Intelligence Course Research Articles

Online Intelligent Course Education Based on Grid Model Simplification.

In order to explore the teaching efficiency of online intelligent courses and improve the quality of online teaching, this paper builds a classroom intelligent auxiliary management model based on the grid simplification method. Moreover, this paper formulates corresponding teaching strategies through the recognition of student state features, uses a target detector to detect all detection targets from the scene, and then counts the number of detection targets, identifies specific individuals, and judges the individual state. Simultaneously, this paper intercepts candidate subregions from the scene image and then inputs the subregion image to the detector to determine whether the candidate region image is a detection target and formulate corresponding countermeasures. In addition, on the basis of the existing 3D mesh model stitching and editing method, this paper proposes a grid splicing and fusion method based on the idea of partial reuse of the model to calculate the result of the target. Finally, this paper designs experiments to verify the performance of the model. The research results show that the model constructed in this paper is effective.

Probabilistic Inference and Dynamic Programming: A Unified Approach to Multi-Agent Autonomous Coordination in Complex and Uncertain Environments

We present a unified approach to multi-agent autonomous coordination in complex and uncertain environments, using path planning as a problem context. We start by posing the problem on a probabilistic factor graph, showing how various path planning algorithms can be translated into specific message composition rules. This unified approach provides a very general framework that, in addition to including standard algorithms (such as sum-product, max-product, dynamic programming and mixed Reward/Entropy criteria-based algorithms), expands the design options for smoother or sharper distributions (resulting in a generalized sum/max-product algorithm, a smooth dynamic programming algorithm and a modified versions of the reward/entropy recursions). The main purpose of this contribution is to extend this framework to a multi-agent system, which by its nature defines a totally different context. Indeed, when there are interdependencies among the key elements of a hybrid team (such as goals, changing mission environment, assets and threats/obstacles/constraints), interactive optimization algorithms should provide the tools for producing intelligent courses of action that are congruent with and overcome bounded rationality and cognitive biases inherent in human decision-making. Our work, using path planning as a domain of application, seeks to make progress towards this aim by providing a scientifically rigorous algorithmic framework for proactive agent autonomy.

Feedback Delay of Sports Intelligent Learning System Based on Model Predictive Control.

In this paper, a trajectory tracking controller based on linear time-varying model prediction is developed, and the model predictive control theory based on the six degrees of freedom dynamic model and tire model is applied. Combined with the soft constraint of turning angle and the control algorithm to ensure the stability, the trajectory tracking is realized. The terminal node is a wearable device. The terminal node is equipped with a pressure sensor and attitude sensor network to collect human data, and then the terminal node sends the data to the ZigBee network. The data in the sensor network will be received by the gateway, and the data will be processed and displayed on the PC software after being received by the gateway. Finally, the data is saved to the remote server for archiving. The application of intelligent learning systems in sports makes up for many shortcomings of traditional sports. The teaching of the course consists of seven types of intelligent courses, which not only conform to the spirit of high-quality education but also combine the shortcomings of traditional physical education learning and combines several intelligent theories and the need for time development. The purpose is to develop a new sports model suitable for students' all-round development. Under the guidance of the theory of intelligent learning system, this paper establishes a new and diverse movement model, including various educational models, educational contents, educational methods, student-based learning model, and multidimensional evaluation. By comparing and analyzing the results of the two groups, the multiple intelligences physical education is more suitable for the development of modern students' various intelligences. According to the physical education learning questionnaire, students also learn more methods and contents and accept diversified teaching evaluation, which expands the development direction of students.

Research on the Impacts of Cognitive Style and Computational Thinking on College Students in a Visual Artificial Intelligence Course.

