Digital literacy improvement method and system for college students

Abstract

The invention relates to the technical field of education, and discloses a digital literacy improvement method and system for college students, and the system comprises a test question generation and grading module which is configured to generate different types of digital level test exercises, collect the answer information data of the students in each type of digital level test exercises, and send the answer information data to a database; determining an initial accomplishment level of the student based on the answer information data; the learning module is configured to determine a learning path for learning according to the initial accomplishment level and the knowledge graph; the processor is also configured to collect learning behavior data of the student in the current learning path and calculate the content complexity of the current learning path according to the learning behavior data; an analysis processing module; and the storage module is configured to store the learning path after the adjustment scheme is determined. According to the invention, through an intelligent, personalized and dynamic mode, the digital literacy level of students is effectively improved, and powerful support is provided for comprehensive development of college students.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and in particular to a method and system for improving the digital literacy of college students. Background Art

[0002] Digital literacy, as a series of qualities and abilities that are indispensable for individuals in the digital age, covers multiple dimensions from digital acquisition, production, use, and evaluation to interaction, sharing, innovation, security, and ethics. Specifically, its components include: Digital survival ability: This refers to the ability of individuals to skillfully use various apps for shopping, travel, social interaction, health management (such as seeing a doctor, epidemic prevention), and other activities in daily life scenarios; at the same time, they must have the ability to effectively browse, retrieve, and query the required information, as well as the ability to properly manage digital assets such as personal photos and videos; Digital security capabilities: This part emphasizes the strict protection of personal data and privacy; it requires individuals to have the ability to identify and prevent illegal activities such as online rumors, telecommunications fraud, and information theft; in addition, it is necessary to cultivate self-control over games, short videos, and other content to prevent the occurrence of addiction; Digital thinking ability: This ability encourages individuals The individual should use digital technology to optimize life experience and work efficiency, such as through online office, digital channel marketing, telemedicine, etc.; at the same time, he should have data thinking and be able to use data analysis to discover the root causes of problems, make accurate judgments or predict future trends; digital production capability: refers to the ability of individuals to create and output digital products, content or solutions to solve practical problems, thereby enhancing the brand influence and competitiveness of individuals or enterprises in the digital world; digital innovation capability: in the field of digital economy, individuals with digital literacy should show the potential to lead the trend, be able to put forward unique insights or show unique innovation advantages in basic technology, open platforms, business models, etc.

[0003] In summary, digital literacy plays a pivotal role in modern society. It not only helps individuals quickly adapt to the digital environment, but also significantly improves their learning and work efficiency and promotes their active participation in the digital society. Having digital literacy means that individuals can use digital technology and resources in a reasonable and responsible manner, actively protect personal privacy, follow network ethics, and resolutely resist bad behaviors such as cyber violence.

[0004] However, the existing digital literacy training system for college students is still imperfect and has many shortcomings. On the one hand, some colleges and universities do not pay enough attention to digital literacy education, and the curriculum and teaching resources are relatively scarce; on the other hand, when faced with complex digital information, students often lack effective screening, judgment and application capabilities, and are prone to information overload, privacy leakage, and Internet addiction. Therefore, how to effectively improve the digital literacy of college students has become an important issue that needs to be solved in the current education field. Summary of the invention

[0005] In view of this, the present invention proposes a method and system for improving the digital literacy of college students, aiming to effectively improve the digital literacy of college students.

[0006] In one aspect, the present invention proposes a digital literacy improvement system for college students, comprising:

[0007] The test question generation and grading module is configured to generate different types of digital proficiency test questions, collect students' answer information data for each type of digital proficiency test questions, and determine the students' initial literacy level based on the answer information data;

[0008] A learning module is configured to determine a learning path for learning according to the initial literacy level and the knowledge graph; and is also configured to collect learning behavior data of the student in the current learning path, and calculate the content complexity of the current learning path according to the learning behavior data;

[0009] The analysis and processing module is configured to compare the content complexity with the complexity threshold, and determine whether the current learning path needs to be adjusted according to the comparison result:

[0010] When it is determined that the current learning path needs to be adjusted, the similarity between the current learning path and the historical learning path is obtained according to the content complexity of the current learning path, and the adjustment plan of the current learning path is determined according to the similarity;

[0011] The storage module is configured to store the learning path after the adjustment plan is determined.

