Cultural fusion type English learning resource intelligent pushing system

Abstract

The invention discloses a culture fusion type English learning resource intelligent pushing system. The system comprises a user feature analysis module, a culture resource library construction module, a culture-language association map module, a multi-modal resource library module, an intelligent pushing algorithm module and a learning feedback and adjustment module. The invention belongs to the technical field of education, and particularly provides a culture fusion type English learning resource intelligent pushing system which realizes deep fusion of language learning and culture cognition and improves the cross-culture communication ability of English learners through multi-modal data analysis, cross-culture knowledge graph construction and a personalized recommendation algorithm.

Description

Technical Field

[0001] The present invention belongs to the field of educational technology, and specifically refers to a cultural integration type English learning resource intelligent push system. Background Art

[0002] With the deepening of globalization, English, as the primary language of international communication, has become a crucial component of global education. Existing English learning resource delivery systems focus primarily on grammar and vocabulary, lacking cultural context, making it difficult for learners to understand language usage scenarios. They rely on static tags to recommend resources, ignoring learners' dynamic cultural interests and cognitive differences. They also fail to effectively integrate multimodal resources such as text, video, and audio, making it difficult to create an immersive cultural learning environment. Therefore, a new, culturally integrated, intelligent delivery system for English learning resources is urgently needed to address these challenges. Summary of the Invention

[0003] To solve the above-mentioned existing problems, the present invention provides a culturally integrated English learning resource intelligent push system that achieves a deep integration of language learning and cultural cognition through multimodal data analysis, cross-cultural knowledge graph construction and personalized recommendation algorithms, thereby improving the cross-cultural communication ability of English learners.

[0004] The technical solution adopted by the present invention is as follows: The cultural integration English learning resource intelligent push system of the present invention includes a user feature analysis module, a cultural resource library construction module, a culture-language association map module, a multimodal resource library module, an intelligent push algorithm module and a learning feedback and adjustment module.

[0005] Furthermore, the user feature analysis module collects learners' basic information, English proficiency, learning interests and other data to build learner portraits, collects behavioral data such as resource clicks and duration of stay on cultural topics, and constructs dynamic cultural interest vectors based on implicit feedback. Through deep learning algorithms, it analyzes learners' learning preferences and cognitive abilities, providing a personalized basis for subsequent resource push.

[0006] Furthermore, the cultural resource library construction module integrates rich English learning resources, including texts, videos, audios, reading materials, etc., and pays special attention to integrating cultural background knowledge of the target language country, such as history, customs, festivals, art, etc.

[0007] Furthermore, the culture-language association graph module constructs a three-layer knowledge graph: language knowledge point layer, cultural element layer, and scenario application layer, and uses a graph neural network to mine association rules between nodes.

[0008] Furthermore, the multimodal resource library module collects text, video, and audio with cultural annotations and extracts multimodal features.

[0009] Furthermore, based on the output results of the learner feature analysis module and combined with the content in the cultural resource library, the intelligent push algorithm module uses intelligent algorithms to push personalized learning resources to learners. When pushing, it takes into account the learner's current English level, learning interests, and the need for cultural background knowledge, ensuring that the pushed content not only meets the learner's ability level but also effectively supplements their cultural background knowledge.

[0010] Furthermore, the learning feedback and adjustment module collects the data generated by learners during the learning process, evaluates the learning effect of learners through data analysis. According to the evaluation results, the intelligent push algorithm module will adjust the push strategy in real time to ensure that the learning resources always match the actual needs of learners.

[0011] The beneficial effects achieved by the present invention with the above structure are as follows:

[0012] 1. Optimize the language acquisition and cultural cognition goals to avoid single-dimensional push deviation.

[0013] 2. Identify the content in the resources that may cause cultural misunderstandings for learners and trigger annotation or replacement suggestions.

[0014] 3. Recommend a combined resource package of "language points - cultural scenarios - practical tasks" based on graph association.

[0015] 4. Improve the accuracy of cross-cultural learning resource matching, reduce the risk of cross-cultural communication misunderstandings, and enhance learning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic structural diagram of the intelligent push system for culture-integrated English learning resources proposed in this solution. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] As Figure 1 shown, the culture-integrated English learning resource intelligent push system proposed in this solution includes a user feature analysis module, a cultural resource library construction module, a culture-language association graph module, a multi-modal resource library module, an intelligent push algorithm module, and a learning feedback and adjustment module.

