Innovative Models of Higher Education Management and Student Training Mechanisms in the Context of Internet Plus
Pubblicato online: 26 set 2025
Ricevuto: 01 gen 2025
Accettato: 01 mag 2025
DOI: https://doi.org/10.2478/amns-2025-1036
Parole chiave
© 2025 Shucheng Li, published by Sciendo.
This work is licensed under the Creative Commons Attribution 4.0 International License.
For a long time, the education and management methods of many colleges and universities have been more focused on the transfer of knowledge and skills as well as the rigid management of teachers and students, which to a certain extent affects the effectiveness of education and management. Higher education with “standardization”, “large class system”, “duck type”, “points only” as the main characteristics of the talent training system, system is an important embodiment of the traditional higher education system and mechanism. Talent cultivation system, system, is an important embodiment of the traditional higher education system, which can not keep up with the requirements of the new era, much less as an effective pillar of talent cultivation in the era of intelligence and information age [1-3]. In addition, today’s college students will face many challenges in school and society, they not only need to learn solid knowledge, but also need to obtain a variety of skills before graduation to cope with the challenges of fierce social competition [4-6]. In view of this, the innovative construction of student development-centered talent cultivation system in colleges and universities is a necessity of the times, which also means that the existing educational support system should be changed accordingly.
The emergence of “Internet+” technology has realized the in-depth integration of the Internet and all walks of life, and has further revolutionized the teaching mode and management system, greatly improving the management efficiency [7-8]. On the one hand, the integration of “Internet +” and the teaching and management of higher education has enriched the mode of higher education management, and some institutions of higher education are able to utilize this technology to effectively improve the mode of education management, promote the sharing of educational resources and management information within the university, and improve management efficiency [9-12]. On the other hand, the integration of “Internet +” and higher education teaching and management work has optimized the resource allocation of higher education institutions to a certain extent. By realizing the automation and intelligence of part of the education management work, and by reasonably allocating the resources for education management work, we can maximize the capacity and materials to be invested in the education work [13-15].
Under the background of “Internet+”, the management of higher education is developing in the direction of informationization and intelligence, so colleges and universities should also innovate the concept of education management and promote the reform of education management. Literature [16] studied the factors affecting the digital competence and digital transformation of higher education institutions, and further examined the impact of the integration of information technology and schools on students and stakeholders, indicating that information technology can help promote the digital transformation of schools, and education management will also realize effective and efficient changes. Literature [17] shows that the academic management system and operation mechanism that integrates information technology has significant improvement in efficiency, effectiveness and efficiency over traditional means of academic management, and the use of data mining technology to mine the knowledge hidden in large amounts of data can further enhance educational management. Literature [18] applies cloud technology to the activities of educational organizations and finds that the resulting new educational process and management system has certain advantages in the management of educational organizations, while attention needs to be paid to the ensuing cloud technology security issues. Literature [19] emphasized the importance of management innovation and AI technology in education, where innovation in education management is important for improving the quality of education, student competitiveness, efficiency and inclusiveness, while AI technology reduces the resistance to the introduction of innovation into education and achieves increased effectiveness in higher education. Literature [20] proposes the establishment of an integrated information system consisting of multi-business systems, which contains a decision support system, an early warning system, and a measure recommendation system will help decision makers to make more rational management decisions, and will help to improve the quality of educational data management and academic affairs management. Literature [21] examined the advantages of Moodle LMS tools in the process of digital transformation of universities, pointing out that the Moodle system is able to provide a comprehensive educational process, monitor and assess the quality of knowledge, and that by synergizing technological advances and management innovations, it is possible to make the Moodle LMS tools better for educational management. It can be seen that innovative education management has changed the traditional way of indoctrination and instruction, utilized big data, artificial intelligence and many educational tools to optimize the management content or management means, and innovated the operation mechanism and system of education management.
In this paper, the combination of education and gamification is envisioned, and after completing the entity-relationship ternary extraction of the knowledge graph, the course knowledge graph is constructed. Using gamification and knowledge graph technology, a teaching management platform based on gamification and knowledge graph is constructed through students’ interaction data, which encourages students to actively participate in classroom interactions, and at the same time analyzes students’ learning data and provides assistance for their learning. This enables personalized learning for students and adds a new model to the student development mechanism. Teachers can visualize the overall learning situation of students through this mode, which greatly facilitates educators’ management of courses and students.