Visual programming language is a crucial part of learning programming. On this basis, it is essential to use visual programming to lower the learning threshold for students to learn about artificial intelligence (AI) to meet current demands in higher education. Therefore, a 3-h AI course with an RGB-to-HSL learning task was implemented; the results of which were used to analyze university students from two different disciplines. Valid data were collected for 65 students (55 men, 10 women) in the Science (Sci)-student group and 39 students (20 men, 19 women) in the Humanities (Hum)-student group. Independent sample t-tests were conducted to analyze the difference between cognitive styles and computational thinking. No significant differences in either cognitive style or computational thinking ability were found after the AI course, indicating that taking visual AI courses lowers the learning threshold for students and makes it possible for them to take more difficult AI courses, which in turn effectively helping them acquire AI knowledge, which is crucial for cultivating talent in the field of AI.

A survey on multimodal-guided visual content synthesis

With the increasing interest in various creative scenes such as social media, film production, and intelligence courses, people expect to be able to compile rich visual content according to their subjective ideas and actual needs. In this context, visual content synthesis technique based on multimodal data has attracted much attention in recent years. Compared to traditional generative methods, multimodal data offer more flexible and concrete clues that provide an interactive and controllable way to generate the desired visual content. In this survey, we comprehensively summarize the improvements in multimodal-guided visual content synthesis. We first formulate the taxonomy of visual content synthesis and divide it into four different subfields depending on the input modality, including visual-guided visual content synthesis, text-guided visual content synthesis, audio-guided visual content synthesis, and visual content synthesis guided by other modalities. In each subfield, we describe the paradigm of different modality-guided visual content synthesis, and also discuss the signature methods mainly based on Generative Adversarial Networks (GANs). Next, we present commonly used benchmark datasets and metrics for evaluating models, as well as detailed comparisons between different methods. Finally, we provide insight into current research challenges and possible future research directions.

Intelligent course recommendation based on neural network for innovation and entrepreneurship education of college students

This paper focuses on intelligence course recommendations for college students’ innovation and entrepreneurship education. Firstly, the traditional collaborative filtering algorithm was introduced, and then a new recommendation technique was designed based on an artificial neural network (ANN). The experimental data were collected through a crawler framework. The two methods were compared and analyzed. It was found that the training time of collaborative filtering and ANN was 16.78 s and 12.36 s, the testing time was 2.64 s and 2.12 s, the HR values were 0.6078 and 0.6264, and the normalized discounted cumulative gain (NDCG) values were 0.2948 and 0.3356, respectively. The results reveal that ANN was more efficient in computation and better in recommendations. The results demonstrate the effectiveness of the ANN method for intelligent course recommendations. The method can be applied to the selection of innovation and entrepreneurship education courses for college students.

An Internet Articles Retrieval Agent Combined With Dynamic Associative Concept Maps to Implement Online Learning in an Artificial Intelligence Course

Online learning has been widely discussed in education research, and open educational resources have become an increasingly popular way to help learners acquire knowledge. However, these resources contain massive amounts of information, making it difficult for learners to identify Web articles that refer to computer science knowledge. This study developed an Internet articles retrieval agent combined with dynamic associative concept maps (DACMs). The system used text mining technology to analyze keywords to filter computer science articles. In previous research, concept maps were manually constructed; in this study, such maps can be automatically and dynamically generated in real time. In a case study of a fundamental course of artificial intelligence, this study designed two experiments to compare students’ learning behaviors while using this system and the Google search engine. The results of the first experiment showed that the experimental group searched for more knowledge articles on computer science using this agent, compared to the control group using the Google search engine. The learning performance of the experimental group was significantly better than that of the control group, while the cognitive load of the experimental group was significantly lower than that of the control group. Furthermore, the results of the second experiment showed that the learning progress of students using the agent was significantly greater than that of students who used the Google search engine. This illustrates that the agent effectively filtered computer science articles, and DACMs helped students gain a deeper understanding of academic concepts and knowledge related to artificial intelligence.

