Innovative Information Technology-Based Teaching Methods for Fingerprint Identification in Criminal Technology Programs and Teacher Skill Development
Published Online: Mar 17, 2025
Received: Oct 05, 2024
Accepted: Jan 31, 2025
DOI: https://doi.org/10.2478/amns-2025-0280
Keywords
© 2025 Yujie Deng, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
As a comprehensive science involving knowledge of many disciplines, criminal science, and technology plays a great role in the struggle between public security organs and criminal offenses and can utilize the principles and methods of natural and social sciences to expose, confirm, and prevent all criminal acts [1-3]. Criminal science and technology is an important part of science and technology to strengthen the police and occupies an important position in the detection of criminal cases.Its role and significance lie in improving the efficiency of investigation, ensuring the accuracy and reliability of the evidence, as well as guaranteeing the fair trial of the case [4-6]. With the continuous development and innovation of science and technology, the application of criminal science and technology in criminal investigation will be more extensive and in-depth and play an increasingly important role in maintaining social security and judicial justice.
The severe social security situation urgently needs the teaching concept, teaching methods and means, and teaching conditions of criminal technology in public security colleges and universities to be compatible with it. However, the teaching of criminal technology has always been disconnected from theory and practice; the teaching mode is obsolete, and the teaching reform is seriously lagging [7-8]. Using fingerprint identification technology for criminal technology teaching, applying modern public security science and technology to classroom teaching, and realizing the integration of modern information technology and curriculum will certainly promote the reform of criminal technology teaching mode and the scientificization of teaching methods [9-12]. On the one hand, the use of fingerprint identification teaching methods is conducive to cultivating students’ awareness and ability to use fingerprint identification technology to detect and solve crimes and laying a solid foundation for giving full play to the functions of the system [13-15]. On the other hand, it is conducive to students to familiarize themselves with the system equipment in advance, and the repeated operation and use of students in the daily teaching of criminal technology and practical skills training, they can master the operation skills and adapt to the rapid development of the application of fingerprint identification technology at the grassroots level of public security more quickly [16-19].
This paper analyzes the current fingerprint inspection technology experiment to carry out the process and puts forward the virtual practical training platform into the fingerprint identification inspection teaching to carry out the criminal science and technology course intelligent teaching mode. Analyze the significance of building a virtual simulation platform for criminal technology course practice and design the teaching process of criminal technology courses using the virtual simulation platform for practical training to carry out on-site investigation. Combined with the development needs of the faculty team of criminal technology, it is proposed to cultivate “dual-teacher” teachers with comprehensive business ability (teaching, scientific research, and case handling). The virtual simulation teaching environment is built, and the advantages of the collision detection algorithm proposed in this paper are examined using comparative tests. It also analyzes the number of fingerprint identification teaching strategies that meet the standard and the informatization teaching ability of criminal technology teachers.
Usually, handprint examination techniques include the identification of the pattern structure and characteristics of fingerprints and the discovery, extraction, and fixation of common handprints. Students are required to be able to achieve proficiency in recognizing and identifying the pattern categories and names of fingerprints and palm prints of each finger and to master the ability to discover, fix, and extract the fingerprints and palm prints of different objects potentially, and further to be able to utilize the same principle of identification to conduct judicial appraisal and other judicial practice activities.
East China University of Political Science and Law College of Criminal Justice to open the handprint inspection technology experiments. For example, in the fingerprinting experiments, usually first watch the teaching video, and then arrange for students to group fingerprint printing card production, and finally, the teacher to check the experimental effect. Long-term teaching practice has found that the basic knowledge of the experiment obtained by watching the teaching video is not able to carry out a complete test of the experimental effect. For some of the poorly completed experimental assignments, after the experimental instructor on-site inquired about the experimental operation process, the students could not find out their shortcomings in the experimental operation. At the same time, the instructor could not make targeted corrections to all students’ operations after the class. For such an experimental operation course, it has little effect on the mastery of theoretical knowledge points, students’ ability to improve and practical application of the ability to exercise.
