English learning material resource intelligent recommendation system and method

Abstract

The invention relates to the technical field of learning material recommendation, in particular to an intelligent recommendation system and method for English learning material resources, and the method comprises the following steps: extracting material features corresponding to each material from an English learning material library; classifying the plurality of material features based on a demand learning purpose and constructing a feature map; acquiring learner characteristics of the learner; freely positioning material features corresponding to the learner features based on the feature spectrum to obtain a plurality of primary material features, and combining the plurality of primary material features to obtain a material package; the learning environment information of the target learner is obtained, the material package is dynamically adjusted based on the learning environment information, the English learning materials corresponding to the target material features are recommended to the target learner, and the English learning materials corresponding to the material features more suitable for the learning features of the target learner can be determined. And the accuracy of recommending the English learning materials is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of learning material recommendation, and in particular to an intelligent recommendation system and method for English learning material resources. Background Art

[0002] In today's globalized world, English, as an international language, is increasingly sought after, encompassing a wide range of fields, including education, business, tourism, and cultural exchange. Learners' demands for English learning materials are diverse and personalized. Different learners have different requirements for the content, format, and difficulty of these materials, based on their learning objectives, knowledge level, and learning environment.

[0003] Since the same knowledge point has different levels of difficulty for different learners and also has different levels of difficulty in different learning environments, the existing method of recommending English learning materials only recommends based on the inherent difficulty of the required knowledge points, which is difficult to respond to the personalized needs of a large number of learners. As a result, the difficulty of the recommended materials does not match the actual level of the learners, affecting the accuracy and efficiency of the material recommendations.

[0004] To this end, we propose an intelligent recommendation system and method for English learning material resources to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent recommendation system and method for English learning material resources to solve the problems raised in the above background technology.

[0006] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for intelligently recommending English learning material resources, the method comprising the following steps:

[0007] Extracting material features corresponding to each material from the English learning material library, where material features include required learning objectives and presentation form features; classifying multiple material features based on required learning objectives and constructing a feature map;

[0008] Obtain learner characteristics of the learner, wherein the learner characteristics include learning objectives, learning environment information, required knowledge point characteristics, and required difficulty characteristics. The learning environment information includes learning environment characteristics and corresponding required presentation forms;

[0009] Based on the feature map, the material features corresponding to the learner's features are freely located to obtain multiple preliminary material features, and the multiple preliminary material features are combined to obtain a material package;

[0010] The learning environment information of the target learner is obtained, the material package is dynamically adjusted based on the learning environment information, the material features corresponding to the adjusted material package are used as the target material features, and the English learning materials corresponding to the target material features are recommended to the target learner.

[0011] Preferably, the step of obtaining the demand difficulty characteristics corresponding to the learner's required knowledge points includes:

[0012] Obtain the learning objectives and required knowledge point characteristics automatically input by learners;

[0013] Obtaining first difficulty information of required knowledge point features and cognitive information of target learners, wherein the cognitive information includes style adaptation information, learning behavior information, and knowledge level information;

[0014] Evaluate the learner's cognitive ability index based on cognitive information, and determine the required difficulty characteristics based on the cognitive ability index and the first difficulty information. The corresponding calculation formula is: Among them, λ represents the difficulty characteristics of learners’ demand for required knowledge points, represents the first difficulty information, μ and σ represent the average cognitive ability and standard deviation of the group, and δ represents the learner's cognitive ability index for the required knowledge points; the learning purpose, required knowledge point characteristics, and required difficulty characteristics are used as learner characteristics.