Conformance Analysis of Student Activities to Evaluate Implementation of Outcome-Based Education in Early of Pandemic using Process Mining

The learning management system has a core component of a system event log that contains data on activities carried out by students and lecturers in the system. Educational process mining is a field in educational data mining that is concerned with finding, analyzing, and improving the overall educational process based on information hidden in educational data sets and event logs. The learning process in student lectures through the learning management system will produce a process flow according to the event data. In one semester in the information technology-based study program, the subjects taken are data from programming and non-programming courses in the 5th semester of the information systems department, namely Data Warehouse and Business Intelligence (DWBI) and Enterprise Architecture (EA). The Data Warehouse and Business Intelligence course is chosen because the main role in the graduate profile is as a data engineer. While the Enterprise Architecture course is chosen because being an IT Consultant requires knowledge of EA. Each course has different measured learning outcomes and each course has a different pattern in obtaining learning outcomes. To get a pattern for each learning achievement, an analysis of learning patterns, Bloom’s taxonomy level, and CLO pass scores was carried out using process mining. Course Learning Outcomes (CLO) is a competency standard or minimum qualification criteria for graduates’ abilities which include attitudes, knowledge, and skills assigned to courses. The existence of a bloom level indicates the level of expected learning achievement, where the higher the bloom level, the higher the expected ability. The mining process is carried out using Disco and PROM 5.2. The modeling process uses a heuristic miner algorithm because of its ability to express the main behavior recorded in the event log well. Heuristic miner algorithm can also take into account the frequency of the relationship between activities in the log to determine causal dependencies. The results of this study indicate that there is a difference between those that pass the course learning outcomes and those that do not pass. The passed CLO is indicated by the realization value of passing the course exceeding the threshold of 85.50%, while the failed CLO is indicated by the realization value of course graduation that is less than the threshold. In addition, control-flow, the frequency of activities that are often carried out indicate the appropriate learning path and are carried out by students to achieve a minimal assessment of course learning outcomes. In the Enterprise Architecture course, the activity that has the highest frequency in CLO1 is Attempt Quiz, while in CLO6 is View Course. In the Data Warehouse and Business Intelligence course, the activity that has the highest frequency in CLO3 is View Course, while in CLO4 is Attempt Quiz. The initial activity of the learning pattern produced in the two courses begins always with the view course activity. The highest bloom level in the Data Warehouse and Business Intelligence course is C6 Creation, while in the Enterprise Architecture course is C5 Evaluation. Thus, it can be said that Data Warehouse and Business Intelligence courses have a higher level of difficulty than Enterprise Architecture. Previously, in the DWBI course there was one CLO that failed in its implementation. With this research, it is hoped that this research can have a positive impact on adding new insights regarding the use of event logs in the field of education, so implementation of outcome-based education can be used as a benchmark for student learning to succeed in the course which include attitudes, knowledge, and skills.

On the Teaching Mode of Artificial Intelligence General Course for Non-Computer Majors in the Vocational Higher Education Institution

Artificial intelligence general course is a general course set up by most higher vocational colleges at present. This subject focuses on the teaching of integrating theory with practice. The main teaching goal of this course is to focus on cultivating students' computer logical thinking ability and improving the application ability of artificial intelligence in their respective majors. However, it is more difficult for non-computer majors. To further consolidate the educational effect of artificial intelligence courses in general higher vocational colleges, it is necessary to design the courses as a whole and integrate each link in the teaching process reasonably. This paper focuses on the teaching reform of artificial intelligence general courses and puts forward the current situation and main problems of artificial intelligence general teaching in higher vocational colleges for non-computer majors. It also explored the main ideas of artificial intelligence course content construction, and expounded on the modular construction content of the new generation of artificial intelligence teaching course systems, hoping to give some help to relevant personnel.

Research on the Dissemination and Influencing Factors of Big Data and Artificial Intelligence Related Courses in Colleges and Universities-Taking MOOC as an Example

The rapid development of information technologies such as artificial intelligence, Internet and big data has promoted the deep integration of technology and education, especially the rise of large-scale online courses, which provides a great opportunity for curriculum teaching reform in colleges and universities. At the same time, artificial intelligence, as a cutting-edge technology, has good development prospects and has become a popular professional course in colleges and universities, artificial intelligence technology has become the focus of subject education in many universities. The combination of online education and AI courses will also greatly enhance the enthusiasm of users and expand the dissemination scope of big data and AI related courses. Based on the grounded theory and technology acceptance model, this paper obtains the users’ perception evaluation of learning MOOC big data and artificial intelligence courses through field interviews, summarizes the core categories, constructs MOOC user acceptance model, collects sample data by questionnaire and makes an empirical study. It is found that perceived usefulness, expectation confirmation and primary communication effect have a significant positive impact on the secondary communication effect, and the content quality and socialization interaction indirectly affect the secondary communication effect of MOOC curriculum by affecting the primary communication effect.

