With the rapid development of big data and AI technology, programming is in high demand and has become an essential skill for students. Meanwhile, researchers also focus on boosting the online judging system's guidance ability to reduce students' dropout rates. Previous studies mainly targeted at enhancing learner engagement on online platforms by providing personalized recommendations. However, two significant challenges still need to be addressed in programming: C1) how to recognize complex programming behaviors; C2) how to capture intrinsic learning patterns that align with the actual learning process. To fill these gaps, in this paper, we propose a novel model called Programming Exercise Recommender with Learning Style (PERS), which simulates learners' intricate programming behaviors. Specifically, since programming is an iterative and trial-and-error process, we first introduce a positional encoding and a differentiating module to capture the changes of consecutive code submissions (which addresses C1). To better profile programming behaviors, we extend the Felder-Silverman learning style model, a classical pedagogical theory, to perceive intrinsic programming patterns. Based on this, we align three latent vectors to record and update programming ability, processing style, and understanding style, respectively (which addresses C2). We perform extensive experiments on two real-world datasets to verify the rationality of modeling programming learning styles and the effectiveness of PERS for personalized programming guidance.
The rapid growth of Massive Open Online Courses (MOOCs) has revolutionized the education landscape by providing accessible and flexible learning opportunities. However, with the emergence of hybrid learning models, how to integrate MOOC into traditional classroom environments and effectively utilize MOOC video content in face-to-face teaching has become a current challenge. To solve this problem, we propose an instructive video locating system for MOOC hybrid teaching. The system analyzes teacher instructions and leverages technologies such as speech recognition, text segmentation, and natural language processing to easily locate relevant video clips in MOOC courses, enhancing the blended teaching experience. We conducted experiments using educational videos in MOOC online courses. The results show that the system can accurately locate video clips based on text queries. Compared with manual searches, the accuracy rate exceeds 85
This paper selected an international school in Brunei as a research case, and used the Learning Behaviors Scale (LBS) to analyze the learning behaviors of learners in Chinese learning. According to statistics gathered by questionnaires, this essay analyzed the results via SPSS 22.0, and reported that when learning Chinese as a second language, all the four dimensions of learning motivation, attitude, attention and strategy were positively correlated with the learners’ academic performance; differences in learning duration could lead to those in learning behaviors; differences also occurred in the learning behaviors between male and female students, i.e. boys performed weaker than girls in terms of the above four dimensions; there was a correlation between learners’ language learning behaviors and their family languages, i.e. different family languages had an impact on all the four dimensions of learners’ learning behaviors, among which difference between motivation and strategy was quite big; in addition, differences in religious beliefs did not affect learners’ second language learning behaviors. Based on these research results, the paper advocates that the idea of computer-based education can be introduced to help students establish reasonable Chinese learning behaviors. At the same time, this study can provide several useful tips on international Chinese education integrated with computer technology.
The packet transport network (PTN) is becoming increasingly popular in mobile communication as a technology to provide efficient transmission. With the dramatic surge in user numbers, the PTN must be able to carry more. However, the existing PTN has low resource utilization and poor network security, Therefore, optimizing every aspect of the current PTN is essential. For optimizing PTN network, the decision of both carrier users and service product suppliers should be considered. When satisfying the decision of the product supplier, it is necessary to consider a number of evaluation indicators in the PTN, and there may be some correlation between the indicators. Hence, This study introduces a multi-objective optimization approach inspired by the Gray Wolf Algorithm and centered around the optimization of a PTN network. Carrier user is taken as the upper decision maker, and the goal is to pay the supplier with the lowest possible cost. Suppliers play a lower role in decision-making, considering the PTN Optimal performance, mainly including two goals, the first goal is to have the highest evaluation score of the LSPOR, and troubleshoot the label switched path (LSP) anomaly on the network. As a secondary objective, By maximizing chain bandwidth utilization ratio (CBWUR), we can resolve the problem of high committed information rate (CIR) bandwidth usage. Results show that the model allows services to use shorter paths, which improves resource usage and reduces costs for upper-level decision-makers. At the same time, the model can also improve the evaluation scores of LSPOR and CBWUR indicators, which improves the security of the PTN, reduces the cost of additional network resources, and optimizes the performance of the PTN to the greatest extent. This paper primarily focuses on the following topics:
The online data science learning platform offers students an adaptable educational experience and a versatile learning and experimental environment. However, with the escalation of system concurrency, performance concerns including resource limitations, protracted online experiment response, and platform breakdowns, all of which can have a detrimental effect on the advancement of data science experimental learning. This paper proposes a performance analysis system for online data science learning platforms. It uses the Application Performance Index (Apdex) to evaluate user satisfaction of system performance, and promptly detects real-time performance anomalies and forecasts performance trends by LightGBM and LSTM algorithm. Meanwhile, the performance bottlenecks are also identified and correlated based on the function invocation chains with various time series performance metrics data. Therefore, negative user experience is effectively prevented. Furthermore, we conducted a series of experiments based on 40 days of operational data from a real online data science learning platform, the effectiveness and superiority of the proposed method in this system are clearly verified.