[0012] Furthermore, the digital proficiency test exercises include digital survival ability test exercises, digital safety ability test exercises, digital learning ability test exercises, digital innovation ability test exercises and digital collaboration ability test exercises.

[0013] Furthermore, collecting the student's answer information data for each type of digital proficiency test questions and determining the student's initial literacy level based on the answer information data includes:

[0014] Assign points to each type of digital proficiency test questions;

[0015] Collecting the answer information data of the student, wherein the answer information data includes answer time, answer times and answer accuracy rate;

[0016] The answer score is determined based on the answer time, the number of answers and the answer accuracy, and the answer score is obtained by the following formula:

[0017]

[0018] Among them, Z represents the answer score, Wti represents the weight coefficient of the correct answer rate of the i-th type of digital proficiency test exercises, CRi represents the correct answer rate of the i-th type of digital proficiency test exercises, Wci represents the weight coefficient of the number of answers to the i-th type of digital proficiency test exercises, ATi represents the number of answers to the i-th type of digital proficiency test exercises, Wtimei represents the weight coefficient of the answer time of the i-th type of digital proficiency test exercises, Tmax,i represents the standard answer time of the i-th type of digital proficiency test exercises, and Ti represents the actual answer time of the i-th type of digital proficiency test exercises.

[0019] Furthermore, when collecting the student's answer information data for each type of digital proficiency test questions and determining the student's initial literacy level based on the answer information data, the method further includes:

[0020] Comparing the answer score with a first answer score threshold, a second answer score threshold, and a third answer score threshold, and determining the initial literacy level according to the comparison result; wherein the first answer score threshold is less than the second answer score threshold, and the second answer score threshold is less than the third answer score threshold;

[0021] When the answer score is less than or equal to the first answer score threshold, determining the initial literacy level to be the first level;

[0022] When the answer score is greater than the first answer score threshold and less than or equal to the second answer score threshold, determining the initial literacy level to be the second level;

[0023] When the answer score is greater than the second answer score threshold and less than or equal to the third answer score threshold, determining the initial literacy level to be the third level;

[0024] When the answer score is greater than the third answer score threshold, the initial literacy level is determined to be the fourth level.

[0025] Further, when determining a learning path for learning according to the initial literacy level and the knowledge graph, it includes:

[0026] Collect digital literacy knowledge data and construct a knowledge graph; the knowledge graph includes a plurality of nodes and edges connecting the nodes, wherein each node represents a digital literacy knowledge point, and each edge represents an association relationship between two knowledge points;

[0027] According to the initial literacy level, selecting a digital literacy knowledge point matching the initial literacy level from the knowledge graph as the starting point of the learning path;

[0028] According to the starting point of the learning path, traverse along the edge of the knowledge graph, select a node associated with the current node as the next learning node, until all the digital literacy knowledge points matching the initial literacy level are traversed to form the learning path;

[0029] The learning is performed according to the sequence of the learning path, and the learning behavior data is recorded during the learning process.

[0030] Furthermore, when calculating the content complexity of the current learning path according to the learning behavior data, it includes:

[0031] The learning behavior data includes learning time, learning frequency, learning accuracy and learning difficulty evaluation value;

[0032] The content complexity is obtained by the following formula:

[0033]

[0034] Among them, C represents content complexity, T represents learning time, S represents standard learning time, F represents learning frequency, N represents standard learning frequency, R represents learning accuracy, D represents learning difficulty evaluation value, H represents standard learning difficulty evaluation value, ω1, ω2, ω3 and ω4 are weight coefficients.

[0035] Furthermore, the content complexity is compared with the complexity threshold, and whether the current learning path needs to be adjusted is determined according to the comparison result, including:

[0036] When the content complexity is less than or equal to the complexity threshold, determining not to adjust the current learning path;

[0037] When the content complexity is greater than the complexity threshold, it is determined to adjust the current learning path.

[0038] Further, when determining to adjust the current learning path, it includes:

[0039] The similarity between the current learning path and the historical learning path is obtained according to the content complexity of the current learning path. The similarity is obtained by the following formula:

[0040]

[0041] Among them, S represents similarity, Cc represents the content complexity of the current learning path, and Ch represents the content complexity of the historical learning path.