Design and Implementation of Intelligent Course Scheduling System for Deep Integration of Education and Teaching

With the development of higher education and the continuous increase of the number of students, under the premise of limited classroom resources, the curriculum arrangement is more onerous. Course scheduling is one of the very important and complex management work in school teaching management. In this paper, parallel genetic algorithm is applied to solve the course scheduling problem, and a course scheduling system based on parallel genetic algorithm is designed and implemented, which can reflect the actual course scheduling situation to a large extent and achieve the optimization of multiple objectives as much as possible, so as to improve the efficiency of course scheduling.

Implementation of artificial intelligence development strategy in the Russian Federation in the educational programs of aerospace engineering training of Bauman Moscow State Technical University

As a part of the National Strategy on Artificial Intelligence development in the Russian Federation, the specific features of training in USSN 24.00.00 Aviation and Rocket-Space Engineering at “Mechanical Engineering” (SM) Department of Bauman Moscow State Technical University were considered. The analysis of self-established educational standards and the classification of artificial intelligence course modules, implemented at the SM Department, is carried out. Individual courses that already use methods of artificial intelligence or are promising for the introduction of such methods are presented.

Research on the Integration of Information Technology and Ideological and Political Course under the Background of Artificial Intelligence

Under the artificial intelligence environment, the integration of information technology and curriculum teaching is deepening, which provides an important impetus for optimizing the classroom teaching form, integrating high-quality resources and promoting the three-dimensional upgrade of curriculum teaching. In order to realize the ideal value of ideological and political education, it is necessary to construct an intelligent ideological and political course based on the mature application of information technology, fully display the vitality of ideological and political education classroom, in order to achieve the ideal effect of ideological and political education. Through the application of artificial intelligence technology as the research background, systematically explore the integration of information technology and ideological and political courses path.

Novelty in the sphere of health human resources in Sri Lanka

A success story is reported on converting a manual recruitment scheme to a fully automated online scheme by the Human Resources (HR) Coordinating Division of the Ministry of Health in collaboration with the Department of Examinations, Ministry of Education, and the University of Moratuwa. This collaboration has successfully validated the educational qualifications of the applicants online and the procedures have been simplified for the applicants to complete the application within five minutes. The E manual published with details and prospects of courses on offer has greatly enhanced the intelligent course selection. This innovation has been internationally acclaimed and it had helped the Ministry of Health to address the shortage of nursing, professions supplementary to medicine, and paramedical categories in a positive manner.

Research on the Application of Deep Learning in Artificial Intelligence Courses

With the in-depth reform of education, taking students as the center, letting students master the basic knowledge of the theory, but also training students’ practical skills, is an important goal of the current artificial intelligence curriculum teaching reform. As a new learning method, deep learning is applied to the teaching of artificial intelligence courses, which can not only give play to students’ subjective initiative, but also improve the efficiency of students’ classroom learning. In order to explore the specific application of deep learning in the teaching of artificial intelligence courses, this article analyzes the key points of the application of deep learning in artificial intelligence courses. In addition, further explores the application strategies of deep learning in artificial intelligence courses. As it aims to provide some useful references to improve the actual efficiency of artificial intelligence course teaching.

Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation.

