Interest Of College Students Research Articles

Research on the Impact of Internet Literature Protagonist Role Setting on Moral Education in Colleges and Universities Based on the Perspective of Big Data Technology

Throughout history, literary works have played a significant role in education, network literature has emerged as a prominent form of literature, captivating the interest of college students. The character portrayals in network literature have a profound impact on the formation of their personalities and their approach to daily life. However, it is essential to acknowledge that some network literature works are poorly crafted, resorting to incestuous and pornographic content for popularity, and plagiarism is rampant. Therefore, with the help of big data technology, we should use information collection, information integration, scientific prediction and other data methods to accurately identify and dialectically look at Internet literature, correctly analyze the setting of the protagonist's role in it, and think about the positive and negative impacts it has brought about. The correct guidance of college students in reading network literature should be carried out from the perspectives of the state, online platforms, and educational institutions. By doing so, network literature can become a catalyst for moral education in higher education.

Student Live Behaviour Monitoring in Online Classes Using Artificial Intelligence

The investigators have made many attempts to grab the interest of college students in their studies. The majority of these approximations are based mostly on qualitative review and lack quantitative examination. Thus, the goal of this artistic production is to bridge the gap between quantitative and subjective techniques in order to foster understudy dedication. Therefore, this study regularly uses machine learning techniques (K-manner and SVM) to classify college students into attentive and inattentive RGB-D sensor statistics. The National Academy of Engineering has conducted extensive research on this subject, and the study's conclusions can be utilized to enhance and improve instructional strategies for educators at all levels. One of the main objectives of instructors' job is the capacity to apply tailored learning approaches. This perspective makes use of contraption learning computations for instructive reasons. Keywords: Student Level, Behaviour, Monitoring, Online Class, Artificial Intelligence

Interactive Module Based on QR Code to Increase Learning Interest in Entrepreneurship Course for College Students

This study examined the effect of an interactive module based on a QR Code on increasing the learning interest of college students at Universitas Muhammadiyah Jakarta in Indonesia. This study used the Research and Development (R&D) method to develop an interactive module based on a QR Code product and to test the effectiveness of the resulting product, and the researcher used an experimental form of one group pretest and post-test design. This research and development used the ADDIE model, which consists of 5 stages, analysis, design, development, implementation, and evaluation. The population in this study are college students at Universitas Muhammadiyah Jakarta in Indonesia. This study used simple random sampling with the Slovin formula consisting of 56 college students at Universitas Muhammadiyah Jakarta in Indonesia. In addition, the researcher used the paired sample t-test to test the hypothesis and the Kolmogorov-Smirnov t-test to test normality in this study. This study concludes that the interactive module based on QR codes increases the learning interest of college students at Universitas Muhammadiyah Jakarta in Indonesia.

Determinants Factor of Entrepreneurial Interest Among University Students

The study aims to find out the impact of entrepreneurship education, financial literacy, self-efficiency, and love of money on the entrepreneurial interests of college students. Sampling techniques include accidental sampling and data collection through questionnaires distributed to active students who have undergone matriculation at the university. This examination uses the testing of the measurement model (outer model), the structural model (inner model), and the hypothesis for the analysis of the test. Research shows that entrepreneurship education, self-efficacy, and love of money have a positive influence on student entrepreneurial interests, whereas financial literacy has no effect on student entrepreneurial interests.

Research on the Protection of Labor Rights and Interests of College Students from the Perspective of Employment Expectation

Research on the Protection of Labor Rights and Interests of College Students from the Perspective of Employment Expectation

Research on the legal protection of labor rights and interests of college students in post internship

Research on the legal protection of labor rights and interests of college students in post internship

A statistical study on the similarities and differences of sports interests among college students

Abstract This study focuses on the status quo of group exercise interest differences and similarities among college students, and proposes the interest scalar law of group exercise for the group exercise data of college students, which measures the frequency of group exercise based on the parameter and activity. Based on the interest degree indexes of exercise duration and time, the interest degree model of group exercise is constructed to explore the laws of group exercise behavior. An association rule mining algorithm based on genetic algorithms and interest degrees is proposed, and an improved fitness function is proposed to optimize the algorithm. Taking China’s province S as an example, the statistics on group sports interests of college students show that the central tendency among group sports interests is ball group sports. Among the influencing factors of interest dissimilarity, the gender influencing factor significantly differs in the dimensions of psychological state and peer influence (p<0.05). There were significant differences (p<0.05) and highly significant differences (p<0.01) in the p-values of the place of origin influence factor in the course offerings and psychological state dimensions, respectively. The group sport type influence factor, on the other hand, showed significant differences (p<0.01) in all four sizes, including psychological state and family status.