[0042] Further, when determining an adjustment scheme of the current learning path according to the similarity, it includes:

[0043] The similarity is compared with a similarity threshold, and an adjustment plan for the current learning path is determined according to the comparison result:

[0044] When the similarity is greater than or equal to the similarity threshold, selecting and adding a knowledge point that is related to the current learning path but has not been learned as a new learning node;

[0045] When the similarity is less than a similarity threshold, the initial literacy level of the student is re-evaluated, and the starting point of the learning path is re-selected from the knowledge graph based on the new initial literacy level to construct a new learning path.

[0046] Compared with the prior art, the beneficial effect of the present invention is that the digital literacy improvement system for college students provided by the present invention first accurately evaluates the initial literacy level of students through the test question generation and grade division module, and provides a strong basis for the subsequent customization of learning paths. Subsequently, the learning module tailors the learning path for the students according to their initial literacy level and knowledge map, and collects the students' learning behavior data in real time during the learning process to calculate the content complexity of the current learning path. This process fully reflects the intelligent and personalized characteristics of the system. When the system detects that the content complexity of the current learning path may exceed the students' understanding ability, the analysis and processing module will intervene, and intelligently determine whether the learning path needs to be adjusted by calculating the similarity between the current learning path and the historical learning path. If adjustment is required, the system will also formulate a scientific and reasonable adjustment plan based on the specific situation of the students. This dynamic adjustment capability makes the students' learning process smoother and easier to achieve the expected learning effect. Finally, the storage module will store the learning path after determining the adjustment plan, providing reference and reference for the subsequent learning process. This not only helps to improve students' learning efficiency, but also helps the system to continuously optimize its own algorithms and models, and provide students with more accurate and personalized learning services.

[0047] The digital literacy improvement system for college students provided by the present invention effectively improves the digital literacy level of students in an intelligent, personalized and dynamic manner, and provides strong support for the all-round development of college students.

[0048] In another aspect, the present invention also proposes a method for improving digital literacy of college students, comprising the following steps:

[0049] S100: generating different types of digital proficiency test exercises, collecting students' answer information data for each type of digital proficiency test exercises, and determining the students' initial literacy level based on the answer information data;

[0050] S200: determining a learning path for learning according to the initial literacy level and the knowledge graph; collecting learning behavior data of the student in the current learning path, and calculating the content complexity of the current learning path according to the learning behavior data;

[0051] S300: Compare the content complexity with the complexity threshold, and determine whether the current learning path needs to be adjusted according to the comparison result:

[0052] S400: when it is determined that the current learning path needs to be adjusted, obtaining the similarity between the current learning path and the historical learning path according to the content complexity of the current learning path, and determining the adjustment plan of the current learning path according to the similarity;

[0053] S500: Storing the learning path after the adjustment plan is determined.

[0054] It is understandable that the above-mentioned method and system for improving digital literacy for college students have the same beneficial effects and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0056] Figure 1 A structural block diagram of a digital literacy improvement system for college students provided by an embodiment of the present invention;

[0057] Figure 2 A flowchart of a method for improving digital literacy for college students provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0059] See also Figure 1 As shown, in some embodiments of the present application, this embodiment provides a digital literacy improvement system for college students, including:

[0060] The test question generation and grading module is configured to generate different types of digital proficiency test questions, collect students' answer information data for each type of digital proficiency test questions, and determine the students' initial literacy level based on the answer information data;

[0061] In this embodiment, the digital level test exercises are divided into different types because the digital literacy level of students can be comprehensively evaluated from multiple dimensions.

[0062] A learning module is configured to determine a learning path for learning according to the initial literacy level and the knowledge graph; and is also configured to collect learning behavior data of the student in the current learning path, and calculate the content complexity of the current learning path according to the learning behavior data;

[0063] In this embodiment, the knowledge graph is a structure that represents the relationship between different knowledge points and is used to guide the determination of learning paths. It can help the system understand students' learning needs and customize appropriate learning content for them based on their initial literacy levels.

[0064] The analysis and processing module is configured to compare the content complexity with the complexity threshold, and determine whether the current learning path needs to be adjusted according to the comparison result:

[0065] When it is determined that the current learning path needs to be adjusted, the similarity between the current learning path and the historical learning path is obtained according to the content complexity of the current learning path, and the adjustment plan of the current learning path is determined according to the similarity;

[0066] In this embodiment, the complexity threshold is set based on a large amount of historical data and expert evaluation to ensure its rationality and accuracy. When the content complexity exceeds the student's current understanding ability, the system can intelligently identify this and start the adjustment mechanism. This dynamic adjustment capability enables the learning path to always fit the student's actual learning situation, avoids the "one-size-fits-all" teaching model, and improves the pertinence and effectiveness of learning.