The proliferation of Internet-based technologies has resulted in the availability of new forms of technical assistance for the development of talent. It does this by making education about innovation and entrepreneurship more data-driven, which in turn makes it possible for education about innovation and entrepreneurship to thrive in the modern day. Nevertheless, as the age of big data has emerged in the last few years, new problems have been presented to the process of talent nurturing in colleges and universities. Education in innovative business practices and entrepreneurship in colleges and universities has to be updated to meet the new standards set by the progression of society, the times, and the students’ own personal growth. With this background in mind, the purpose of this article is to conduct an investigation into the present state of affairs regarding the building of an education system for innovation and entrepreneurship at colleges and universities. At addition to this, it explores the connection between big data and the establishment of an innovation and entrepreneurship education environment in schools like colleges and universities. It also suggests a roadmap for the establishment of an ecosystem for the teaching of innovation and entrepreneurship at local colleges and universities based on big data platforms. This serves to encourage the deep integration of big data technology with innovation and entrepreneurship education, which gives theoretical reference value for what has been referred to as the “innovation and entrepreneurship education ecosphere.“
This paper studies and explores the construction method of the integrated curriculum system under the concept of “five integrations”, in which intelligent computing technology helps professional ability improvement. At the same time, the course “Intelligent Computing Fundamentals”, which is located at the underlying logic of the integrated curriculum framework, is constructed, and a teaching path that uses CPBL and P-MASE to achieve “five integrations” and course objectives is proposed.
Artificial intelligence course is an essential fundamental compulsory course for information majors. Current assessment methods of the course lead students to focus solely on their final grades rather than the knowledge itself, failing to provide effective information for students to improve their comprehensive qualities. This paper first analyzes the appropriateness of applying formative evaluation to the artificial intelligence course. Subsequently, it adopts the integrated design concept of “teaching-learning-evaluating’ and a three-step design procedure of “objectives-tasks-evaluation”, using the example of the School of Computer Science and Engineering at Central South University throughout the whole process. Implementing formative evaluation in the artificial intelligence course, promoting evaluation as a means of teaching and an impetus in learning, is conducive to cultivating advanced skills in students, which is an effective approach for nurturing innovative talents in the era of intelligence.
Online practicing platforms and open-source communities have enriched teachers and students with abundant teaching resources and convenient practicing environments. However, the designs of these platforms cannot fully meet the demands of educational scenarios, particularly in programming courses. To address challenges such as code collaboration, homework management, and configuration of practicing environment, this paper introduces an intelligent code hosting and project collaboration platform known as ShuiShan Code Park, which explores a new educational model based on collaboration. Specifically, it introduces team collaboration into teaching, and centers education around repositories. Furthermore, ShuiShan Code Park enhances the educational experience in multiple dimensions including the code similarity comparison, point-based ranking, enhanced Markdown, and Tianhe containers-based practicing. Detailed cases of ShuiShan Code Park's applications effectively demonstrate its optimization of educational scenarios. Feedback from students highlights the practical value of Shuishan Code Park.
The basic point of the dimension of legal system is the institutional factor that restricts the teaching methods of law, and it is also the institutional basis that determines us to adopt specific teaching methods. The idea of taking knowledge as the clue in traditional law teaching does not help to cultivate law students’ ability of “entrepreneurship and innovation” and character building. We should rely on judicial big data and take the “task-driven” teaching mode as the main line to create a “entrepreneurship and innovation” talent training path for law students.
