Evaluation Method Based on Text Data with Real-time Attribution - A Case Study for Mathematical Class with Students of Junior Middle School in China

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Published:10 March 2023Publication History
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Abstract

In today's intelligent age, the vigorous development of education-based information analysis technology has had a profound impact on the education and teaching process. The use of computational linguistics technology to extract teaching data for learning evaluation is an important hot domain in this research field. Therefore, the study of student learning assessment methods based on text data has become a key issue. The text data extracted from the education process has attributes related to time and operational attributes, which are important indicators to measure the effect of student learning effect. However, these attributes are not focused by the traditional educational effect evaluation method, which make the learning effect of students difficult to measure comprehensively and effectively. In response to this problem, this article first uses perception technology to extract learning text data based on time and operational attributes. Secondly, according to the real-time attributes of text data, such as time and operation attributes, a learning evaluation method based on real-time text data is proposed. Finally, this article compares the traditional evaluation method with the proposed method. The results show that using real-time attribute text data is more effective in students’ learning measure.

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  1. An Effective Learning Evaluation Method Based on Text Data with Real-time Attribution - A Case Study for Mathematical Class with Students of Junior Middle School in China

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    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 3
      March 2023
      570 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3579816
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 10 March 2023
      • Online AM: 16 March 2022
      • Accepted: 29 June 2021
      • Received: 22 March 2021
      Published in tallip Volume 22, Issue 3

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