[0067] In this embodiment, the historical data includes the previous learning behavior data of students on different learning paths.

[0068] The storage module is configured to store the learning path after the adjustment plan is determined.

[0069] It is understandable that the digital literacy improvement system for college students provided in this embodiment first accurately evaluates the initial literacy level of students through the test question generation and grade division module, providing a strong basis for the subsequent customization of learning paths. Subsequently, the learning module tailors the learning path for the students according to their initial literacy level and knowledge map, and collects the students' learning behavior data in real time during the learning process to calculate the content complexity of the current learning path. This process fully reflects the intelligent and personalized characteristics of the system. When the system detects that the content complexity of the current learning path may exceed the students' understanding ability, the analysis and processing module will intervene to intelligently determine whether the learning path needs to be adjusted by calculating the similarity between the current learning path and the historical learning path. If adjustment is required, the system will also formulate a scientific and reasonable adjustment plan based on the specific situation of the students. This dynamic adjustment capability makes the students' learning process smoother and easier to achieve the expected learning effect. Finally, the storage module will store the learning path after determining the adjustment plan to provide reference and reference for the subsequent learning process. This not only helps to improve students' learning efficiency, but also helps the system to continuously optimize its own algorithms and models to provide students with more accurate and personalized learning services.

[0070] It can be understood that the digital literacy improvement system for college students provided in this embodiment effectively improves the students' digital literacy level through intelligent, personalized and dynamic methods, and provides strong support for the all-round development of college students.

[0071] Specifically, the digital proficiency test exercises include digital survival ability test exercises, digital security ability test exercises, digital learning ability test exercises, digital innovation ability test exercises and digital collaboration ability test exercises.

[0072] Specifically, collecting the student's answer information data for each type of digital proficiency test questions and determining the student's initial literacy level based on the answer information data includes:

[0073] Assign points to each type of digital proficiency test questions;

[0074] Collecting the answer information data of the student, wherein the answer information data includes answer time, answer times and answer accuracy rate;

[0075] The answer score is determined based on the answer time, the number of answers and the answer accuracy, and the answer score is obtained by the following formula:

[0076]

[0077] Among them, Z represents the answer score, Wti represents the weight coefficient of the correct answer rate of the i-th type of digital proficiency test exercises, CRi represents the correct answer rate of the i-th type of digital proficiency test exercises, Wci represents the weight coefficient of the number of answers to the i-th type of digital proficiency test exercises, ATi represents the number of answers to the i-th type of digital proficiency test exercises, Wtimei represents the weight coefficient of the answer time of the i-th type of digital proficiency test exercises, Tmax,i represents the standard answer time of the i-th type of digital proficiency test exercises, and Ti represents the actual answer time of the i-th type of digital proficiency test exercises.

[0078] In this embodiment, when assigning points to each type of digital proficiency test questions, the scores of digital survival ability test questions, digital safety ability test questions, digital learning ability test questions, digital innovation ability test questions and digital collaboration ability test questions are 20 points, 15 points, 25 points, 20 points and 20 points respectively. Such score setting is determined based on the different importance and difficulty of each ability for improving the digital literacy of college students. By comprehensively considering the three aspects of answering time, number of answers and answer accuracy, the system can more comprehensively evaluate the students' answers, thereby more accurately determining their initial literacy level.

[0079] In this embodiment, Tmax,i represents the standard answering time for the i-th type of digital proficiency test exercises. The standard answering time is obtained by statistically analyzing the historical answering data of a large number of students, and reflects the average answering time for this type of exercises.

[0080] Specifically, when collecting the student's answer information data for each type of digital proficiency test questions and determining the student's initial literacy level based on the answer information data, it also includes:

[0081] Comparing the answer score with a first answer score threshold, a second answer score threshold, and a third answer score threshold, and determining the initial literacy level according to the comparison result; wherein the first answer score threshold is less than the second answer score threshold, and the second answer score threshold is less than the third answer score threshold;

[0082] When the answer score is less than or equal to the first answer score threshold, determining the initial literacy level to be the first level;

[0083] When the answer score is greater than the first answer score threshold and less than or equal to the second answer score threshold, determining the initial literacy level to be the second level;

[0084] When the answer score is greater than the second answer score threshold and less than or equal to the third answer score threshold, determining the initial literacy level to be the third level;

[0085] When the answer score is greater than the third answer score threshold, the initial literacy level is determined to be the fourth level.