[0019] The user feature analysis module collects learners' basic information, English proficiency, learning interests and other data to build learner portraits, collect behavioral data such as resource clicks and duration of stay on cultural topics, and builds dynamic cultural interest vectors based on implicit feedback. Through deep learning algorithms, it analyzes learners' learning preferences and cognitive abilities, providing a personalized basis for subsequent resource push.

[0020] The cultural resource library construction module integrates rich English learning resources, including texts, videos, audios, reading materials, etc., and pays special attention to integrating cultural background knowledge of the target language country, such as history, customs, festivals, art, etc.

[0021] The culture-language association graph module constructs a three-layer knowledge graph: language knowledge point layer, cultural element layer, and scenario application layer, and uses a graph neural network to mine association rules between nodes.

[0022] The multimodal resource library module collects text, video, and audio with cultural annotations and extracts multimodal features.

[0023] The intelligent push algorithm module is based on the output results of the learner feature analysis module and combined with the content in the cultural resource library. It pushes personalized learning resources to learners through an intelligent algorithm. When pushing, it takes into account the learner's current English level, learning interests and cultural background knowledge needs to ensure that the pushed content is both in line with the learner's ability level and can effectively supplement his or her cultural background knowledge.

[0024] The learning feedback and adjustment module collects data generated by learners during the learning process, evaluates the learners' learning effects through data analysis, and based on the evaluation results, the intelligent push algorithm module adjusts the push strategy in real time to ensure that learning resources always match the learners' actual needs.

[0025] In practice, the system first collects basic information about learners, such as age, gender, and English proficiency, and uses questionnaires or tests to understand their learning interests and preferences. Using deep learning algorithms, it constructs a learner profile, providing a personalized foundation for subsequent resource delivery.

[0026] The learner's interest in a certain course is 50% higher than the average, and their cross-cultural sensitivity rating is B+; they associate a certain course with a certain cultural node, locking in 3 related videos and 5 case studies; they prioritize pushing videos that include a certain course and attach interactive tasks (simulated cross-cultural negotiation recordings); based on the cultural misunderstandings in the task completion, they will subsequently recommend supplementary audio courses on "high-context vs. low-context communication."

[0027] If a learner performs poorly in a certain aspect, the system can push more learning resources in related areas; if a learner is particularly interested in a certain topic, the system can push more learning resources on related topics.

[0028] It should be noted that in this specification, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus.

[0029] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent push system for English learning resources with cultural integration, characterized in that: It includes user feature analysis module, cultural resource library construction module, culture-language association map module, multimodal resource library module, intelligent push algorithm module and learning feedback and adjustment module.

2. The intelligent push system for English learning resources with cultural integration according to claim 1, wherein: The user feature analysis module collects learners' basic information, English proficiency, learning interests and other data to build learner portraits. It also collects behavioral data such as resource clicks and the length of time spent on cultural topics. Based on implicit feedback, it constructs a dynamic cultural interest vector. Through deep learning algorithms, it analyzes learners' learning preferences and cognitive abilities, providing a personalized foundation for subsequent resource push. The cultural resource library construction module integrates a wealth of English learning resources, including texts, videos, audios, reading materials, etc., and pays special attention to incorporating cultural background knowledge of the target language country, such as history, customs, festivals, art, etc.; The culture-language association graph module constructs a three-layer knowledge graph: language knowledge point layer, cultural element layer, and scenario application layer, and uses a graph neural network to mine association rules between nodes; The multimodal resource library module collects text, video, and audio with cultural annotations and extracts multimodal features; The intelligent push algorithm module uses the output of the learner feature analysis module and the content in the cultural resource library to push personalized learning resources to learners through intelligent algorithms. When pushing, it takes into account the learner's current English level, learning interests, and cultural background knowledge needs, ensuring that the pushed content is both in line with the learner's ability level and can effectively supplement their cultural background knowledge. The learning feedback and adjustment module collects data generated by learners during the learning process, evaluates the learners' learning effects through data analysis, and based on the evaluation results, the intelligent push algorithm module adjusts the push strategy in real time to ensure that learning resources always match the learners' actual needs.

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