Research on multi-dimensional evaluation method of sports interest of college students

Abstract This study investigates students’ engagement with sports content, employing a novel method to quantify interest levels through observable physical responses. By analyzing facial expressions and body language, specifically the muscle activities across various body parts, we derive a unique numerical value to represent the intensity of pleasure experienced by viewers in each frame. This approach involves calculating the rotational angle weights between limbs and their movements over consecutive frames, leading to a comprehensive evaluation score for the observed actions. Utilizing the Openpose platform for data analysis, we discovered a significant correlation (r=0.43) between real-time assessment scores and the “sports participation” interest dimension. Analysis of facial expressions in educational sports videos revealed “concentration” as the predominant expression, indicating high student interest. Furthermore, the study identified instances of interest fatigue within an 18-minute duration, notably between frames 32005 and 36507, with a PERCLO value of 0.46, demonstrating the method’s potential in assessing and enhancing engagement with sports education.

Experiential Learning and its Application in College English Intensive Reading Teaching

The teaching of College English intensive reading faces many thorny issues, among which the interest of student is a big one: some articles in the textbook are not that relevant to students' lives and contain grammatical points that students consider boring, therefore they do not have a strong interest. It becomes a big issue how to raise the interest of college students and enhance teaching efficiency. This article applies experiential learning theory and explains how to raise students' self-awareness accordingly, with a case study of Unit 5 Unfreezing Frozen in iEnglish book 1.

A Neural Networks Based Model to Predict the Interest of College Students in Sports Activities

The widespread application of big data technology in various fields, including research in education and sports in colleges, has also been deeply influenced. College students are the future strength of a country, and their habits and interests in sports activities have profound significance for their physical and mental health, teamwork, and outlook on life. However, traditional research methods, such as questionnaire surveys, observations, or interviews, have obvious limitations when dealing with large amounts of complex high-dimensional data. This study aimed to extract the interesting features of college students regarding sports activities using graph neural network (GNN) technology. Then, the labels of those interest features were further predicted, and a feature matrix was constructed. Finally, the K-means clustering method was used to achieve accurate feature clustering. This study presents a novel idea and approach for physical education and event planning in colleges, offering both practical value and theoretical significance.

Research on the Teaching Improvement Strategy of Marxist Classical Original Work Course Based on the Students' Perspective

In order to effectively improve the teaching effect of Marxist classic original works in the new era, better stimulate students' interest in reading Marxist classic original works. This paper is based on the perspective of students, student-based, taking Das Kapital as an example. The survey collected some information about 500 college students reading and studying Das Kapital. Through the cross-analysis of the collected data, the common pain points in the process of learning Das Kapital are analyzed, and the targeted suggestions are put forward based on the specific learning situation, educational elements of the new era, educational situation and curriculum examination situation. It is hoped to stimulate the interest of college students in reading Marxist classics such as Das Kapital, so as to improve the teaching effect of Marxist classic original course.

“Do I need to read to do well?”: a post-pandemic case study of the reading habits and academic attainment of STEM and non-STEM students

Little is known about the reading habits and interests of college students in the post-pandemic environment, especially those of the understudied student populations of the Middle East. Thus, a convenient sample of female undergraduate students of Saudi Arabian descent was asked to report reading habits and interests. First, the study asked whether the responses of students organized by field of study (STEM versus non-STEM) differed. Then, it assessed the extent to which reading habits (as indexing reading comprehension skills) predicted college attainment (as measured by GPA) in each of the selected subsamples. Although habits and attitudes did not differ between STEM and non-STEM students, attitudes predicted reading habits differently for the two groups. Furthermore, habits and attitudes were better predictors of academic attainment for STEM than non-STEM learners. These findings underscored the need for institutions of higher education to carry out periodical assessments of their students’ reading habits and interests. Results can then inform library policies and instructional interventions that will benefit STEM students the most.