[0086] In this embodiment, the first level is smaller than the second level, the second level is smaller than the third level, and the third level is smaller than the fourth level.

[0087] It is understandable that through this meticulous comparison and grading method, the system can accurately divide students into different literacy levels, thus providing a solid foundation for the subsequent development of personalized learning paths. The setting of each level is strictly based on the student's answer, ensuring the accuracy and fairness of the grading. At the same time, this grading method also helps students clarify their own strengths and weaknesses, so that they can study and improve more targetedly.

[0088] Specifically, when determining a learning path for learning based on the initial literacy level and the knowledge graph, it includes:

[0089] Collect digital literacy knowledge data and construct a knowledge graph; the knowledge graph includes a plurality of nodes and edges connecting the nodes, wherein each node represents a digital literacy knowledge point, and each edge represents an association relationship between two knowledge points;

[0090] According to the initial literacy level, selecting a digital literacy knowledge point matching the initial literacy level from the knowledge graph as the starting point of the learning path;

[0091] According to the starting point of the learning path, traverse along the edge of the knowledge graph, select a node associated with the current node as the next learning node, until all the digital literacy knowledge points matching the initial literacy level are traversed to form the learning path;

[0092] The learning is performed according to the sequence of the learning path, and the learning behavior data is recorded during the learning process.

[0093] In this embodiment, the digital literacy knowledge data includes various digital literacy-related theoretical knowledge, practical cases, and the latest research results.

[0094] In this embodiment, the nodes of the knowledge graph include multiple key areas such as digital survival ability, digital safety ability, digital learning ability, digital innovation ability and digital collaboration ability. Each node describes in detail the specific knowledge points and core concepts in the corresponding field. Through the edges between nodes, the system can clearly show the internal connection and logical relationship between each knowledge point, helping students build a comprehensive digital literacy knowledge system.

[0095] In this embodiment, the associated knowledge points of digital survival skills include, but are not limited to, information retrieval, data analysis, and basic office software usage;

[0096] In this embodiment, the associated knowledge points of digital security capabilities include, but are not limited to, network security awareness, password management, and anti-fraud skills;

[0097] In this embodiment, the association of digital learning ability includes, but is not limited to, learning methods, time management, information screening and critical thinking;

[0098] In this embodiment, the related knowledge points of digital innovation ability include, but are not limited to, innovative thinking cultivation, innovative tool use, creative practice, etc.;

[0099] In this embodiment, the related knowledge points of digital collaboration capabilities include but are not limited to teamwork, communication skills, online collaboration platform applications, etc.

[0100] In this embodiment, when the system selects the starting point of the learning path from the knowledge graph according to the initial literacy level, it will give priority to the knowledge points that best match the current level and that the students have not yet mastered or have not mastered firmly as the starting point. As learning progresses, the system will dynamically adjust the learning path according to the students' learning progress and performance to ensure that students can gradually improve their digital literacy. During the learning process, the system will also record students' learning behavior data in real time, including learning time, learning progress, learning results, etc. These data not only provide an important reference for students' self-assessment, but also provide data support for the subsequent personalized recommendations and optimization of the system. Through in-depth analysis of students' learning behavior, the system can more accurately grasp students' learning needs and preferences, thereby providing students with more accurate and effective learning support.

[0101] It is understandable that by constructing a knowledge graph, the system can clearly display the logical relationship between various knowledge points, helping students to better understand and master various aspects of digital literacy. At the same time, based on the students' initial literacy level, the system can intelligently plan a learning path for students that best suits their current level, ensuring that students can continuously improve their digital literacy in gradually in-depth learning. In the process of determining the learning path, the system fully considers the individual differences and interest preferences of students. By recording students' learning behavior data, the system can understand students' learning status and progress in real time, thereby dynamically adjusting the learning path and providing students with more personalized and accurate learning suggestions. This intelligent way of learning not only improves students' learning efficiency, but also stimulates students' interest and enthusiasm in learning.