The handprint/fingerprint examination course is an applied criminal science and technology professional course that closely integrates basic theoretical knowledge with practice. The traditional handprint examination course adopts a single offline lecture, failing to apply the theory to practice. However, in the actual teaching experience, students for a variety of handprint testing practice, not the teacher “a point that is penetrating”, the teacher often needs to be repeated in the subsequent practice for the same point of explanation and demonstration. Even so, teachers also find it difficult to guide students on the course of scientific research project thinking. Through the virtual training platform, students can study on their own on the knowledge points that have not been fully mastered and can grasp the content more solidly through repeatable online learning before the practice class. At the same time, students are able to carry out in-depth research on topics of interest and stimulate their passion for learning.
Practical ability is an important part of the core competence of public security students in public security colleges and universities, and the criminal technology course is an application-oriented course built on the basis of professional theories with strong practicality.
From the previous training situation, the equipment required for practical training loss, instrument consumption, and simulation of the case scene is more difficult to build.
The emergence of virtual simulation technology has opened up a new way of practical teaching of “intelligence + education” [20-21]. Applying a virtual simulation platform in training can provide a platform closer to reality for practical teaching, improve the traditional classroom to cultivate students’ exploratory ability and innovation ability on the lack of motivation, and enhance students’ practical ability and innovation ability.
Through the reform, the teaching content is systematically sorted out, integrated, and summarized, and the knowledge and skills of the on-site investigation are extracted from the thematic content. And on the basis of the five components of deconstructing the case, “source”, “object”, “time”, “geography,” and “tool”, the ability training project is formed.
The significance of the project:
On the one hand, by revealing the qualities of the case components and the correlations between them, it is appropriate to motivate students to examine each component independently and to clarify the coupling phenomenon of different components.
On the other hand, under the framework of thinking orientation, learning activities that meet the cognitive level of students are well constructed, leading students to achieve progressive thinking ability from “immersive” (what is happening) to “hook the summary” (what are the key components), and then to “peek into” (learn logical reasoning).
Finally, the ability training program combined with the virtual simulation platform for practical training, in addition to enhancing the students’ experience of the view of the components, but more to stimulate students’ interest in exploring the systematic nature of the content, breaking the silence of the classroom.
The criminal technology course reform framework is shown in Figure 1. The overall reform measures not only expand the learning resources and space but also promote the image of students to understand the relationship between the crime scene and various types of physical evidence and effectively improve the students’ ability to investigate the scene.
The framework of the reform of the criminal technology course
The on-site investigation should be three-dimensional. In order to maximize the expansion of the types of physical evidence and extraction paths, in addition to the investigation of traditional fingerprints, footprints, DNA, and other information, should also be involved in the case of video data, case geographic information, base station information, and so on. In the teaching case, all kinds of roles (such as case officers, suspects, informants, witnesses, etc.) appeared in the time sequence, space, behavior, psychological state, the use of tools and other information, the role of each other will be presented in the “seventy-two changes” like the combination. If we only rely on traditional classroom teaching, it is difficult to clearly convey complicated information and knowledge to students.
After the introduction of the virtual simulation platform, the above problems can be solved, and the transformation from traditional education to intelligent education can be realized. Before the introduction of the platform, teachers design three steps: selecting the story prototype (writing the script), summarizing the various problems affecting the components (improving the teaching content), and organizing the visual story (deductive case). The design idea can refer to online games such as “tower defense” and “CS” and build a training system that is highly close to the real scene as structural components such as scene objects (terrain/architecture/light), game objects (parties involved), game logic (principle explanation/induction ability/problem-solving ability/reasoning ability), game effect objects (interface design/special effects/sound) and tools (observation tools/lasso tools/text tools). After the introduction of the platform, it simplifies the behavioral results of the combination of “seventy-two changes”.
The combination of generalized behavioral results and the virtual simulation platform is shown in Figure 2. Focusing on the above behavioral results, the virtual simulation platform can abstract the typical performance of the components in the development process of the case and the coupling between the components of the prominent performance, focusing on the generalized behavioral results from various components. At the same time, the virtual simulation platform is built around the reflection of the above content, allowing students to experience the results of different combinations of components within the platform and guide students to think logically and creatively about the results. This is intended to realize the enhancement of students in the application of law enforcement basis, the use of investigation tools, on-site investigation, and other aspects of the comprehensive ability to train students to investigate all kinds of traces of physical evidence of logical reasoning of higher-order thinking ability.