In this paper, the innovative mode of higher education management and student training mechanism is studied. The traditional classroom is no longer able to fully attract the students’ attention, and at the same time, the amount of teaching data embodied in the traditional classroom is not able to let teachers recognize the real student learning situation in the era of big data. Therefore, this paper collects students’ learning behavior data through intelligent devices combined with gamification platform, and lets computers use data processing algorithms to accurately portray students, so as to realize teachers’ efficient management of students, and at the same time, realize the innovation of students’ cultivation mode by gamification platform.
The three elements of points, medals, and levels constitute a gamified PBL system, which is utilized to encourage users to complete tasks in a non-game boring scenario. Points reflect the user’s personal status, presenting the individual workload in a numerical way, which is very intuitive to portray the user’s status. Medal reward mechanism aims at encouraging users, clarifying their goals and giving them full motivation to complete the tasks; level represents the user’s hierarchy, reflecting the user’s contribution through hierarchical differentiation. Through these elements can be better non-game content so that users are very motivated to complete.
For gamification in education, a single PBL system is not the whole content of gamification, and it needs to be more combined with the updated footsteps of real games to make learning more “fun”. Examining the hottest game mode in the student circle and applying it to teaching, turning the teaching material into a copy of the game, on the one hand, mobilizes the classroom to a positive degree, but also improves the students’ motivation to learn, so that the students can continue to break through the self-exploration of the mystery of knowledge.
Traditional knowledge graphs [22] are defined as directed graphs based on relation triples and attribute triples.
Eq. (1) represents relationship = {head entity, relationship, tail entity} ternary.
Eq. (2) denotes attribute = {entity, attribute, attribute value} ternary.
There are two main definitions of multimodal knowledge graph (MMKG): multimodal data as specific attribute values of entities or concepts and multimodal data as entities in a knowledge graph.
In the process of constructing the curriculum knowledge graph, entities are categorized into algorithms, structures, related terms, and speech, and relationships are categorized into containment, antecedent, same, sibling, correlation, and association relationships. The adoption is to use multimodal data as entities in the knowledge graph.
The modalities involved in the teaching management platform include multi-source video [23], text and audio, if the multimodal data is used as the entity in the knowledge graph, then the data fusion will be very complicated, it is difficult to complete the training of the fusion model in a single space, which makes the multimodal data difficult to support the task of flexible teaching management.
Eq. (6) indicates that the set of graph attributes contains multi-source audio, video and text data.
The biggest advantage of using multimodal data as entities in the knowledge graph with unified spatial encoding is on intelligent search, which can accomplish multimodal intelligent search applications. However, the teaching management platform does not focus on the application of intelligent search, so the multimodal knowledge graph is constructed by using the specific attribute value method of multimodal data as entities or concepts in equation (6).
According to the platform application characteristics, the knowledge entities are categorized into four types: algorithms, structures, concept terms and formulas. Relationships are categorized into containment, antecedent, same, brother and related. Attributes are categorized into fine-grained multi-source audio and video, student learning and question bank data.
In the teaching management platform, knowledge is expressed in the form of ternary groups, and the extraction of ternary groups is realized with the help of DeepKE, so that the model has the ability to recognize the knowledge entities, and to realize the classification of the above five kinds of relations based on DeepKE.
One of the methods of knowledge extraction is carried out in a pipelined manner, i.e., entity extraction and relationship extraction are executed in steps. The named entity recognition technique is first utilized to extract entities, and then the relationships between these entities are identified, which in turn generates triples. The knowledge extraction process is shown in Figure 1.
Triplet extraction process of text knowledge points
From Figure 1, it can be seen that the most primitive text corpus will form a knowledge structure after data annotation, and the primitive corpus will be formed into DeepKE standard training corpus format after data preprocessing. Before the model training, the framework provided by DeepKE is used to configure the model into transformer model, which is used to complete the ternary extraction task, and after the model is configured, the labeled training corpus is loaded into DeepKE to start the model training. Constructing a knowledge graph can also be done by first constructing a simple knowledge graph using the already labeled triples in the training corpus. In the inference process, more triples in the corpus will be extracted, and the knowledge graph will be enriched gradually. That is, in the construction process of knowledge graph is used in the training set labeled ternary information, the training corpus labeled ternary in the data preprocessing time through the function in the DataMarking class to complete.