[0102] Specifically, when calculating the content complexity of the current learning path according to the learning behavior data, it includes:

[0103] The learning behavior data includes learning time, learning frequency, learning accuracy and learning difficulty evaluation value;

[0104] The content complexity is obtained by the following formula:

[0105]

[0106] Among them, C represents content complexity, T represents learning time, S represents standard learning time, F represents learning frequency, N represents standard learning frequency, R represents learning accuracy, D represents learning difficulty evaluation value, H represents standard learning difficulty evaluation value, ω1, ω2, ω3 and ω4 are weight coefficients.

[0107] In this embodiment, the standard learning time is based on a large amount of historical learning data statistical analysis, reflecting the average time required for students to complete the learning of a certain knowledge point. The standard learning frequency is set according to the students' learning habits and efficiency, and is used to evaluate the students' learning enthusiasm and stability. The learning accuracy and learning difficulty evaluation values ​​reflect the students' mastery of the knowledge points and the difficulty of the knowledge points themselves, respectively. By substituting these data into the formula, the system can comprehensively evaluate the content complexity of the current learning path, thereby providing a basis for subsequent learning adjustments and optimizations. If the content complexity is too high, the system may suggest that students adjust their learning pace or provide students with more detailed learning guidance and support; if the content complexity is moderate or low, the system may encourage students to try higher-level learning content to further improve their digital literacy. In this way, the system can continuously track and evaluate students' learning status, provide students with personalized learning suggestions and support, and help students achieve comprehensive improvement in digital literacy.

[0108] Specifically, comparing the content complexity with the complexity threshold, and judging whether the current learning path needs to be adjusted according to the comparison result, includes:

[0109] When the content complexity is less than or equal to the complexity threshold, determining not to adjust the current learning path;

[0110] When the content complexity is greater than the complexity threshold, it is determined to adjust the current learning path.

[0111] In this embodiment, the complexity threshold is a reference value set by the system according to the student's learning ability and learning status, which is used to evaluate whether the current learning path is suitable for the student. If the content complexity is lower than the complexity threshold, it means that the current learning path is relatively simple, and the student may have mastered most of the knowledge points. At this time, the system will not adjust the path, but encourage students to continue to study in depth or try higher-level learning content. On the contrary, if the content complexity is higher than the complexity threshold, it means that the current learning path may be too complex or beyond the student's current ability. At this time, the system will adjust the path according to the actual situation. The adjustment methods may include increasing learning support resources, reducing learning difficulty, extending learning time, etc., to ensure that students can keep up with the learning progress and gradually improve their digital literacy level. In addition, the system will continuously optimize and adjust the learning path based on students' learning feedback and performance to provide students with a more personalized and effective learning experience.

[0112] Specifically, when determining to adjust the current learning path, it includes:

[0113] The similarity between the current learning path and the historical learning path is obtained according to the content complexity of the current learning path. The similarity is obtained by the following formula:

[0114]

[0115] Among them, S represents similarity, Cc represents the content complexity of the current learning path, and Ch represents the content complexity of the historical learning path.

[0116] In this embodiment, by calculating the similarity between the current learning path and the historical learning path, the system can evaluate the continuity and stability of the student in the learning process. If the similarity is high, it means that the student's learning path is relatively stable, and the system may only need to fine-tune the learning path to adapt to the student's learning progress; if the similarity is low, it means that the student's learning path has changed significantly, and the system may need to re-plan the learning path to ensure that the student can make progress in the new learning direction. In addition, the system will also analyze the student's mastery of different knowledge points in combination with the student's historical learning data, so as to provide students with more targeted learning suggestions and support. For example, if a student does not have a solid grasp of certain knowledge points, the system may recommend relevant learning resources or exercises to help students consolidate their knowledge and improve their abilities. In this way, the system can continuously track the student's learning status, and dynamically adjust and optimize according to the student's actual situation, so as to provide students with a more personalized and effective learning experience.

[0117] Specifically, when determining the adjustment scheme of the current learning path according to the similarity, it includes:

[0118] The similarity is compared with a similarity threshold, and an adjustment plan for the current learning path is determined according to the comparison result:

[0119] When the similarity is greater than or equal to the similarity threshold, selecting and adding a knowledge point that is related to the current learning path but has not been learned as a new learning node;

[0120] When the similarity is less than a similarity threshold, the initial literacy level of the student is re-evaluated, and the starting point of the learning path is re-selected from the knowledge graph based on the new initial literacy level to construct a new learning path.