The combination of general behavior results and virtual simulation platforms
In the development process of the virtual simulation platform system for practical training, the construction of 3D models is the most time-consuming, so the model construction is carried out using a combination of automatic 3D modeling technology and traditional 3D modeling technology. Automatic three-dimensional modeling technology through the photo of the object to restore the three-dimensional model of the technology is called three-dimensional reconstruction. You can directly use the three-dimensional reconstruction technology to build a good three-dimensional model or according to the need to use three-dimensional reconstruction technology to build a model of the overall contour. Then through the traditional 3D modeling technology to optimize the details, to get the development of the required 3D model, greatly saving the system development time.
As 3D reconstruction is an algorithmic processing of photos to reconstruct the three-dimensional structure of the object, before 3D reconstruction, the camera imaging principle of computer vision is studied and analyzed. The key to camera imaging in computer vision is the conversion between coordinate systems, which mainly involves the conversion between four coordinate systems: world coordinate system, camera coordinate system, image coordinate system, and pixel coordinate system.
The conversion process of the world coordinate system to the camera coordinate system contains rotation and translation. Rotating different angles around different coordinate axes can obtain the corresponding rotation matrix. That is:
The same reasoning can be obtained when rotating around the x and y axes:
From the above, we can get the rotation matrix
Through the above analysis combined with the non-chiral coordinates and chiral coordinates conversion method can be obtained
The conversion between the camera coordinate system to the image coordinate system is a perspective projection relationship from three dimensions to two dimensions. Based on the principle of triangulation, it is possible to derive the conversion from the camera coordinate system to the image coordinate system:
At this point the projected points are still in mm and need to be further converted to pixels in the image coordinate system.
The image coordinate system and the pixel coordinate system are both in the plane of the image, but they have different origins and different units of measure. The unit of the image coordinate system, mm, belongs to the physical domain, while the unit of the pixel coordinate system, pixel, does not belong to the physical domain; let the pixel coordinate system be uov, and the image coordinate system be xoy.
Let the number of mm in each row and column be dx and dy, i.e., 1pixel = dxmm, with the following relation:
Through the above transformation relationship between the four coordinate systems, the conversion process of a point in the real world from world coordinates to pixel coordinates can be deduced:
Where
Usually, the camera’s internal and external references can be obtained directly through calibration or open-MVG technology, and as long as the value of
The OBB box is a rectangular box, similar to the AABB box, but its direction is not along the coordinate axes but arbitrary.The OBB box can be expressed in many ways, such as by the point set of eight vertices, the face set of six faces, the geometry of three sets of parallel planes, a vertex, and three edge vectors orthogonal to each other, but it is most convenient to use the centroid, a rotation matrix, and the lengths of the three half edges. The intersection test of this expression is simpler compared to other ways.
In this paper, this expression is used to represent the OBB bounding box, which is much more complicated to construct than the AABB bounding box. Moreover, a poorly aligned OBB box and a better-aligned box will lead to a huge difference in the computational efficiency of collision detection. It isn’t easy to compute OBB boxes that can be tightly fitted. The most important step in the construction process of the OBB enclosing box is to find a better direction (it is difficult to find the optimal direction in general) and to determine the minimum size of the object to be enclosed in this direction.
In this paper, we propose a method of calculating the OBB enclosing box based on the vertex coordinates of the model, which mainly utilizes the first-order and second-order statistical properties of the vertices coordinates of the model to calculate the OBB enclosing box. Firstly, the average value of the coordinates of all vertices of the model is calculated, and then the covariance matrix of these points is derived, and based on the covariance matrix, the enveloping box is calculated. The specific procedure is as follows:
where
(3) The three dimensions of the OBB enclosing box are determined by calculating the maximum and minimum values of each vertex
(4) The center of the OBB enclosing box c is the average of the maximum and minimum values of the projection. i.e.:
To date, most institutions already have a group of teachers with high professional quality, which has played a great role in cultivating high-quality public security talents. However, due to the short period that public security institutions of higher learning have been operating and the rapid pace of development, the faculty team is still relatively weak compared with that of ordinary institutions.
“Having a sense of scientific research and the ability to conduct scientific research is an important guarantee for the continuous development of teachers’ professional ability and is also an inevitable requirement for making teachers’ work full of creative spirit and vitality.” As teachers, on the one hand, lack practical experience in public security, their teaching is detached from public security practice, and they cannot find out the research topics from the actual work of public security. On the other hand, they also lack the necessary criticism and reflection on their teaching work and cannot actively explore the problems of education and teaching. As a result, many professional teachers have nothing to do in scientific research and are busy with their daily teaching work. At the same time, the scientific research work of the whole school also lacks effective organization and management. In terms of academic level, public security colleges and universities are generally lower than local colleges and universities due to their short history of development.