The original corpus of this paper is realized in two steps: firstly, the start and end indexes of the head and tail entities in the original corpus are put into a tuple, and the index ids of the relations are put into a list, and then the annotation format “[(0, 3), 0, (10, 13)]” is constructed, and then the preprocessing is carried out to extract and process the contents of the labeled index ids, and finally the training format “Bubble sort is a simple exchange sort, containing, exchange sort, 0, bubble sort, 10” is constructed. Then preprocessing is performed to extract the contents of the labeled index ids and finally construct the training format of “Bubble sort is a simple exchange sort containing, exchange sort, 0, bubble sort, 10”.
The ternary data is stored using neo4J graph data, in this paper python is used to build a connection with neo4J database to store the extracted ternary using the ternary storage logic.
When constructing the ternary relationship, the known ternary in the training set is stored in the neo4J graph database through the trainingSetTripleStorage function. The ternary extracted from the PaddleOCR recognized text and speech recognized text is stored in the neo4J database through the identificationResultTripleStorage function. Stored to the neo4j database.
After completing the extraction of entity-relationship triples of the knowledge graph, the course knowledge graph is constructed. Knowledge graph technology not only has important research value in the field of computer education, but also has important application significance in the field of higher education. The disciplines in the field of higher education are highly structured and constitute a system between knowledge points, so the knowledge mapping technology is also applicable.
In the process of constructing user profiles for personalized learning paths, the analysis of users purely from the perspective of the learner model has greater limitations because the data used for modeling are mostly static data, and even if the learning style modification algorithm uses learning behaviors to adjust the learning style to obtain a more realistic user learning style, it lacks the real-time nature of the content of the user’s personalized learning. In addition, traditional analytics methods lack semantic parsing when analyzing user behavior. The entities in the user’s search behavior are extracted and then sorted according to their importance, which can better reflect the user’s real-time interest, and Figure 2 shows the flowchart of constructing an image based on the user’s interest entities [24].
Process of constructing portrait based on user interest entity
Using gamification and knowledge mapping technology to build a gamification teaching management platform and a knowledge mapping teaching management platform based on students’ interaction data, the gamification platform is used to encourage students to participate in the classroom, and the knowledge mapping teaching management platform is used to do data analysis of students’ interaction data and to provide assistance for students’ learning. The teaching management platform application in this paper combines the advantages of gamification and knowledge mapping to achieve personalized learning for students. The general framework of the teaching management platform application is shown in Figure 3:
Teaching management platform application framework
The gamified classroom platform uses smartphones as the main means of classroom interaction for students. After logging into the gamified interactive platform, students can use the pop-up and shake functions in the platform. The pop-up function is when students are interested in a certain knowledge point or a sentence narrated by the teacher to interact with the teacher in the form of text, the content of the pop-up sent by the students will be displayed in the teacher’s PPT page at the first time, and the teacher will be able to quickly understand the corresponding students’ learning status and students’ interests after seeing the corresponding pop-ups.
The second function is the shake function, which is inspired by WeChat’s shake function, when students are confused or do not understand the content of the teacher, they can shake their phones and record it to their own smart device interface, at the same time, when more than 30% of the students in the class shake their phones at the same time, it means that this is the key point of the lesson, and the teacher needs to adjust his teaching mode and progress according to the feedback of the students at this moment. The teacher needs to adjust his/her teaching method and progress according to the students’ feedback at this moment. All the operations done by students by logging into the interactive platform in class will be recorded in the database and saved as the most original behavioral data, on the one hand, for the later teaching management platform to do data support, but also for the students to keep learning traces of class learning, every pop-up screen sent by the students and the shake operation with the corresponding time and the number of pages of the PPT at the same time as the student’s process of learning data to be saved.
