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Method for measuring user behaviors in online learning environment based on collective attention

A technique for learning context, attention, and applications in the field of network analysis

Pending Publication Date: 2019-09-06
BEIJING NORMAL UNIVERSITY +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

So while this openness and flexibility is often portrayed as unconstrained human attention, with the belief that vast resources and low learning costs will make education more accessible, as is frequently reported, this marketing Slogans must be challenged and validated by educational researchers

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  • Method for measuring user behaviors in online learning environment based on collective attention
  • Method for measuring user behaviors in online learning environment based on collective attention
  • Method for measuring user behaviors in online learning environment based on collective attention

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Embodiment Construction

[0037] In the following, the present invention will be described in detail with reference to the accompanying drawings and specific implementation methods of measuring user behavior in an online learning environment based on collective attention.

[0038] Based on the open flow network theory, the present invention designs a set of measurement methods for user behavior in an online learning environment, which is mainly divided into two parts, one part is to use user click stream data to construct a collective attention open flow network, and the other part is to use The method in the open flow network theory - flow distance, dissipation law and spatial embedding to analyze the user's behavior pattern, the specific operation is as follows:

[0039] Step 1) User click stream data preprocessing

[0040] Data source: The clickstream data comes from XuetangX (xuetangX), which is the first Chinese large-scale open online course platform launched by Tsinghua University in 2013. As o...

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Abstract

The invention discloses a method for measuring user behaviors in an online learning environment based on collective attention measurement, and the method takes a flow network theory as a basis, establishes a user group measurement and evaluation scheme of an online learning resource platform, and helps the platform to design a more efficient teaching system. According to the method, an open flow network based on collective attention is constructed from click flow data generated by online learning of a large number of users, and indexes such as network flow, flow distance and space embedding are respectively utilized to explore collective attention distribution rules of different student groups in online learning. According to the invention, interaction between teaching and teaching and mutual influence with collective behaviors of student groups are considered from the perspective of the system, so that objective laws in online learning data are revealed more dynamically and visually,measurement results are more objective, and the method has more practical guiding significance.

Description

technical field [0001] The present invention relates to the fields of network analysis, learning analysis and educational evaluation. It is mainly devoted to the study of the distribution of collective attention in various educational resources, helping to understand part of the learning behavior of learners, and exploring the learning patterns of different learning types, so as to measure and evaluate And design a more efficient learning resource platform. Background technique [0002] The massive rise of open online courses (MOOCs) and hybrid models of on-campus courses has changed the way educators view how knowledge is generated, disseminated, and consumed. We have entered an era where "abundance of information creates attention scarcity". Although the open and flexible nature of online learning allows us to learn at any time and any place, failure to consider the cost of attention allocation will greatly reduce our learning benefits on it. When the number of resources...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/20G06F11/34
CPCG06Q10/06393G06Q50/205G06F11/3438
Inventor 楼晓丹张江张婧婧张汉杰
Owner BEIJING NORMAL UNIVERSITY
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