[0121] In this embodiment, the similarity threshold is a reference value set by the system according to the student's learning characteristics and historical learning data, which is used to evaluate the similarity between the current learning path and the historical learning path. If the similarity is higher than or equal to the similarity threshold, it means that the student's learning path is relatively continuous and stable. At this time, the system may choose to add knowledge points related to the current learning path but not yet learned as new learning nodes to help students expand their knowledge and deepen their understanding of related knowledge points. This adjustment method helps to maintain students' learning motivation and interest, while avoiding repeated learning of knowledge points that have been mastered. On the contrary, if the similarity is lower than the similarity threshold, it means that the student's learning path has changed significantly, which may be caused by the student's own learning needs or changes in the external learning environment. At this time, the system will re-evaluate the student's initial literacy level and re-select the starting point of the learning path from the knowledge graph based on the new initial literacy level to construct a new learning path. This way of re-planning the learning path helps to ensure that students can make progress in the new learning direction and gradually improve their digital literacy level. In this way, the system can flexibly respond to students' learning changes and provide students with more personalized and effective learning support.

[0122] See also Figure 2 As shown, in some embodiments of the present application, this embodiment provides a method for improving digital literacy for college students, including the following steps:

[0123] S100: generating different types of digital proficiency test exercises, collecting students' answer information data for each type of digital proficiency test exercises, and determining the students' initial literacy level based on the answer information data;

[0124] S200: determining a learning path for learning according to the initial literacy level and the knowledge graph; collecting learning behavior data of the student in the current learning path, and calculating the content complexity of the current learning path according to the learning behavior data;

[0125] S300: Compare the content complexity with the complexity threshold, and determine whether the current learning path needs to be adjusted according to the comparison result:

[0126] S400: when it is determined that the current learning path needs to be adjusted, obtaining the similarity between the current learning path and the historical learning path according to the content complexity of the current learning path, and determining the adjustment plan of the current learning path according to the similarity;

[0127] S500: Storing the learning path after the adjustment plan is determined.

[0128] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0129] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0130] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0131] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A digital literacy improvement system for college students, characterized by: include: The test question generation and grading module is configured to generate different types of digital proficiency test questions, collect students' answer information data for each type of digital proficiency test questions, and determine the students' initial literacy level based on the answer information data; A learning module is configured to determine a learning path for learning according to the initial literacy level and the knowledge graph; and is also configured to collect learning behavior data of the student in the current learning path, and calculate the content complexity of the current learning path according to the learning behavior data; The analysis and processing module is configured to compare the content complexity with the complexity threshold, and determine whether the current learning path needs to be adjusted according to the comparison result: When it is determined that the current learning path needs to be adjusted, the similarity between the current learning path and the historical learning path is obtained according to the content complexity of the current learning path, and the adjustment plan of the current learning path is determined according to the similarity; The storage module is configured to store the learning path after the adjustment plan is determined.

2. The digital literacy improvement system for college students according to claim 1 is characterized in that: The digital proficiency test exercises include digital survival ability test exercises, digital security ability test exercises, digital learning ability test exercises, digital innovation ability test exercises and digital collaboration ability test exercises.

3. The digital literacy improvement system for college students according to claim 2 is characterized in that: Collecting the student's answer information data for each type of digital proficiency test questions and determining the student's initial literacy level based on the answer information data includes: Assign points to each type of digital proficiency test questions; Collecting the answer information data of the student, wherein the answer information data includes answer time, answer times and answer accuracy rate; The answer score is determined based on the answer time, the number of answers and the answer accuracy, and the answer score is obtained by the following formula: Among them, Z represents the answer score, Wti represents the weight coefficient of the correct answer rate of the i-th type of digital proficiency test exercises, CRi represents the correct answer rate of the i-th type of digital proficiency test exercises, Wci represents the weight coefficient of the number of answers to the i-th type of digital proficiency test exercises, ATi represents the number of answers to the i-th type of digital proficiency test exercises, Wtimei represents the weight coefficient of the answer time of the i-th type of digital proficiency test exercises, Tmax,i represents the standard answer time of the i-th type of digital proficiency test exercises, and Ti represents the actual answer time of the i-th type of digital proficiency test exercises.