The underclass design of the gamification platform by layering knowledge points, high-level knowledge points can be broken down into a number of low-level knowledge points, and vice versa, a number of related low-level knowledge points can be combined to form a high-level knowledge points, the difference between low-level knowledge points and high-level knowledge points lies in the breadth of the content, the high-level knowledge points are more abstract, and the low-level knowledge points are the specific basic content. Some students like to learn the basics first, while others like to learn from the higher level knowledge first, and then explore the lower level knowledge. Splitting and combining gamification platform is precisely for this kind of psychology of students to split and combine in the form of cards to show to students, students through the pre-study and review two modes of knowledge to learn multiple times, students who like to split need to learn from the advanced knowledge points, through the splitting into small knowledge points, and then learn the small knowledge points, and vice versa, the combination of the students are also the same. The platform will also record every operation of the students, do the corresponding points accumulated, when the students do a split or combination of operations plus two points, pre-study and review plus one point, so as to record the score of each student, and at the end of the time on the total score of the students ranked, ranked in the front of the students can be prioritized to choose the order of the defense.
By entering students’ basic information and student behavioral data from interactive systems and gamification platforms into the data visualization platform, the student data is presented in an organized manner through display charts in Echarts. The platform presents the multi-source learning data generated by students in the classroom, various life data after class, and students’ personal information as intuitive charts, labels, and user profiles, etc. It is mainly used in today’s colleges and universities to visualize students’ and teachers’ educational data and the latest career information data, and to give recommended courses and recommended job search directions based on students’ personal information and comparing with the latest career needs. The job search direction. Provide accurate course, person and career related searches. Use knowledge graph to build course knowledge base and students’ social relationship knowledge base, through which the system can explain the knowledge points that students don’t understand in the course and recommend related knowledge for them based on what they don’t understand, such as leading and following knowledge, and it can also recommend suitable career positions and compatible courses based on the students’ personality and skill labels. This platform software is divided into two major parts: the student platform and the teacher platform.
The student platform mainly focuses on seamlessly integrating and visualizing the multi-source data generated by college students in and out of the classroom and in their lives. The platform makes specific courses available for students to view in the form of knowledge maps, so that students can intuitively understand the relationships and weights between knowledge points. The platform collects students’ behavioral data through various intelligent hardware and teaching systems, and processes and clusters the data accordingly to make it more valuable.
At the same time, the platform collects students’ personal data, such as skill data, personality, knowledge data, etc., and compares them with the latest career information, and recommends the most suitable career positions, workplaces and corresponding courses to students. The teacher’s platform is a collective display platform for all class data, which allows teachers to clearly and intuitively see the overall situation of the students in the classroom, their mastery of the knowledge points, the employment information of all students, and the detailed personal data of each student. It greatly facilitates the teacher’s management of courses and students.
The implementation of this teaching experiment lasted about three months, two classes in a university were randomly selected as the control class and the experimental class, the number of students in both the control class and the experimental class was 42, and their math scores were selected as the experimental samples, starting from the first chapter quiz test to the end of the final exam, the teaching experiment took the students’ scores on the first chapter quiz of math as the pre-test data. After the pre-test, the teaching management platform was introduced into the daily course teaching of the experimental class. During the experiment, the teaching tasks were performed by the same teacher. The first experimental class was taught by the teacher, who briefly introduced the system to the students and taught them how to use the system, and informed them that they could search for knowledge through the system at any time in the classroom, as well as answer the homework and discuss the topics on the system when the teacher assigned the accompanying homework and topics. The control class, on the other hand, learns the course in a normalized state without external influences, with the purpose of comparing their grades with the experimental class. The final grades of the students were used as post-test data in this experiment.
In order to determine whether there is a significant difference between the students of the two classes before and after the experiment, a paired-sample t-test was conducted on the midterm grades and final grades of the experimental and control classes, and the specific test results are shown in Table 1. From Table 1, it can be seen that: the mean value of the experimental class’s midterm grades is 86.23, and the mean value of the final grades is 89.95, which is a significant achievement improvement. The control class had a midterm grade average of 85.12 and a final grade average of 85.23, with no significant grade improvement. By comparing and analyzing the data with the pre-test scores, the mid-term and final grades and the students’ weekly average weekly test scores are imported into EXCEL and a graph is created to visualize the trend of change between the data, and the visualized results are shown in Fig. 4, where the x-axis ordinal numbers 1-15, 1 represents the pre-test scores, 88 represents the midterm scores, 15 represents the final scores, and the rest of the ordinal numbers represent the weekly test scores, respectively. Figure 4 shows that the upward trend of the experimental class is significantly higher than that of the control class, and the final grade of the experimental group is 4.72 points higher than that of the control group.
