Student Learning Behavior Research Articles

Analysis of Literature Education Strategies and Learning Behaviors in the Age of Big Data

Abstract In the contemporary era of big data, the educational landscape is undergoing significant transformations. Literature education, as a vital component, faces both emerging opportunities and challenges. This study develops a framework for analyzing learning behaviors specific to literature education. It employs both an enhanced K-means clustering algorithm and a refined Apriori algorithm for mining data on student learning behaviors. Through cluster analysis and the investigation of association rules, this research explores the interconnections between students’ learning behaviors and their literary education. The findings categorize students into four distinct groups based on their learning behaviors. Students in Category 1 are identified as the most proficient learners, consistently achieving test scores above 85. Conversely, Category 2 students display the least motivation and effectiveness, with their examination scores not exceeding 70. Students in Categories 3 and 4 exhibit comparable levels of performance. Crucially, the analysis reveals that the most significant predictors of students’ literary achievement are their regular and examination scores, with correlation coefficients of 0.627 and 0.653, respectively. This segmentation and analysis of student behaviors facilitate the early detection of atypical learning patterns by educational practitioners, enabling timely intervention strategies to enhance academic outcomes.

The reform and practice path of “curriculum civics” of medical specialties in colleges and universities on the integration of multiple data

Abstract In the context of the new era, reforming “curriculum and political thinking” in the medical specialty of colleges and universities is a key path to realizing the fundamental task of cultivating moral integrity. The study adopts a blended teaching mode, combines online and offline resources, and focuses on analyzing the learning behavior of medical students. The correlation between learning behavior and performance was analyzed by DH-Kmeans cluster analysis algorithm and Pearson’s correlation coefficient. The experimental data came from the online learning behaviors of students in the 2022-2023 academic year of T Medical College, and a total of 2426 data were collected. The results showed that learning behaviors were highly correlated with grades, especially the number of tasks completed, course video progress, and the number of chapter studies. In addition, the comparative analysis of teaching experiments showed that students in the experimental class were better than the control class regarding ideological values, learning attitudes and grades. The conclusion shows that the blended teaching mode effectively improves medical students’ learning effect and the quality of ideological education.

Enhancing Educational Assessment: Predicting and Visualizing Student Performance using EDA and Machine Learning Techniques

Abstract— Educational institutions must manually evaluate student's performance, which requires a significant amount of faculty time and effort. Teachers can easily monitor and document student's learning behavior if they utilize a learning management system (LMS). It can be utilized by coaching sessions or educational institutions to quickly analyze student's performance. Due to the enormous number of pupils, huge data must be analyzed; teachers frequently encounter challenges. The qualities of learners who are students are described analytically in this analysis. In this work, student's cognitive and psychomotor skills are predicted and visualized using Exploratory Data Analysis (EDA) and Machine Learning techniques like KNN and Multiple Regressions. The maximum accuracy of 99% has been obtained in Multiple Regression. Keywords—Analytics; Education; Learning Management System; Performance; Students;

Research on the Informatization Strategy and Practical Effect of Chinese Traditional Culture Education

Abstract The development of curriculum culture is a profound reflection on the evolution of school curriculum in the present era. The article proposes a Retina Student network to detect students’ classroom behaviors and then incorporates the implementation of a classroom behavior coding system using computer vision. Based on this, we established a student detection dataset for the classroom attention recognition task, utilizing the Retina Studen student detection algorithm to achieve optimal recognition accuracy of 0.92 and 0.93, and recall of 0.91 and 0.94 for both gaze and non-gaze states. Then, the visualization information of students’ learning behaviors was targeted, and the classroom status was discussed. The experimental results demonstrate that this paper’s algorithm outperforms other algorithms in identifying student learning behaviors. Finally, through the coding analysis of the teaching video of the cultural education smart classroom, we found that students’ behaviors in the calligraphy, painting, and opera smart classroom are rich, and the traditional way of “explaining-questioning-answering-feedback” is still the main behavior in the classroom.