4. The digital literacy improvement system for college students according to claim 3 is characterized in that: Collecting the student's answer information data for each type of digital proficiency test questions and determining the student's initial literacy level based on the answer information data also includes: Comparing the answer score with a first answer score threshold, a second answer score threshold, and a third answer score threshold, and determining the initial literacy level according to the comparison result; wherein the first answer score threshold is less than the second answer score threshold, and the second answer score threshold is less than the third answer score threshold; When the answer score is less than or equal to the first answer score threshold, determining the initial literacy level to be the first level; When the answer score is greater than the first answer score threshold and less than or equal to the second answer score threshold, determining the initial literacy level to be the second level; When the answer score is greater than the second answer score threshold and less than or equal to the third answer score threshold, determining the initial literacy level to be the third level; When the answer score is greater than the third answer score threshold, the initial literacy level is determined to be the fourth level.

5. The digital literacy improvement system for college students according to claim 4 is characterized in that: When determining a learning path for learning based on the initial literacy level and knowledge graph, it includes: Collect digital literacy knowledge data and construct a knowledge graph; the knowledge graph includes a plurality of nodes and edges connecting the nodes, wherein each node represents a digital literacy knowledge point, and each edge represents an association relationship between two knowledge points; According to the initial literacy level, selecting a digital literacy knowledge point matching the initial literacy level from the knowledge graph as the starting point of the learning path; According to the starting point of the learning path, traverse along the edge of the knowledge graph, select a node associated with the current node as the next learning node, until all the digital literacy knowledge points matching the initial literacy level are traversed to form the learning path; The learning is performed according to the sequence of the learning path, and the learning behavior data is recorded during the learning process.

6. The digital literacy improvement system for college students according to claim 5 is characterized in that: And calculating the content complexity of the current learning path according to the learning behavior data includes: The learning behavior data includes learning time, learning frequency, learning accuracy and learning difficulty evaluation value; The content complexity is obtained by the following formula: Among them, C represents content complexity, T represents learning time, S represents standard learning time, F represents learning frequency, N represents standard learning frequency, R represents learning accuracy, D represents learning difficulty evaluation value, H represents standard learning difficulty evaluation value, ω1, ω2, ω3 and ω4 are weight coefficients.

7. The digital literacy improvement system for college students according to claim 6 is characterized in that: Comparing the content complexity with the complexity threshold, and judging whether the current learning path needs to be adjusted according to the comparison result, includes: When the content complexity is less than or equal to the complexity threshold, determining not to adjust the current learning path; When the content complexity is greater than the complexity threshold, it is determined to adjust the current learning path.

8. The digital literacy improvement system for college students according to claim 7 is characterized in that: When determining to adjust the current learning path, it includes: The similarity between the current learning path and the historical learning path is obtained according to the content complexity of the current learning path. The similarity is obtained by the following formula: Among them, S represents similarity, Cc represents the content complexity of the current learning path, and Ch represents the content complexity of the historical learning path.

9. The digital literacy improvement system for college students according to claim 8 is characterized in that: When determining an adjustment plan for the current learning path according to the similarity, the method includes: The similarity is compared with a similarity threshold, and an adjustment plan for the current learning path is determined according to the comparison result: When the similarity is greater than or equal to the similarity threshold, selecting and adding a knowledge point that is related to the current learning path but has not been learned as a new learning node; When the similarity is less than a similarity threshold, the initial literacy level of the student is re-evaluated, and the starting point of the learning path is re-selected from the knowledge graph based on the new initial literacy level to construct a new learning path.

10. A method for improving digital literacy for college students, applied to the system for improving digital literacy for college students as claimed in any one of claims 1 to 9, characterized in that: include: Generate different types of digital proficiency test exercises, collect students' answer information data for each type of digital proficiency test exercises, and determine the students' initial literacy level based on the answer information data; Determine a learning path for learning according to the initial literacy level and knowledge graph; Collecting the learning behavior data of the student in the current learning path, and calculating the content complexity of the current learning path according to the learning behavior data; Compare the content complexity with the complexity threshold, and determine whether the current learning path needs to be adjusted based on the comparison result: When it is determined that the current learning path needs to be adjusted, the similarity between the current learning path and the historical learning path is obtained according to the content complexity of the current learning path, and the adjustment plan of the current learning path is determined according to the similarity; The learning path after the adjustment plan is determined is stored.

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