[0015] Preferably, the step of freely locating material features corresponding to learner features based on the feature map to obtain a plurality of preliminary material features, and combining the plurality of preliminary material features to obtain a material package includes:

[0016] Determine the location information of each material feature in the feature map and the corresponding feature information, wherein the feature information includes the knowledge point information and knowledge point difficulty information corresponding to each material feature;

[0017] Preliminarily marking material features corresponding to the current learning environment information of the target learner in the feature map to obtain a plurality of candidate material features, and extracting material features corresponding to the required knowledge point features of the target learner from the plurality of candidate material features to obtain a plurality of preliminary selected material features;

[0018] Based on the learner's demand difficulty characteristics, multiple preliminary material features are screened and marked again, and the multiple preliminary material features after multiple markings are extracted and combined to obtain a material package.

[0019] Preferably, the step of re-screening and marking the features of the multiple pre-selected materials based on the learner's demand difficulty features includes:

[0020] Setting a corresponding difficulty adjustment interval for each of the preliminary selected material features according to the knowledge point difficulty information of the plurality of preliminary selected material features, wherein the knowledge point difficulty information includes a plurality of knowledge point difficulty values;

[0021] Set corresponding required difficulty characteristics for multiple difficulty adjustment intervals respectively, and mark parameters within the difficulty adjustment intervals according to the required difficulty characteristics of the target learners;

[0022] A single adjustment point is set in a single difficulty adjustment interval, and the adjustment point moves linearly within the adjustment interval;

[0023] A connection relationship between the adjustment point and the required difficulty feature is established, and preliminary material features corresponding to the required difficulty feature are determined based on the connection relationship and marked again to obtain multiple marked preliminary material features.

[0024] Preferably, the step of establishing a connection relationship between the adjustment point and the demand difficulty feature includes:

[0025] A control line is set up to connect the corresponding adjustment points. The control line is composed of a fixed number of control points connected in sequence. The positional relationship between the multiple control points is fixed. The other end of the control line is connected to the required difficulty feature.

[0026] When the adjustment point moves in the difficulty adjustment range according to the required difficulty characteristics, the control line is driven to move to find the difficulty adjustment range corresponding to the difficulty value of the knowledge point where the adjustment point is located, and the preliminary material characteristics corresponding to the difficulty adjustment range are obtained and marked again.

[0027] Preferably, the step of obtaining the target learner's learning environment information and dynamically adjusting the material package based on the learning environment information includes:

[0028] Obtain the learning environment characteristics of the target learners and formulate change rule information for each learning environment characteristic;

[0029] Determining the changing learning environment characteristics and the demand presentation forms and demand difficulty characteristics corresponding to the changing learning environment characteristics based on the change rule information;

[0030] Establish a mapping relationship between changes in learning environment characteristics and demand presentation forms and demand difficulty characteristics, and determine the location information of material characteristics corresponding to demand presentation forms and demand difficulty characteristics based on the mapping relationship;

[0031] Determine the location information of the preliminary material features in the material package before the learning environment features that have changed and replace them with material features that meet the required presentation form and required difficulty characteristics.

[0032] Preferably, the steps of obtaining learning environment characteristics of the target learner, formulating change rule information for each learning environment characteristic, and determining the learning environment characteristics that have changed based on the change rule information include:

[0033] Obtaining the learning environment characteristics of the target learner, where the learning environment characteristics include learning time, lighting information, noise information, and stability information;

[0034] Formulate change rule information for each learning environment feature, wherein the change rule information includes a change threshold and a change judgment rule;

[0035] Collect the learning environment characteristics of target learners in real time;

[0036] Compare the learning environment characteristics acquired in real time with the learning environment characteristics recorded last time according to the change judgment rules;

[0037] If the change value of any feature exceeds the corresponding change threshold, the feature is determined to be a learning environment feature with a change.