The midterm and final results are compared and analyzed
| Mean value | Case number | Standard deviation | Standard error mean | |
|---|---|---|---|---|
| Laboratory class (midterm) | 86.23 | 42 | 6.451 | 1.03 |
| Laboratory class (Final examination) | 89.95 | 42 | 6.235 | 0.98 |
| Cross-reference class (midterm) | 85.12 | 42 | 8.269 | 1.21 |
| Cross-reference class (Final examination) | 85.23 | 42 | 8.965 | 1.36 |
The two components are changing
The paired samples t-tests were done on the midterm and final grades of the experimental and control classes, and the results of the samples t-tests are shown in Table 2. From the contents of Table 2, it can be seen that: in the control class, the value of significance is 0.954 > 0.05, which indicates that there is no significant difference between the midterm grades and the final grades of the control class. In the experimental class, the value of significance is 0.026 < 0.05, indicating that there is a significant difference between the midterm and final grades in the experimental class. This indicates a significant effect of whether or not the course is taught using the instructional management platform on student performance.
Final grade matching sample t test
| Laboratory class | Cross-reference class | |
|---|---|---|
| Mean value | -3.131 | -0.095 |
| Standard deviation | 8.512 | 12.315 |
| Standard error mean | 1.359 | 1.845 |
| The difference is 95% of the confidence interval | -5.818 | -3.845 |
| The difference is 95% true interval limit | -0.423 | 3.648 |
| t | -2.359 | -0.047 |
| freedom | 41 | 41 |
| Significance (double tail) | 0.026 | 0.954 |
In order to further analyze the experimental results, independent samples t-test was conducted on the midterm final grades of the two classes respectively, and the test results are shown in Table 3. As can be seen from Table 3: the F value of the midterm grades of the two classes is 2.175, and the value of significance is 0.146 > 0.05, which indicates that there is no significant difference between the data, indicating that the variance is consistent, and therefore it is necessary to focus only on the data of the hypothetical equal variance. The value of significance in the t-test of equivalence of means is 0.825 > 0.05 which indicates that there is no significant difference between the midterm grades of the two classes. The F-value of the final grades of the two classes is 8.914, and the value of significance is 0.003 < 0.005, which indicates that there is a significant difference between the data, indicating that the variance chi-square is inconsistent, and attention needs to be paid to the data that do not assume equal variance. The value of significance in the t-test of equivalence of means is 0.047 < 0.05, indicating that there is a significant difference between the final grades of the two classes, indicating that the instructional management based on gamification and knowledge mapping designed in this paper is effective in the enhancement of students’ performance, and that this innovation is able to dedicate itself to the training of students.
Final and interim performance independent sample t test
| Midterm | Final examination | ||||
|---|---|---|---|---|---|
| Assumed equal variance | Unassuming equal variance | Assumed equal variance | Unassuming equal variance | ||
| Levin variance equivalence test | F | 2.175 | 8.914 | ||
| significance | 0.146 | 0.003 | |||
| Average equivalent t test | t | 0.215 | 0.213 | 1.978 | 2.003 |
| freedom | 84 | 81.456 | 83 | 78.945 | |
| Significance (double tail) | 0.825 | 0.829 | 0.052 | 0.047 | |
| Mean difference | 0.364 | 0.346 | 3.374 | 3.381 | |
| Standard error difference | 1.651 | 1.625 | 1.705 | 1.687 | |
| The difference is 95% of the confidence interval | -2.911 | -2.891 | -0.008 | 0.026 | |
| The difference is 95% true interval limit | 3.617 | 3.545 | 6.779 | 6.745 | |
Davis designed the Technology Acceptance Model (TAM) in 1989, the TAM model mainly includes four dimensions, namely, perceived ease of use, perceived usefulness, user attitude, and behavioral willingness, and this model is widely used to judge the user’s acceptance level of computers, and this paper designs evaluation indexes based on the TAM model as a way of judging the application of the Instructional Management Platform based on Gamification and Knowledge Mapping. Effectiveness. The questionnaire design of this paper is based on the evaluation indexes of the application effect of the teaching management platform based on gamification and knowledge mapping, and incorporates a 5-point Likert scale, which is divided into 1 (strongly disagree) to 5 (strongly agree) levels, with the higher the level, the stronger the degree of agreement, i.e., the better the application effect of the system. Table 4 shows the content of the evaluation indicators.