Application of decision tree algorithm mining model in career planning goal simulation

Abstract To help college students choose their first career type, the author proposes an application method of a decision tree algorithm mining model in constructing a career planning goal model. The author uses OLAP technology to study and analyze the internal factors such as gender, personality, temperament, and interest of college students, as well as the career type that college students choose for the first time, excavate the potential law between the career type that college students choose for the first time and the internal factors of students, and apply it to the choice of college students’ first career type. The actual results show that the overall accuracy of the mining model is quite high when 50% of the data is used to obtain 50% of the target. The prediction accuracy of the decision tree model in 50% of the data is 47.14%. When the amount of data is 100%, the prediction accuracy of the model reaches 94.29%. The prediction accuracy of the actual decision tree model in 50% of the data is 47.14%. When the amount of data reaches 100%, the prediction accuracy of the model is 68.57%.

Research on the Existing Problems and Countermeasures of "Student Disciplinary Punishment Regulations" in Colleges and Universities

Colleges and universities have the right of independent management according to law. In the aspect of student management, formulate "Disciplinary Punishment Regulations for students", according to which students are rewarded and punished. However, the legal rights and interests of some college students have been infringed due to various problems in the formulation basis, the content of regulations and their implementation which are contrary to the constitution and laws. The fundamental reason is that the concept of education is not correct, the legal system of higher education is not perfect, and the judicial review is absent. In particular, the law based on which colleges and universities formulate the "Regulations on the Punishment of students' discipline Violations" does not clearly define the scope of the affairs of the punishment of students' power, which leads to the confusion of the decentralization of power in the student affairs office of colleges and universities. In this regard, the article puts forward the principle of legal reservation, from the absolute legal reservation, relative legal reservation and no legal reservation to clarify the scope of college students' management power of three aspects, supplemented by judicial review limited involvement, adhere to due process, improve the student punishment institutions, improve the supervision system, and construct a scientific "student punishment regulations for disciplinary violations". The article puts forward the opinion on how to protect the legitimate rights and interests of college students with the right to education as the core and has profound enlightenment.

BAGAIMANA MINAT MAHASISWA TERHADAP PEMBELIAN PRODUK DI MEDIA SOSIAL

This research was conducted by students, this study aims to determine the effect of social media on consumer buying interest. This research was conducted using quantitative data, data collection in this study used a questionnaire containing 10 questions and was distributed to 41 respondents who had made a purchase at any online shop at least once. The results of the study show that there is a very strong and significant correlation between social media and consumer buying interest in college students. 97.6%. Respondents agree that online applications are very helpful in purchasing products. The reason is because it makes it easier for them to find goods without having to leave the house, just by using their cellphone and the online application, they can search for goods and buy them.

The Construction of a Prediction Model for the Teaching Effect of Two Courses Education in Colleges and Universities Based on Machine Learning Algorithms

In this era of machine learning being applied to various fields, its use in college Two courses education has gradually become the next outlet. The reform and innovation based on machine learning is an upgrade that must be carried out in Two courses education. In this experiment, the purpose of predicting the effect of Two courses classroom teaching is achieved by rationally using the Apriori algorithm in the machine learning algorithm to construct a prediction model. After modeling and verification, it has been shown the prediction model of Two courses classroom and extracurricular teaching effect using the machine. Apriori algorithm technology can enhance the participation and interaction of Two courses education, stimulate the interest of college students in Two courses education, and improve their enthusiasm. The selection and basic information of Apriori algorithm and the prediction for practical application have been studied. The accuracy of the prediction model is investigated. Compared with the logistic regression algorithm, it is concluded that the model established by the Apriori algorithm selected in this experiment has higher accuracy. Finally, the prediction model is organically integrated into the Two courses of a university carefully selected by the experiment. After a semester of intensive use, and then algorithm effects, the difference in accuracy is no more than 2%, far better than the expected 6%.