[0038] An intelligent recommendation system for English learning resources, applied to any of the above-mentioned intelligent recommendation methods for English learning resources, comprises:

[0039] The material feature collection module is used to extract the material features corresponding to each material from the English learning material library, where the material features include the required learning purpose and the presentation form features; based on the required learning purpose, multiple material features are classified and a feature map is constructed;

[0040] A learner characteristics acquisition module is used to obtain learner characteristics of learners, wherein learner characteristics include learning objectives, learning environment information, required knowledge point characteristics, and required difficulty characteristics. wherein learning environment information includes learning environment characteristics and corresponding required presentation forms;

[0041] A feature positioning module is used to freely locate the material features corresponding to the learner's features based on the feature map to obtain multiple preliminary material features, and combine the multiple preliminary material features to obtain a material package;

[0042] The material recommendation module is used to obtain the learning environment information of the target learner, dynamically adjust the material package based on the learning environment information, use the material features corresponding to the adjusted material package as the target material features, and recommend English learning materials corresponding to the target material features to the target learner.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] According to the learners' cognitive abilities and learning environments, the difficulty values ​​of the knowledge points in the English learning materials relative to the learners are dynamically adjusted, the material features corresponding to the knowledge points applicable to the learners are determined, and multiple material features are freely combined according to different learners. The material features are dynamically adjusted according to the difficulty values ​​of the same knowledge point for the same learner in different learning environments, so as to determine the English learning materials corresponding to the material features that are more suitable for the learning characteristics of the target learners, improve the accuracy of the recommended English learning materials, and their adaptability to the target learners, thereby improving the learning efficiency of the target learners. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 Schematic diagram of the method flow of the present invention;

[0047] Figure 2 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] Example

[0050] See also Figures 1 to 2 The present invention provides a technical solution for an intelligent recommendation system and method for English learning material resources: an intelligent recommendation method for English learning material resources, comprising the following steps:

[0051] S1: Extract the material features corresponding to each material from the English learning material library, where the material features include the required learning purpose and the presentation form features; classify multiple material features based on the required learning purpose and construct a feature map;

[0052] S2: Obtain learner characteristics of the learner, wherein the learner characteristics include learning objectives, learning environment information, required knowledge point characteristics, and required difficulty characteristics. The learning environment information includes learning environment characteristics and corresponding required presentation forms;

[0053] The step of obtaining the required difficulty characteristics corresponding to the learner's required knowledge points includes: obtaining the learning objectives and required knowledge point characteristics automatically input by the learner; obtaining first difficulty information of the required knowledge point characteristics and cognitive information of the target learner, wherein the cognitive information includes style adaptation information, learning behavior information, and knowledge level information; and evaluating the learner's cognitive ability index based on the cognitive information, and the corresponding evaluation formula is: Among them, RZ represents the learner's knowledge level information, RX represents the learner's learning behavior information, and RF represents the learner's style adaptation information. They represent the weight coefficients corresponding to the learner's knowledge level information, learning behavior information, and style adaptability information, respectively. δ represents the learner's cognitive ability index for the required knowledge points. The required difficulty characteristics are determined based on the cognitive ability index and the first difficulty information. The corresponding calculation formula is: Among them, λ represents the difficulty characteristics of learners’ demand for required knowledge points, represents the first difficulty information, μ and σ represent the group's average cognitive ability and standard deviation, which are used for standardization adjustment; learning objectives, required knowledge point characteristics, and required difficulty characteristics are used as learner characteristics;

[0054] Specifically, a short test (e.g., 5 multiple-choice questions) is conducted on the required knowledge points to assess the initial mastery level and the learner's self-assessed familiarity with the relevant knowledge points (which can be expressed as a score). Based on the initial mastery level and self-assessed familiarity, the learner's knowledge level information on the required knowledge point characteristics is determined. The learning output per unit time is determined based on the learner's historical average learning time, number of repeated video viewings, and rate of redoing wrong questions, thereby determining the learning behavior data. The cognitive ability index is comprehensively evaluated based on cognitive information. If the cognitive ability index is high, the required difficulty characteristics corresponding to the required knowledge point characteristics are adjusted upward, otherwise they are adjusted downward. Based on the different required difficulty characteristics, material characteristics of different difficulty levels are selected, and English learning materials suitable for the learner's learning difficulty are selected for recommendation.