Teacher team building is a safeguard measure for professional teaching reform. Teacher team building mainly carries out the following aspects:
1) Comprehensively improving the ideological and political quality and business quality of teachers. Encourage teachers of criminal science and technology to participate in high-level training and strive to form a group of backbone teachers and academic leaders in this specialty. Young teachers under the age of 40 are required to formulate the Individual Planning for Young Teachers’ Academic Qualification (Degree) Enhancement Project so as to improve the academic level of the teaching force. Implement the guidance system for young teachers, designate teachers with senior professional and technical positions and higher seniority to guide young teachers, pass on, help, and bring up so that young teachers can familiarize themselves with the business as soon as possible and improve their teaching level. 2) Increase the proportion of “dual-teacher” teachers. Encourage teachers to actively apply for the national “forensic appraiser qualification” and obtain the qualification of the appraiser. Actively declare the professional and technical positions of criminal science and technology appraisal series. To the evidence identification center or off-campus internship base to carry out criminal science and technology testing and identification and scientific research work. As well as in the public security grass-roots exercise, thereby increasing the proportion of “dual-teacher” teachers as a specialized course of teaching. 3) Improve teachers’ comprehensive business ability by combining teaching, scientific research, and case handling. Encourage teachers to combine teaching with the practice of criminal science and technology testing and identification. Actively carry out scientific research activities in criminal science and technology identification, and strive to obtain a higher level of scientific research projects. Actively undertake criminal science and technology testing and identification work so as to continuously improve the business and academic level of teachers.
Hardware environment: Intel(R)Core(TM) i52022CPU@3.65GHz processor, NVIDIA GeForce GTX650 graphics card, and 6GB RAM.
Software environment: Window10 operating system, Visual Studio programming environment, Unity3D (5.5.3) virtual simulation engine as the simulation development platform. In Unity3D, scripts can be written in programming languages such as C#, Javascript, and Boo. Since C# is inherited from C+ and C++ languages, it has the features of simple syntax and powerful functions. Therefore, this paper adopts C# language to write the experimental program.
Experiment: choose the built-in sphere and capsule body in Unity3D as collision detection objects. In order to better test the comprehensive performance of the algorithms proposed in this chapter, two groups of anti-collision algorithms with different real-time and accuracy are selected as comparison methods.
Algorithm I: the calculation method of OBB enclosing box based on model vertex coordinates proposed in this paper.
Algorithm II: Bisection backward collision avoidance method based on AABB hierarchical bracket box.
Algorithm III: bisection backward collision avoidance method based on OBB enclosing box. This method only constructs the outermost OBB for the model. Instead of a complete hierarchical tree structure, collisions are detected by performing a split-axis test on the OBB at each position of the bisection backtracking. Each experiment is executed 50 times and the average of the results of each experiment is taken.
Scenario 1: A capsule body moves forward and rotates at a certain speed and eventually collides with another capsule body at rest.
Scenario 2: - A capsule body with a certain speed does uniform forward and rotary movement and finally collides with another sphere at rest.
Set up the Yahoo conditions for the execution of the algorithms:
The algorithm execution time (in ms) is shown in Table 1.
