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Learning state collection and recognition method and system

A technology of learning status and learning content, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of little in-depth cognitive input, lack of, and neglect of learner input, etc., to achieve rich details, good The effect of the noise canceling effect

Inactive Publication Date: 2017-12-12
北京点易通科技有限公司
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AI Technical Summary

Problems solved by technology

[0003] At present, the collection and analysis of online learning data at home and abroad are mainly limited to shallow-level behaviors, and rarely go deep into the level of cognitive input; at the same time, the input of learners’ contribution to learning content is ignored, and data collection is mainly discussed from a technical perspective. Ways of tracking and analyzing various operational behaviors of network users, such as analyzing database access records and Web log files, studying and learning operational behaviors, lack of consideration from the perspective of learning

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  • Learning state collection and recognition method and system
  • Learning state collection and recognition method and system

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Embodiment Construction

[0046] Combine below figure 2 The specific embodiment of the present invention will be further described in detail.

[0047] Such as figure 1 As shown, in the actual application process of a learning state collection and identification method and system designed by the present invention, it specifically includes the following steps:

[0048] Step 001. The knowledge map is used as a metadata database to learn the basic data of state collection, and enter step 002;

[0049] Step 002. According to the selected courses and test questions, combined with knowledge map analysis, enter step 003;

[0050] Step 003. Carry out learning status marking, enter step 004;

[0051] Step 004. Identify various learning states and proceed to step 005;

[0052] Step 005. The adaptive engine operates online, analyzes various dimension information, and proceeds to step 006;

[0053] Step 006. Learning process behavior collection, and enter step 007;

[0054] Step 007. sort out various learnin...

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Abstract

For carrying out structural sorting on the online education courses and test resources, marking is carried out through the knowledge point-based tag system, and then based on the location and the relationship of the knowledge points in the knowledge map, structural description of the courses and test resources is formed. The behavior data of a student in browsing, retrieving and learning online courses as well as conducting various online tests is collected, filtering, cleaning, sorting, associating and recalculation are carried out according to a certain method, and an effective behavior description log of the individual student learning process is established. Based on the structural description of resources and the behavior log description of the individual student, behavioral marking, performance quantification and ability rating are carried out on the student from the perspective of knowledge points, and a preliminary ability quantification model is established.

Description

technical field [0001] The invention relates to the field of learning data collection and analysis, in particular to an online learning input data collection and analysis method. Background technique [0002] Researchers have made many attempts to collect and analyze online learning behavior data. Peng Wenhui (2012) divided learners’ online learning behavior into synchronous communication, asynchronous discussion, online questioning, accessing resources, online testing, submitting assignments, online examinations, etc. And use the learning behavior OCCP (operating behavior layer, cognitive behavior layer, collaborative behavior layer and problem-solving behavior layer) classification model proposed by him to analyze the network learning behavior (Peng Wenhui. Analysis and modeling of network learning behavior [D]. Huazhong Normal University, 2012.). Wang Lina (2011) divides online learning behavior into personalized interaction behavior between learners and learning resourc...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q50/20
CPCG06F16/367G06Q50/20
Inventor 张力超胡加明陈磊郝小汉
Owner 北京点易通科技有限公司
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