[0055] Specifically, the demand difficulty feature includes the recognized difficulty of the required knowledge point input by the learner, which is also the first difficulty information (which can be determined by the average of the difficulty evaluations of multiple learners on the knowledge point). It also includes the influence of the learner's own cognitive ability on the difficulty (for example, some people with strong learning ability may find the knowledge point easy, while the target learner belongs to the weaker series and may find the knowledge point difficult). Based on the learner's cognitive ability and the first recognized difficulty, a comprehensive assessment is made to obtain the demand difficulty feature, thereby improving the personalization of recommended materials.

[0056] S3: Based on the feature map, freely locate the material features corresponding to the learner's features to obtain multiple preliminary material features, and combine the multiple preliminary material features to obtain a material package;

[0057] The steps of freely locating material features corresponding to learner features based on the feature map to obtain multiple preliminary material features, and combining the multiple preliminary material features to obtain a material package include: determining the position information of each material feature in the feature map and the corresponding feature information, wherein the feature information includes the knowledge point information and knowledge point difficulty information corresponding to each material feature; preliminarily marking the material features corresponding to the current learning environment information of the target learner in the feature map to obtain multiple candidate material features, extracting material features corresponding to the target learner's required knowledge point features from the multiple candidate material features to obtain multiple preliminary material features; re-screening and marking the multiple preliminary material features based on the learner's required difficulty features, extracting the multiple marked preliminary material features and combining them to obtain a material package;

[0058] It should be noted that, based on the current learning environment information, the learner's current demand for the presentation form of material features can be determined, and the demand presentation form that meets the current learning environment information can be screened out, thereby preliminarily determining the corresponding material features. For example, in a noisy environment, a text series can be considered, and in a quiet environment, an audio series can be considered. When considering the demand presentation form, it is necessary to comprehensively consider the learner's learning environment characteristics. Learning environment characteristics include learning time, lighting information, noise information, and stability. Based on the learning environment information, the material features suitable for the current learning environment are preliminarily selected, and the material features are selected according to the presentation form to make it better fit the current environment. The material package can be adjusted according to changes in the environment. When changes in the learner's learning environment are detected, English learning materials that meet the current learning environment are automatically recommended, which can improve the learner's learning efficiency.

[0059] The step of re-screening and marking multiple preliminary material features based on the learner's demand difficulty characteristics includes: setting a corresponding difficulty adjustment interval for each preliminary material feature according to the knowledge point difficulty information of the multiple preliminary material features, wherein the knowledge point difficulty information includes multiple knowledge point difficulty values; setting corresponding demand difficulty characteristics for the multiple difficulty adjustment intervals, and marking parameters within the difficulty adjustment intervals according to the demand difficulty characteristics of the target learner; setting a single adjustment point in a single difficulty adjustment interval, and the adjustment point moving linearly within the adjustment interval; establishing a connection relationship between the adjustment point and the demand difficulty characteristic, and determining the preliminary material feature corresponding to the demand difficulty characteristic according to the connection relationship, and re-marking it to obtain multiple marked preliminary material features;

[0060] The step of establishing a connection relationship between the adjustment point and the required difficulty characteristic includes: setting a control line corresponding to the adjustment point, wherein the control line is composed of a fixed number of control points connected in sequence, and the positional relationship between the multiple control points is fixed, and connecting the other end of the control line to the required difficulty characteristic; when the adjustment point moves within the difficulty adjustment range according to the required difficulty characteristic, the control line is driven to move to find the difficulty adjustment range corresponding to the difficulty value of the knowledge point where the adjustment point is located, and the preliminary material characteristics corresponding to the difficulty adjustment range are obtained and marked again;

[0061] Adjust the adjustment point to the knowledge point difficulty value corresponding to the target learner's required difficulty characteristics according to the connection relationship; re-mark the preliminary material characteristics corresponding to the adjustment interval where the knowledge point difficulty value that meets the preset conditions is located;