School-to-home streets are an important part of the public space network of child-friendly cities. The improvement of their child-friendliness needs to be based on the investigation and evaluation of the current usage status. The traditional site investigation methods are limited by manpower and time, lack of efficiency and effectiveness. This paper proposes a new analysis framework for children’s street behaviors based on AI recognition methods, including action recognition algorithm, human flow statistic algorithm, and target tracking algorithm. It takes one school-to-home street in the Mingdong community, Ningbo city as a case study. Based on the AI recognition of this street’s surveillance videos, the result data verifies the effectiveness of street usage analysis. This is valuable to promote the child-friendly school-to-home street design in the future.
Given the lack of experimental teaching platforms and tools for integrated circuit (IC) related courses, this paper conducts research on the construction and teaching reform of IC series courses, and transforms the scientific research achievements and academic competition results of Fujian-Taiwan cooperation into virtual reality experimental cases focused on IC layout and routing. Practice shows that transforming the results of scientific research and competition into experiments can stimulate students’ enthusiasm for scientific research and academic competitions in the field of IC, and cultivate students’ abilities to solve problems of IC routing design. Moreover, the time for achievement transformation is effectively shortened, and the circular development of scientific research, subject competition, and teaching is achieved.
The principle of computer composition is an important basic course for undergraduates majoring in computer science, in which the I/O channel control method in the input/output system is mainly applied to mainframes. However, mainframes that support I/O channel design experiments are expensive, complex and costly to operate and maintain, which makes it impossible to carry out hands-on experiments for large-scale students. In this paper, to break the situation that this part of content is mainly taught through textbook in traditional teaching, we design a virtual simulation experiment of computer I/O channel, which is an effective supplement to the practical teaching of computer composition principle. The experimental case has been applied in computer science and technology, information security and software engineering in Tongji University and other colleges and universities, and has been awarded Chinese national first-class course (Virtual Simulation Class). The experimental system has been opened and shared on Chinese national virtual simulation experiment teaching project sharing service platform ( www.ilab-x.com ). The experiment website is http://www.ilab-x.com/details/2020?id=7256 .
Currently, most college students majoring in computer science in China have studied the computer science courses such as C language programming, compiling principles, digital logic circuit, computer organization and system architecture. However, many of them cannot specifically state how a C program runs inside the computer when they graduate. A key reason for this phenomenon is because these courses are taught independently, and lack coherence and continuity with each other. Consequently, the students are lack of computer system capabilities. To solve this problem, we offered a course named "Computer System Fundamentals (RISC-V version)" in Wuhan University of Technology. This course integrates the knowledge from different software and hardware courses so as to improve the students' computer system capabilities. Specifically, this course designs a representative C program example, and then uses this example to investigate how a program runs inside the computer from the high-level application layer to the low-level hardware layer. First, it guides the students to explore how a C program is converted into assembly code; Then, it discusses how the assembly code is translated into machine code; Next, it introduces how the machine code is stored in the memory system; Finally, it investigates how to design a RISC-V CPU to run the machine code in the memory system. This course can help the students understand the correlation between software and hardware, and enhance the ability to analyze a computer problem from a global system perspective. The course has been offered in the computer science major of Wuhan University of Technology for 4 years and has been greatly welcomed by the students. The survey results also show this course has effectively improved the students' computer system capabilities.
With the rapid development of China’s online courses and the national proposal of the task of intelligent education development, how to promote the deep integration between artificial intelligence and higher education, accelerate the higher education from digitization and networking to intelligence, improve the quality of online course construction, and promote the innovation and development of higher education has become an overall, long-term and strategic issue in the field of higher education question. As an important carrier of deep integration between artificial intelligence and higher education, online course knowledge graph is the core influencing factor of the development of intelligent education. In this paper, knowledge graph, an intelligent and efficient way of knowledge organization, is introduced into the teaching of information security introduction course. For the knowledge of “information security” course, a visual knowledge graph is constructed, and then the teaching resource platform of the course is formed.