Construction and Application of iWrite Artificial Intelligence Evaluation System for College English Writing

The traditional English writing evaluation is an artificial way to judge, which is a relatively subjective evaluation method. Also, the artificial method has the defects of low timeliness and high error rate, which limits the interest of college students in English learning. If the content of college students’ English writing cannot be judged, it also limits their understanding of the correctness of the English language, because English writing can reflect problems such as English grammar, sentence structure, and emotional expression. Artificial intelligence (AI) technology has developed rapidly, and it can efficiently process the data of research objects. If artificial intelligence technology is combined with English writing evaluation, it can improve the timeliness and error rate of college students’ English evaluation. This is also an innovative way of judging college students’ English writing. Combining the characteristics of great learning English writing and judging criteria, this research uses artificial intelligence technology to design an efficient judging platform. It needs to use a convolutional neural network (CNN) and long-short-term memory (LSTM) neural network to extract features of grammar, sentence patterns, and emotional expressions of college students’ English writing. The research results show that CNN and LSTM methods have high feasibility and accuracy in extracting grammar, sentence patterns, and emotional expressions of college students’ English writing. The prediction error of the college students’ English writing evaluation system is also within a reasonable range. CNN and LSTM methods have high accuracy in predicting English rectangle, grammar, and emotional expression, and the largest prediction error is only 2.91%. Also, the three prediction errors are distributed within 3%.

Optimization of College English Teaching Plan under the Background of Micro-Video and Big Data

English education has gained greater popularity due to the demands of international economy as well as international politics. College English education is also a part of national English education. However, the English education mode in colleges and universities has certain defects, and it still adopts traditional English education methods to impart knowledge to college students. This limits the interest of college students in learning English. The class hours and assessment mode of English education in colleges and universities limit the time for English learning in colleges and universities, which is also one of the reasons for the disadvantages of college English. Today, with the development of information technology, college English education should consider more technological elements to improve quality of English education in colleges and universities. Micro-video technology is a kind of video with interactive and real-time characteristics, which can be free from geographical restrictions. There are also many English education videos in the micro-video technology, which has a richer background of English knowledge and local customs. This research uses micro-video technology to assist English class level in universities. This research also considers big data theory to extract relevant features in micro-video technology to improve level of college English education. The research results show that micro-video technology can improve learning interest of English students in colleges and universities. CNN and LSTM methods can better predict student grades, student performance characteristics, and teacher performance characteristics. The hybrid CNN-LSTM algorithm has higher accuracy in predicting relevant features of college English teaching compared to the single CNN method. This also shows that the micro-video technology has great temporal characteristics in English teaching. This is a friendly study for improving the quality of English teaching.

English Learning Motivation of College Students Based on probability Distribution

Abstract In order to systematically study the motivation tendency of college students in English learning, researchers in various countries have used a variety of test scales to conduct a questionnaire survey on college students on the basis of understanding the current situation of practical language knowledge learning. The final results show that motivation, as a major factor affecting successful language learning, can not only arouse the interest of college students in learning, but also improve their learning efficiency in practice and development.Therefore, after understanding the bucket sorting algorithm of probability distribution, this paper, according to the types and research status of English learning motivation, uses a questionnaire to select a college student to conduct a survey on English learning motivation, so as to provide effective basis for practical education management.

A Brief Analysis of the Legal subject Qualification and Rights and Interests Protection Countermeasures of University Students' Part-time Jobs from the Perspective of Labor Law

At present, college students part-time is a kind of common social phenomenon, widespread law, but because of the lag of the law, part-time college students laborer legal subject qualification is not clear, with the unit of choosing and employ person labor relations between fuzzy, eventually led to the part-time labor rights and interests by different levels of college students, and because of the absence of the law, their basic rights and interests can not be effectively guaranteed. How to solve a series of problems caused by it. This paper analyzes and explains the current legal context from the perspective of legal theory, to make the legal subject qualification of punishing students for part-time jobs more clear and closer to the scope of "Labor Law". To effectively protect the legitimate rights and interests of college students, it puts forward ideas and suggestions from the legal level.