[0062] Each preliminary material feature has a corresponding difficulty adjustment interval. Multiple difficulty adjustment intervals are data-bearing areas that can record marked parameters and specific values ​​in the data-bearing area. At the same time, they can satisfy the movement of the adjustment point in the data-bearing area. The adjustment point is a movable port that can move in the data-bearing area and is connected to the preliminary material feature via a control line, enabling rapid adjustment. The control line is fixed by the control point, so that the preliminary material feature can be quickly adjusted and controlled, improving the adaptability of the preliminary material feature to the required difficulty feature.

[0063] It should be noted that the difficulty value of a knowledge point that meets the preset conditions means that the difficulty value of the knowledge point corresponds to the difficulty value of the knowledge point that meets the target learner's demand difficulty characteristics; setting a corresponding difficulty adjustment interval for each preliminary material feature based on the knowledge point difficulty information of multiple preliminary material features means that the knowledge point difficulty information of a material feature corresponds to a difficulty adjustment interval, and a difficulty adjustment interval corresponds to an adjustment point. When the adjustment point exceeds the difficulty adjustment interval, the current material feature needs to be replaced, and other material features that meet the knowledge point difficulty information corresponding to the adjustment point are screened and marked. When the adjustment point does not exceed the adjustment interval, the current material feature is directly screened and marked. Multiple adjustment intervals are respectively set with corresponding learner cognitive abilities, and the adjustment intervals are parameter-marked according to the learner's cognitive ability. Adjustment points are set in the adjustment intervals, and the adjustment points move in the adjustment intervals to establish a mapping relationship between the adjustment points and the material features (dynamically adjusting the difficulty value of the same knowledge point for different learners, and then finding the difficulty value corresponding to the learner's demand difficulty characteristics and marking it).

[0064] Specifically, there are many English learning materials, and each English learning material can extract corresponding material features. The cognitive ability of each learner is also different. For the same learning purpose and unified required knowledge points, different learners need different English learning materials. Therefore, in order to make adaptive adjustments to the learners' cognitive abilities, it is necessary to recommend corresponding English learning materials according to the corresponding cognitive abilities, extract the material features of the English learning materials and the knowledge point difficulty information of each material feature. The knowledge point difficulty information refers to the difficulty value of the knowledge point contained in the English learning material corresponding to the material feature. Since the cognitive abilities of different learners are different, multiple knowledge point difficulty values ​​are configured for the knowledge points corresponding to the material features to generate knowledge point difficulty information. After the knowledge point difficulty information is arranged in descending or ascending order, the knowledge point adjustment range corresponding to the material feature is formed, and the adjustment point is determined according to the cognitive abilities of different learners. The position of the adjustment point in the adjustment interval is determined to determine the difficulty value of the knowledge point corresponding to the cognitive ability, and the difficulty value of the knowledge point that meets the demand difficulty feature is selected from the difficulty values ​​of the knowledge point corresponding to multiple material features. The demand difficulty feature is comprehensively calculated based on the recognized average difficulty value of the knowledge point by the group and the difficulty value of the knowledge point for the target learner under the cognitive ability of the learner. The difficulty value of the knowledge point with the load demand difficulty feature is used as the knowledge point difficulty value corresponding to the target learner, and then the material feature corresponding to the knowledge point difficulty value is determined, and the material feature is marked, and the marked material features are combined to form a material package; the position of the adjustment point in the adjustment interval is dynamically adjusted according to the cognitive ability of the learner, so as to dynamically adjust the difficulty of each knowledge point relative to different learners, thereby matching English learning materials corresponding to different material features to each learner, so that it is more in line with the learner's cognitive ability and improves the learner's learning efficiency.

[0065] S4: Acquire the learning environment information of the target learner, dynamically adjust the material package based on the learning environment information, use the material features corresponding to the adjusted material package as the target material features, and recommend English learning materials corresponding to the target material features to the target learner.