Algorithm execution time (unit:ms)
| Algorithm scenario | I | II | III | ||||
|---|---|---|---|---|---|---|---|
| Translation velocity | Rotational speed | Scene 1 | Scene2 | Scene 1 | Scene 2 | Scene 1 | Scene2 |
| 5 | 100 | 13.56 | 14.24 | 19.57 | 18.65 | 0.89 | 0.83 |
| 150 | 21.54 | 20.16 | 17.64 | 19.85 | 0.89 | 0.91 | |
| 200 | 14.65 | 18.78 | 23.21 | 17.03 | 0.91 | 0.83 | |
| 250 | 25.34 | 41.32 | 25.36 | 29.34 | 0.93 | 0.92 | |
| 300 | 14.56 | 13.68 | 20.73 | 21.32 | 0.86 | 0.85 | |
| 10 | 100 | 16.85 | 18.69 | 15.64 | 10.05 | 0.86 | 0.83 |
| 150 | 13.24 | 15.86 | 16.28 | 19.58 | 0.82 | 0.89 | |
| 200 | 19.86 | 12.53 | 18.76 | 16.42 | 0.93 | 0.89 | |
| 250 | 12.32 | 12.26 | 18.96 | 16.65 | 0.93 | 0.98 | |
| 300 | 16.58 | 17.23 | 20.68 | 15.16 | 0.86 | 0.82 | |
| 20 | 100 | 36.25 | 30.75 | 14.65 | 23.98 | 0.93 | 0.82 |
| 150 | 50.65 | 42.36 | 10.32 | 14.24 | 0.85 | 0.92 | |
| 200 | 44.65 | 55.25 | 13.65 | 15.46 | 0.85 | 0.89 | |
| 250 | 16.34 | 60.42 | 23.36 | 29.64 | 0.94 | 0.82 | |
| 300 | 78.93 | 110.31 | 18.55 | 16.32 | 0.95 | 0.83 | |
| 30 | 100 | 65.36 | 77.05 | 17.83 | 12.93 | 0.84 | 0.89 |
| 150 | 96.21 | 66.35 | 16.75 | 20.04 | 0.93 | 0.88 | |
| 200 | 75.62 | 115.21 | 18.32 | 11.37 | 0.85 | 0.86 | |
| 250 | 77.58 | 76.85 | 14.96 | 18.54 | 0.89 | 0.79 | |
| 300 | 88.04 | 87.23 | 18.98 | 19.65 | 0.84 | 0.93 | |
Algorithm III has the shortest anti-collision execution time of less than 1ms, which is because Algorithm III does not construct the structure of a hierarchical tree for the model and calls OBB interference detection during collision detection, which only needs to compare the projections on the 19 separation axes at most to determine the interference situation.
The advantage of Algorithm III is that the computation time is short, and it is suitable for occasions where the collision accuracy requirement is low. The disadvantage of Algorithm III is that it only detects collisions at the level of the bounding box, and there is a large redundancy in replacing the model with the bounding box. Since it does not pick up the collision between models at the basic element level, it is less accurate and is not suitable for scenarios that require high accuracy, such as virtual assembly.
Algorithm II is less sensitive to the relative motion speeds of objects (translational and rotational motions), and its anti-collision execution time is kept in the range of 10~30ms, with better stability of the algorithm. Algorithm II uses the RAPID principle in detecting a collision. The advantage of Algorithm II is that it adopts the structure of the hierarchical tree, which can quickly exclude the disjoint parts, quickly search the collision area, and perform the triangle intersection test to give accurate collision results. Algorithm II is able to deal with the occasions that require high precision, such as virtual robots grasping objects.
The anti-collision execution time of Algorithm I shows an overall increasing trend as the relative motion speed increases. If the relative motion speed is small (less than 10m/s), the time consumption of Algorithm I is smaller than that of Algorithm II. This is because when a collision is detected, the puncture depth between models is smaller, there are fewer triangles in the collision region, and the execution time of the two-by-two interference test between triangles is less than the sum of the time for traversing the hierarchical tree and executing the leaf node triangle intersection test. If the relative motion speed is large (greater than 10m/s), the time consumed by Algorithm I is greater than that of Algorithm II. This is because when a collision occurs, the puncture depth between models is larger, there are more triangles in the collision region, and the two-by-two intersection test between triangles takes too much time. If the piercing depth is further increased, the phenomenon of misalignment of the collision region may occur.
Experiments show that this algorithm takes less time than the OBB hierarchical box method, which has the same accuracy at the triangle level when dealing with minor collisions between models, i.e., when the number of triangles in the collision region is small. The computational accuracy is higher than the OBB bracket box method.
By creating a learning environment of practical training virtual simulation platform and updating the reform practice of fingerprint identification teaching methods in the Criminal Technology class of 2022 students, the questionnaire survey method was used to examine the status of learning Criminal Technology class of 2022 students and 2021 students who extended the traditional teaching methods to learn Criminal Technology class respectively.
In 2023, this paper took two majors and four district teams of 2020 grade and 2021 grades, distributed 300 questionnaires, and retrieved 289 questionnaires and 289 valid questionnaires. The distribution of questionnaires on the learning status of students in the Criminal Technology class is shown in Table 2. A total of 145 valid questionnaires were recovered from the Criminal Investigation class and the Public Security Management class of 2022.