The concept of “competency-based learning” is proposed in the CC2020 report, which provides a new guidance for the training of computer professionals. This paper first analyzes the role of the concept in the teaching process and puts forward the “3 + 2” competency model. The model updates students’ cognitive structure twice through three teaching processes, so as to attain the three-dimensional competency training covering knowledge, skills and dispositions. Then, under the guidance of this model, the teaching strategy for computer courses is proposed which adopts the way of “Learning Maps”, uses MOOC to realize students ‘personalized learning, uses task-based teaching to realize students ‘participatory learning, and realizes the improvement of students ‘comprehensive literacy through cooperation and discussion. Finally, taking Python Language Programming as an example, two classes of first-year computer majors were selected for comparative experiments. The improvement of students’ competency in the dimension of knowledge and skills was quantitatively analyzed by comparing their scores, and the cultivation effect of students’ competency in the dimension of dispositions was analyzed by interviews. The result showed that the teaching strategy proposed in this paper can improve students’ competency level and get a good teaching effect.
In recent years, with the development of cyber security, the national demand for cybersecurity-related professionals is growing, and the practice of cyber security related practices is increasing, which makes it difficult for students to find suitable practices in the massive practice resources, so the practice recommendations method for students emerges. As an emerging technique, Matrix Completion becomes one of the most powerful techniques to predict missing entries of a low-rank matrix from incomplete samples of its entries. Despite its efficiency in discovering and quantifying the interactions between students and practices, Matrix Completion in the recommender scheme suffers from the problems of low rating density and scalability. To conquer the above challenges as well as to provide quick and high-quality recommendations, we propose partition-based Matrix Completion, a novel recommender scheme that concurrently exploits locality-sensitive hash (LSH) and Matrix Completion. Specifically, taking advantage of the good properties of LSHs, our recommender scheme adopts an LSH hash table to reorder students with similar students buffered in close positions. As a result, the original student-practice rating matrix is partitioned into sub-matrices each containing a group of similar students thus having a lower rank. Matrix Completion is then used in the partitioned matrix to more effectively predict the missing ratings of a student for practice. The experimental results show that our recommendation scheme achieves better recommendation results in terms of practicing recommendation compared to other traditional recommendation methods.
Artificial intelligence (AI) is advancing rapidly and finding its way into various industries. Universities are placing a high priority on incorporating AI into professional education. However, for students who are not majoring in AI, applying AI technology to projects can be challenging. To foster the development of application-oriented talents in the AI era, this article presents an illustrative example of project-based learning that focuses on the use of AI tools for assisted education in the context of blind navigation vehicles. The project specifically concentrates on core AI applications, such as pedestrian detection using Raspberry Pi and line tracking for blind navigation. Voice broadcasting is employed to facilitate interaction with visually impaired individuals. The project is carried out in teams, with an emphasis on utilizing user-friendly AI tools for project-based education and practical experimentation. By integrating theory and practice, students have the opportunity to enhance their practical skills, deepen their understanding of AI, strengthen teamwork, improve communication skills, and foster innovative thinking.
With the construction of new engineering, the development of engineering education has put forward new requirements and challenges for talent training. Since 2017, a personalized talent training model based on the characteristics of disciplines and specialties has beed explored through the innovation and entrepreneurship education. In order to deepen the achievements of the reform, “8+8” whole process education, classified culture, “Five Entries” and all-staff and all-round education have been applied on the reform of undergraduate personalized talent training, which complete the main construction tasks of training program, training mode, growth path and student-centered concept.
The computer fundamentals course is an important course aiming at cultivating students’ computational thinking. In order to improve the course teaching effects, combined PBL, which is an effective instructional approach that can help students to acquire knowledge and to master skills, was employed from 2021’s spring semester to 2023’s spring semester. The role of the teacher has changed from indoctrinator to facilitator. The learning model has shifted from being teacher-centered to a student-centered approach. Teachers carefully prepared various problems, applied new teaching pattern throughout the whole teaching process and gathered students’ reflections. Through these reflections, this model’s teaching effectiveness was verified. On the basis of problem solving, the ability of computational thinking and collaborative learning of students was improved. Furthermore, the conclusion summarizes the main ideas, and further work perspectives.