[0066] The steps of obtaining the learning environment information of the target learner and dynamically adjusting the material package based on the learning environment information include: obtaining the learning environment characteristics of the target learner and formulating change rule information for each learning environment characteristic; determining the learning environment characteristics that have changed and the demand presentation form and demand difficulty characteristics corresponding to the learning environment characteristics that have changed based on the change rule information; establishing a mapping relationship between the learning environment characteristic changes and the demand presentation form and demand difficulty characteristics, and determining the position information of the material characteristics corresponding to the demand presentation form and demand difficulty characteristics based on the mapping relationship; determining the position information of the preliminary selected material characteristics of the learning environment characteristics that have changed in the material package before the change and replacing them with material characteristics that meet the demand presentation form and demand difficulty characteristics;

[0067] Establishing a connection between adjustment points and demand difficulty characteristics, as well as a mapping relationship between changes in learning environment characteristics and demand presentation forms and demand difficulty characteristics, can dynamically determine the difficulty value of the knowledge point for the learner according to the learner's own situation in the difficulty adjustment range through the adjustment point, and select corresponding material characteristics based on the actual difficulty value of the knowledge point for the learner, thereby determining the primary material package; since changes in learning environment characteristics will lead to changes in demand difficulty characteristics, therefore, according to the changes in learning environment characteristics, adjust the material characteristics of the knowledge point difficulty or material presentation form that needs to be adjusted, and reselect material knowledge points and material presentation forms that meet the current learning environment characteristics, so that the recommended English learning materials can better adapt to the learner's own learning situation and the learner's current learning environment, thereby improving the learner's learning efficiency;

[0068] The steps of obtaining learning environment characteristics of a target learner, formulating change rule information for each learning environment characteristic, and determining a changed learning environment characteristic based on the change rule information include: obtaining the learning environment characteristics of the target learner, wherein the learning environment characteristics include learning time, lighting information, noise information, and stability information; formulating change rule information for each learning environment characteristic, wherein the change rule information includes a change threshold and a change judgment rule; collecting the learning environment characteristics of the target learner in real time; comparing the learning environment characteristics obtained in real time with the learning environment characteristics recorded last time according to the change judgment rule; and if a change value of any characteristic exceeds a corresponding change threshold, determining that characteristic as a changed learning environment characteristic;

[0069] It should be noted that the steps of determining the position information of the preliminary material features in the material package before the changed learning environment features and replacing them with material features that meet the required presentation form and required difficulty features include: retrieving materials that meet the required presentation form and required difficulty features from the material library; removing multiple preliminary material features corresponding to the changed learning environment features in the material package; adding the retrieved materials to the location of the preliminary material features; updating the relevant information of the material package, and recording the detailed information of the material replacement, the detailed information including the replacement time, the reason for the replacement, and the material features before and after the replacement.

[0070] Specifically, according to the changes in the characteristics of the learning environment, the characteristics of multiple preliminary materials in the material package are dynamically adjusted to determine the final material package, and the English learning materials corresponding to the various material characteristics in the final material package are used as recommended materials; the demand presentation form refers to the demand presentation form for the material in the environment, and the presentation form refers to the media type of the material, such as text, audio, video, etc. The corresponding media type is dynamically selected according to the changes in the learner's environment, so as to modify the material package and make personalized recommendations for different learners in different learning environments, so that it is more suitable for learners in the current scenario, improves the accuracy of the recommendation, and thus improves the accuracy and practicality of the recommendation of English learning materials.