Application effect evaluation index
| Index | Index judgment | Metric |
|---|---|---|
| Learning resources | Knowledge organization visualization | It is useful to show the knowledge of knowledge. |
| Recommendation for learning resources | Learning resources are recommended. | |
| Learning companion | Peer group | Learning peer group is effective. |
| Learning path | Recommendation for learning path | The learning path is recommended. |
| Learning path rendering | The learning path is clearer in the form of knowledge. | |
| Perceptual ease of use | System operability | The system works simple. |
| Perceptual usefulness | The usefulness of the system | The system can help me learn. |
| User attitude | Learners’ satisfaction | I am willing to use the system to study. |
| Behavior will | The use of learners | I often use the system to study. |
Through the questionnaire star random online distribution of questionnaires, survey learners in the use of gamification and knowledge mapping based on teaching management platform after the feeling, this time a total of 50 questionnaires issued, 50 questionnaires recovered, the basic situation of the survey respondents are shown in Table 5. from Table 5 can be seen, the questionnaire female students to fill in the majority of the number of students, the science and the arts of the learners basically about the same, and fill in the learners for the master’s degree graduates, accounting for 40% of the students.
The basic situation of the survey
| Categories | Options | Number | Proportion |
|---|---|---|---|
| Gender | man | 12 | 24% |
| female | 38 | 76% | |
| Professional background | science | 24 | 48% |
| Liberal arts | 26 | 52% | |
| Educational background | Specialty and below | 6 | 12% |
| undergraduate | 15 | 30% | |
| Master graduate | 20 | 40% | |
| Doctoral student | 9 | 18% |
A descriptive analysis of the recovered questionnaire data was conducted as an illustration of the application effect of the gamification and knowledge graph-based instructional management platform, where the serial numbers 1-9 on the x-axis represent the number of the questions for the learning resource indicator, learning companion indicator, learning path indicator, perceived ease of use indicator, perceived usefulness indicator, questions for the user’s attitude indicator, and questions for the behavioral willingness indicator, and the serial numbers 1-5 on the y-axis represent the number of the questions for the 1 ( Strongly Disagree)-5 (Strongly Agree). Figure 5 shows the results of the descriptive analysis of the questionnaire, from which it can be clearly seen that in terms of learning resources, learning peers, learning paths, perceived ease of use, perceived usefulness, user attitudes and behavioral intentions, most learners choose the two options of “agree” and “strongly agree”, and the number of people who choose strongly disagree is less than 5% in each question, or it indicates that learners think that this platform is no matter from which perspective after using the teaching management platform based on gamification and knowledge graph. Therefore, it can be said that the application effect of the teaching management platform based on gamification and knowledge graph constructed in this paper is relatively good, which is conducive to learners’ learning and is helpful to learners, which can not only improve the learning effect of students, but also facilitate teachers to understand the learning progress of students, and facilitate the centralized management of students.
Frequency analysis results
The intelligent teaching management platform provides support for students to adapt to the development of the information-based society and the realization of students’ information-based cultivation, and also aids the efficient management of educational administrators.
The average of the final grade of the experimental class is 89.95, which is a significant improvement compared with its mid-term grade. The average of the midterm and final grades of the control class was 85.12 and 85.23 respectively, with no significant improvement. The overall trend of the experimental class is significantly higher than that of the control class, which shows that the teaching management platform based on gamification and knowledge mapping can improve students’ motivation in the classroom, and thus enhance their academic performance. In the seven aspects of learning resources, learning peers, learning paths, perceived ease of use, perceived usefulness, user attitudes and behavioral willingness, more students chose “agree” and “strongly agree”, and the number of those who chose strongly disagree was almost none. The number of students who choose strongly disagree is almost none, which shows that students are positive and supportive of the platform designed in this paper, and also shows that the application effect of the intelligent teaching management platform constructed in this paper is better, which not only helps students’ learning, but also facilitates the teachers’ understanding of students’ interaction data, and improves the efficiency of students’ education management.