The questionnaire distribution of students’ learning state in criminal technology
| Teaching code | Effective questionnaire | Subordinate major | At the time of this course | Questionnaire statistics |
|---|---|---|---|---|
| 22-XZ-1 | 72 | Criminal investigation | 2022-03-07 | 145 copies (2022) |
| 22-ZA-2 | 73 | Security management | 2022-03-07 | |
| 23-XZ-1 | 72 | Criminal investigation | 2023-03-07 | 144 copies (2023) |
| 23-ZX-2 | 72 | Security management | 2023-03-07 |
Now, based on the questionnaire data, the students of grades 2022 and 2023 are compared and analyzed in terms of learning attitudes, learning strategies, learning outcomes, creative thinking, and reflective evaluation.
The learning outcomes of the Criminal Technology class for students of grades 2022 and 2023 are shown in Table 3.
The goal of the 2022 and 2023 students is “criminal technology.”
| Serial number | Project | Coincidence number | Gap | |
|---|---|---|---|---|
| 2022 | 2023 | |||
| 1 | Basic concepts, knowledge, and principles are well mastered | 112 | 135 | 23 |
| 2 | Excellent test performance | 121 | 143 | 22 |
| 3 | Flexible application of knowledge, analysis of case | 118 | 144 | 26 |
| 4 | The on-site comprehensive examination results are good | 116 | 138 | 22 |
| 5 | Ability to identify and filter information | 124 | 136 | 12 |
| 6 | Can find, ask questions | 113 | 141 | 28 |
| 7 | The ability to solve practical problems and gradually develop the ability to think | 119 | 142 | 23 |
| 8 | Basic skills can be mastered, and the relevant technologies can be completed independently | 124 | 140 | 16 |
| 9 | Flexible use of learning to solve practical problems has a successful career experience | 121 | 139 | 18 |
| 10 | Written examination results are good | 113 | 135 | 22 |
| 11 | Operational skills and theoretical knowledge can be closely related | 115 | 139 | 24 |
The high quality of learning outcomes is the ultimate goal pursued by the learning process and a key indicator of efficient learning. For the quality of the results of the course learning, not only the academic performance reflected in the score, the most critical is the ability to think, analyze, and summarize the problem, which is used to solve the actual problem of the activation of the ability.
For question 6, “Ability to identify and formulate problems,” this item has the largest gap in the number of students meeting the standard, with a difference of 28 students between the classes of 2022 and 2023. Questions 3, 5, 6, 7, 9, and 11 all reflect students’ problem-solving ability from different aspects, and students in grade 2023 have improved their mastery of the criminal technology course after using the virtual simulation platform for practical training in the criminal technology course.
“The so-called learning strategy mainly refers to the rules, methods, and techniques of learning in learning activities in order to achieve certain learning goals. It is an operational process of thinking about problems in learning activities.” It can be considered that learning strategy refers to the rules, methods, techniques, and their regulation for learners to learn effectively in learning activities. It is both an implicit system of rules and an explicit program and steps. This paper specifically refers to the learning strategy of fingerprint identification teaching using the practical training virtual simulation platform.
The components of the fingerprint identification teaching strategy based on the practical training virtual simulation platform are summarized, and it is concluded that the learning strategy includes three parts: cognitive strategy, metacognitive strategy, and resource management strategy. In other words, the learning strategy contains components that directly affect the information processing components of the learning materials, components that affect the information processing process, and components that manage the learning environment, time, and tools. Questions 1, 3, and 4 reflect student cognitive strategies, questions 2 and 6 reflect metacognitive strategies, and question 5 reflect resource management strategies.
A comparison of the fingerprint identification instructional strategies of the students in the classes of 2022 and 2023 is shown in Table 4. Among them, the resource management strategy represented by question 5 improved the most. Only 2 students in grade 2023 did not meet the requirements, and 98.6% of the students were able to use the hands-on virtual simulation platform for fingerprint identification learning.
