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Online learner activeness evaluation method based on deep clustering algorithm

A clustering algorithm and learner's technology, applied in the field of data analysis, can solve problems such as statistical data representation, and achieve the effect of improving learning efficiency and learning quality

Active Publication Date: 2022-05-03
HUAZHONG NORMAL UNIV
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

These users often have difficulty conveying their importance with simple statistics

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  • Online learner activeness evaluation method based on deep clustering algorithm
  • Online learner activeness evaluation method based on deep clustering algorithm
  • Online learner activeness evaluation method based on deep clustering algorithm

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Embodiment Construction

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and implementation cases. The specific implementation cases described here are only used to explain the present invention and are not intended to limit the present invention. .

[0018] Such as figure 1 As shown, the embodiment of the present invention provides a method for evaluating the activity of online learners based on a deep clustering algorithm, including the following steps:

[0019] (1) Multi-source online learning information collection.

[0020] Such as figure 2 As shown, the information used in the present invention to evaluate the activity of online learners includes learner attributes, online behaviors and online resources. The specific collection methods and details of various types of information are as follows.

[0021] (1-1) Collection...

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Abstract

The invention relates to the field of data analysis, and provides an online learner activeness evaluation method based on a deep clustering algorithm, which comprises the following steps: acquiring multi-source information of learner attributes, online behaviors and online resources from an online platform by adopting database operation, code burying point and web crawler technologies; according to a full-view learning theory, the liveness of online learners is comprehensively evaluated from interaction behaviors between the learners and an online platform, learning contents and other learners, and liveness distribution of the learners in demographic statistics, regional affiliation and platform registration attributes is presented through a visualization method. And a new thought and a new method are provided for online platform application and service level evaluation.

Description

technical field [0001] The invention relates to the field of data analysis, and more specifically, to a method for evaluating online learner activity based on a deep clustering algorithm. Background technique [0002] With the deep integration and development of information technology and education, online education can meet the individual and customized needs of learners, and has become a normalized education and teaching mode. The online learning platform is the main carrier for online education, and it retains a large amount of real and detailed learner operation behavior data. By mining these sequence data, we can discover the potential correlation behind the learners' behavior and the collocation rules related to learning activities. By exploring the activity of learners with different backgrounds on the platform, we can effectively gain insights into learners' behavior habits and rules, optimize their online learning experience, enhance online learning investment and ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06F16/904G06F16/951G06F16/2455G06F16/242G06F16/215G06F40/30G06K9/62G06N3/04G06N3/08G06Q50/20
CPCG06Q10/0639G06F16/951G06F16/904G06F16/215G06F16/2455G06F16/2433G06F40/30G06Q50/20G06N3/04G06N3/084G06F18/23
Inventor 卢春李淼云吴砥钟正徐建
Owner HUAZHONG NORMAL UNIV
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