[0071] An intelligent recommendation system for English learning resources, applied to any of the above-mentioned intelligent recommendation methods for English learning resources, comprises:

[0072] The material feature collection module is used to extract the material features corresponding to each material from the English learning material library, where the material features include the required learning purpose and the presentation form features; based on the required learning purpose, multiple material features are classified and a feature map is constructed;

[0073] A learner characteristics acquisition module is used to obtain learner characteristics of learners, wherein learner characteristics include learning objectives, learning environment information, required knowledge point characteristics, and required difficulty characteristics. wherein learning environment information includes learning environment characteristics and corresponding required presentation forms;

[0074] A feature positioning module is used to freely locate the material features corresponding to the learner's features based on the feature map to obtain multiple preliminary material features, and combine the multiple preliminary material features to obtain a material package;

[0075] The material recommendation module is used to obtain the learning environment information of the target learner, dynamically adjust the material package based on the learning environment information, use the material features corresponding to the adjusted material package as the target material features, and recommend English learning materials corresponding to the target material features to the target learner.

[0076] Dynamically adjust the difficulty value of knowledge points relative to learners according to their cognitive abilities, determine the material features corresponding to the knowledge points that are suitable for learners, and freely and dynamically combine multiple material features according to different learners to determine the English learning materials corresponding to the material features that are more suitable for the learning characteristics of target learners, thereby improving the accuracy of recommended English learning materials and their adaptability to target learners, thereby improving the learning efficiency of target learners.

[0077] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0078] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligently recommending English learning material resources, characterized in that: The following steps are involved: Extracting material features corresponding to each material from the English learning material library, where material features include required learning objectives and presentation form features; classifying multiple material features based on required learning objectives and constructing a feature map; Obtain learner characteristics of the learner, wherein the learner characteristics include learning objectives, learning environment information, required knowledge point characteristics, and required difficulty characteristics. The learning environment information includes learning environment characteristics and corresponding required presentation forms; Based on the feature map, the material features corresponding to the learner's features are freely located to obtain multiple preliminary material features, and the multiple preliminary material features are combined to obtain a material package; The learning environment information of the target learner is obtained, the material package is dynamically adjusted based on the learning environment information, the material features corresponding to the adjusted material package are used as the target material features, and the English learning materials corresponding to the target material features are recommended to the target learner.

2. The method for intelligently recommending English learning resources according to claim 1, wherein: The step of obtaining the demand difficulty characteristics corresponding to the learner's required knowledge points includes: Obtain the learning objectives and required knowledge point characteristics automatically input by learners; Obtaining first difficulty information of required knowledge point features and cognitive information of target learners, wherein the cognitive information includes style adaptation information, learning behavior information, and knowledge level information; Evaluate the learner's cognitive ability index based on cognitive information, and determine the required difficulty characteristics based on the cognitive ability index and the first difficulty information. The corresponding calculation formula is: Among them, λ represents the difficulty characteristics of learners’ demand for required knowledge points, represents the first difficulty information, μ and σ represent the average cognitive ability and standard deviation of the group, and δ represents the learner's cognitive ability index for the required knowledge points; the learning purpose, required knowledge point characteristics, and required difficulty characteristics are used as learner characteristics.

3. The method for intelligently recommending English learning resources according to claim 1, wherein: The steps of freely locating material features corresponding to learner features based on the feature map to obtain a plurality of preliminary material features, and combining the plurality of preliminary material features to obtain a material package include: Determine the location information of each material feature in the feature map and the corresponding feature information, wherein the feature information includes the knowledge point information and knowledge point difficulty information corresponding to each material feature; Preliminarily marking material features corresponding to the current learning environment information of the target learner in the feature map to obtain a plurality of candidate material features, and extracting material features corresponding to the required knowledge point features of the target learner from the plurality of candidate material features to obtain a plurality of preliminary selected material features; Based on the learner's demand difficulty characteristics, multiple preliminary material features are screened and marked again, and the multiple preliminary material features after multiple markings are extracted and combined to obtain a material package.