The design of fingerprint recognition of students in the 2022 and 2023 students
| Serial number | Project | Coincidence number | Gap | |
|---|---|---|---|---|
| 2022 | 2023 | |||
| 1 | To summarize the experience of each experiment | 112 | 137 | 25 |
| 2 | Note the operation points involved in the experiment | 124 | 143 | 19 |
| 3 | Observe and summarize all kinds of experiments | 119 | 140 | 21 |
| 4 | The same electrical classification and a special section of knowledge | 125 | 139 | 14 |
| 5 | With problems, you can seek support from others | 117 | 142 | 25 |
| 6 | Apply the test strategy and review your knowledge | 123 | 140 | 17 |
It can be seen that students’ use of learning strategies increased significantly in learning after the creation of the learning environment. At the same time, the identification and application of learned strategies were practiced in integrated and complex contexts. The strategies were further generalized and analogized by applying them in new contexts. At the same time, attention is paid to the identification of different contexts and the adaptation and differentiation of strategies in special contexts, which is conducive to the gradual internalization of students into their learning strategies.
In the process of teaching criminal technology courses with informatization, the degree of teachers’ informatization teaching ability is shown in Figure 3. For the item “Q1 Criminal technology teachers’ ability to integrate information technology resources”, 32.59% of the criminal technology teachers think that they are very skillful, and 50.28% of the criminal technology teachers think that they are quite skillful. Only 5.61% and 1.13% of the criminal technology teachers considered less skillful and not skillful at all. For the item “Q2 Criminal Technology Teachers’ Informatization Instructional Design Skills”, 70.14% of the Criminal Technology Teachers think they are very skillful. For “Q3 Criminal Technology Teachers’ Informationized Teaching Implementation Ability”, only 0.59% of Criminal Technology Teachers think they are not skilled at all. For “Q4 Criminal Technology Teachers’ Informatized Teaching Evaluation Skills”, a total of 82.66% of Criminal Technology teachers believed that they had mastered the informatized teaching evaluation skills. For the item “Q5 Criminal Technology Teachers’ Informatized Teaching Reflection Skills”, 60.74% and 29.15% of criminal technology teachers thought that they were very skilled and relatively skilled, and 2.35% thought that they were not skilled at all.
The degree of competence of teachers’ information teaching
This study further compares the mean value of the mastery of the criminal technology teachers’ informationalized teaching ability, and the mean value of the mastery of the criminal technology teachers’ informationalized teaching ability is shown in Figure 4.
The means of the teaching ability of criminal technology teachers
Based on the data in the figure, it can be clearly seen that the mean value of the mastery degree of criminal technology teachers’ informatization teaching ability is greater than 4, and the overall level is very good. The mastery of teachers’ informatization resource integration ability, teachers’ informatization teaching design ability, teachers’ informatization teaching implementation ability, teachers’ informatization teaching evaluation ability, and teachers’ informatization reflection ability are all in the middle to high level. Among them, the mastery of teachers’ informalized teaching implementation ability is the highest, with a mean value of 4.7201 and a variance of 4.3479, while the mastery of teachers’ information resource integration ability and teachers’ informalized teaching evaluation ability is relatively low, with a mean value of 4.1253 and 4.0096 respectively, and a variance of 3.0662 and 3.0698.
This paper is based on the development needs of fingerprint identification teaching, combining virtual simulation teaching technology with criminal technology courses to create a fingerprint identification teaching method based on a practical training virtual simulation platform. Analyze the development of criminal technology professional teachers, put forward the teaching, scientific research, and case handling three combinations of criminal technology teachers’ teaching skills to improve the path. Equipped with a virtual simulation teaching experimental environment, two groups of anti-collision algorithms with different real-time and accuracy are selected as a comparison method. The anti-collision execution time of the collision detection algorithm proposed in this paper shows an overall increasing trend as the relative motion speed increases. However, the relative motion speed is small (less than 10m/s), so this paper’s algorithm has a certain advantage in time consumption. The fingerprint identification teaching method based on the practical training virtual simulation platform has obvious advantages over the traditional fingerprint identification teaching method. The teaching class that applies the practical training virtual simulation platform has a higher standard rate and is better able to master the fingerprint identification teaching strategy. Criminal technology teachers who use the teaching skills improvement strategy of combining teaching, scientific research, and case handling have excellent informatization teaching ability and overall level of teaching implementation ability, which can be adapted to the intelligent teaching of criminal science and technology courses.
This paper is a stage of the research results of the 2023 annual college teaching reform project of Shaanxi Police College (project number: YJJG202305).