Formative feedback has long been recognised as an effective tool for student learning, and researchers have investigated the subject for decades. However, the actual implementation of formative feedback practices is associated with significant challenges because it is highly time-consuming for teachers to analyse students’ behaviours and to formulate and deliver effective feedback and action recommendations to support students’ regulation of learning. This paper proposes a novel approach that employs learning analytics techniques combined with explainable machine learning to provide automatic and intelligent feedback and action recommendations that support student’s self-regulation in a data-driven manner, aiming to improve their performance in courses. Prior studies within the field of learning analytics have predicted students’ performance and have used the prediction status as feedback without explaining the reasons behind the prediction. Our proposed method, which has been developed based on LMS data from a university course, extends this approach by explaining the root causes of the predictions and by automatically providing data-driven intelligent recommendations for action. Based on the proposed explainable machine learning-based approach, a dashboard that provides data-driven feedback and intelligent course action recommendations to students is developed, tested and evaluated. Based on such an evaluation, we identify and discuss the utility and limitations of the developed dashboard. According to the findings of the conducted evaluation, the dashboard improved students’ learning outcomes, assisted them in self-regulation and had a positive effect on their motivation.

Research on College Physical Education Reform Method based on Multiple Intelligences Theory

With the development of the times, people put forward higher and higher requirements for teaching, as well as physical education “Multiple Intelligences Theory" is applied in teaching reform in many fields. Multiple intelligences theory also plays a key role in sports teaching reform and promotes the development of sports teaching reform. The application of Multiple Intelligences Theory in physical education makes up for many shortcomings in traditional physical education. The teaching of eight kinds of intelligent organization courses is not only consistent with the spirit of quality education, but also aims to build a new physical education model suitable for students' comprehensive development in view of the shortcomings of traditional physical education and the needs of the times. Combining the characteristics of physical education teaching and the actual situation of students, it discusses how to apply Multiple Intelligences Theory to cultivate students' comprehensive intelligence in school physical education, in order to provide theoretical reference for physical education teaching reform.

Automatic Grading Tool for Jupyter Notebooks in Artificial Intelligence Courses

Jupyter notebooks provide an interactive programming environment that allows writing code, text, equations, and multimedia resources. They are widely used as a teaching support tool in computer science and engineering courses. However, manual grading programming assignments in Jupyter notebooks is a challenging task, thus using an automatic grader becomes a must. This paper presents UNCode notebook auto-grader, that offers summative and formative feedback instantaneously. It provides instructors with an easy-to-use grader generator within the platform, without having to deploy a new server. Additionally, we report the experience of employing this tool in two artificial intelligence courses: Introduction to Intelligent Systems and Machine Learning. Several programming activities were carried out using the proposed tool. Analysis of students’ interactions with the tool and the students’ perceptions are presented. Results showed that the tool was widely used to evaluate their tasks, as a large number of submissions were performed. Students expressed positive opinions mostly, giving feedback about the auto-grader, highlighting the usefulness of the immediate feedback and the grading code, among other aspects that helped them to solve the activities. Results remarked on the importance of providing clear grading code and formative feedback to help the students to identify errors and correct them.

Estimating the Artificial Intelligence Learning Efficiency for Civil Engineer Education: A Case Study in Taiwan

The civil engineering educators focused on implementing interdisciplinary learning in artificial intelligence (AI) based on a more innovative application of construction requirements. However, only a few pieces of literature discussed the educational learning efficiency and feedback for this trend. Hence, this study surveyed the 237 data from eight universities that issued the interdisciplinary courses. The factors were modified from the scales in science, technology, engineering, and mathematics education. Further, the descriptive analysis was used to explain this situation in Taiwan. A novel approach based on data envelopment analysis and Mahalanobis distance approaches was proposed to solve this problem. The advantages of the proposed approach were discussed and compared with traditional method. Based on the student gains in the interdisciplinary courses, three groups were clustered and compared. The feedback of a high-input and low-efficiency student group was suggested for improving learning strategies. The sensitivity analysis of this special group showed that effective teaching practice is the key factor in the artificial intelligence courses for civil engineering students. These students may increase technical efficiency by 37% by paying 21% inputs. Therefore, this paper provided a useful and easy approach to make learning strategies for non-informatics students in AI learning.

Featured Cover

The cover image is based on the Research Article An intelligent course keeping active disturbance rejection controller based on double deep Q-network for towing system of unpowered cylindrical drilling platform by Yuemin Zheng et al., https://doi.org/10.1002/rnc.5740.