4. The method for intelligently recommending English learning resources according to claim 3, wherein: The step of re-screening and marking the features of the multiple pre-selected materials based on the learner's demand difficulty features includes: Setting a corresponding difficulty adjustment interval for each of the preliminary selected material features according to the knowledge point difficulty information of the plurality of preliminary selected material features, wherein the knowledge point difficulty information includes a plurality of knowledge point difficulty values; Set corresponding required difficulty characteristics for multiple difficulty adjustment intervals respectively, and mark parameters within the difficulty adjustment intervals according to the required difficulty characteristics of the target learners; A single adjustment point is set in a single difficulty adjustment interval, and the adjustment point moves linearly within the adjustment interval; A connection relationship between the adjustment point and the required difficulty feature is established, and preliminary material features corresponding to the required difficulty feature are determined based on the connection relationship and marked again to obtain multiple marked preliminary material features.

5. The method for intelligently recommending English learning resources according to claim 4, wherein: The step of establishing a connection relationship between the adjustment point and the demand difficulty feature includes: A control line is set up to connect the corresponding adjustment points. The control line is composed of a fixed number of control points connected in sequence. The positional relationship between the multiple control points is fixed. The other end of the control line is connected to the required difficulty feature. When the adjustment point moves in the difficulty adjustment range according to the required difficulty characteristics, the control line is driven to move to find the difficulty adjustment range corresponding to the difficulty value of the knowledge point where the adjustment point is located, and the preliminary material characteristics corresponding to the difficulty adjustment range are obtained and marked again.

6. The method for intelligently recommending English learning resources according to claim 1, wherein: The steps of obtaining the learning environment information of the target learner and dynamically adjusting the material package based on the learning environment information include: Obtain the learning environment characteristics of the target learners and formulate change rule information for each learning environment characteristic; Determining the changing learning environment characteristics and the demand presentation forms and demand difficulty characteristics corresponding to the changing learning environment characteristics based on the change rule information; Establish a mapping relationship between changes in learning environment characteristics and demand presentation forms and demand difficulty characteristics, and determine the location information of material characteristics corresponding to demand presentation forms and demand difficulty characteristics based on the mapping relationship; Determine the location information of the preliminary material features in the material package before the learning environment features that have changed and replace them with material features that meet the required presentation form and required difficulty characteristics.

7. The method for intelligently recommending English learning resources according to claim 6, wherein: The steps of obtaining learning environment characteristics of the target learner, formulating change rule information for each learning environment characteristic, and determining the learning environment characteristics that have changed based on the change rule information include: Obtaining the learning environment characteristics of the target learner, where the learning environment characteristics include learning time, lighting information, noise information, and stability information; Formulate change rule information for each learning environment feature, wherein the change rule information includes a change threshold and a change judgment rule; Collect the learning environment characteristics of target learners in real time; Compare the learning environment characteristics acquired in real time with the learning environment characteristics recorded last time according to the change judgment rules; If the change value of any feature exceeds the corresponding change threshold, the feature is determined to be a learning environment feature with a change.

8. An intelligent recommendation system for English learning resources, applied to the intelligent recommendation method for English learning resources according to any one of claims 1 to 7, characterized in that: include: The material feature collection module is used to extract the material features corresponding to each material from the English learning material library, where the material features include the required learning purpose and the presentation form features; based on the required learning purpose, multiple material features are classified and a feature map is constructed; A learner characteristics acquisition module is used to obtain learner characteristics of learners, wherein learner characteristics include learning objectives, learning environment information, required knowledge point characteristics, and required difficulty characteristics. wherein learning environment information includes learning environment characteristics and corresponding required presentation forms; A feature positioning module is used to freely locate the material features corresponding to the learner's features based on the feature map to obtain multiple preliminary material features, and combine the multiple preliminary material features to obtain a material package; The material recommendation module is used to obtain the learning environment information of the target learner, dynamically adjust the material package based on the learning environment information, use the material features corresponding to the adjusted material package as the target material features, and recommend English learning materials corresponding to the target material features to the target learner.

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