Artificial Intelligence (AI) in Education: A Case Study on ChatGPT’s Influence on Student Learning Behaviors

Artificial Intelligence (AI) in Education: A Case Study on ChatGPT’s Influence on Student Learning Behaviors

Research on Management Path and Operation Mechanism Construction of Civic Education in Colleges and Universities in the Era of Artificial Intelligence

Abstract Under the background of the artificial intelligence era, the education management style of colleges and universities is moving towards a more intelligent direction. This paper combines the F-S learning style model and the environmental characteristics of online learning to construct an index system of online learning behavioral characteristics for Civic and Political Education in colleges and universities. On this basis, the traditional K-means clustering algorithm is improved based on three-branch decision-making and is used to cluster and divide the learning behaviors of students in the Civic and Political Education courses. At the same time, the degree of interest is introduced to optimize the Apriori algorithm, and the association rules of students’ learning behavior and performance are mined. Then, taking the student learning behavior data of the online course of X Civic and Political Education in colleges and universities as a research sample, TK-means, and Apriori algorithm are used to explore the management path and operation mechanism of Civic and Political Education in colleges and universities. The study shows that among the students in the four clusters, the students with higher task point completion, chapter completion number, and check-in number are the most, accounting for 32%, and the students with a higher course video completion progress, task completion number, and document completion are the least, accounting for 19%. The probability of receiving an ‘Excellent’ grade was 93.9% when students were classified as ‘Visual-Active-High-Commitment’. When students were of the ‘Verbal - Active - passive - High Engagement’ type, there was a 91.3% probability of getting a ‘Medium’ grade. The effectiveness of civics education can be improved by enriching curriculum resources and improving assessment methods.

Analysis and Optimization of Learning Behavior of Music Students in Colleges and Universities Based on Big Data

Abstract As digital education continues to progress, more and more scholars are focusing on the analysis and optimization of big data in the field of education. However, the analysis and optimization of students’ learning behaviors using big data has received less attention. Therefore, this paper uses the improved K-means algorithm to cluster the four aspects of learning, diet, exercise, and consumption behaviors of music majors in College Z. We use the Apriori algorithm to conduct a correlation analysis between the clustered students’ consumption, life, learning, and grades. This analysis summarizes the characteristics of the students’ various behaviors and habits, enabling school administrators to provide effective and reasonable advice to the students. We used the improved K-means algorithm to identify five clustering results related to students’ behaviors. The correlation analysis revealed that 10.98% of the students were regular and hardworking, and there was a 97.78% probability that these students would get “excellent” grades. The majority of students who live a more regular life, spend more time on the Internet and have a low to medium level of consumption have a probability of getting “good” and “medium” grades, which indicates that the results of the big data survey are basically consistent with their actual situation. Obviously, the use of big data can improve the analysis of the correlation between students’ behaviors and grades.

Information Flow and Knowledge Management Strategies in the Digital Transformation of Party History and Party Building Education

Abstract In this paper, a relatively complete digital education management information system is established for the digital basic education resources involved in the field of basic education of Marxist theory to realize the digital transformation of party history and party-building education. Faced with a large amount of student data, the improved new BP model is utilized for integration, cataloging, and prediction. The data elements describing the resources are used as semantic tags to generate XLM documents in a standardized format, which enables the system to exchange data with other resource systems easily. It can be observed that the BP model accurately predicts and analyzes the shared learning behaviors of certain students with a prediction accuracy of 98% upon analysis. The educational management information system proposed in this paper can effectively identify potential risk-takers and categorize learning groups. The indicator of resource-sharing learning behavior has a significant effect on the learning effect of Marxist theory (P<0.05).

Dynamic Analysis of the Influencing Factors of Learning Behavior of Vocational College Students by Large Language Models (LLM)

Dynamic Analysis of the Influencing Factors of Learning Behavior of Vocational College Students by Large Language Models (LLM)

Transformation Research on the Teaching Mode of Ideological and Political Courses in Colleges and Universities under Big Data Environment

Abstract The application of big data technology in the teaching of ideology and politics courses in colleges and universities can provide strong support for the precise reform and innovation of ideology and politics work. In this paper, we first sort out the status quo and dilemma of the teaching of ideology and politics courses in colleges and universities and analyze the advantages and feasibility of big data technology in the learning of ideology and politics. Then, based on the big data streaming computing Spark framework, a personalized learning service platform for teaching Civics and Politics courses in colleges and universities was established. Aiming at the online learning behavior of students on the platform, the K-mean algorithm is used to select their behavioral characteristics. The personalized recommendation of learning resources for Civics and Political Science courses is implemented based on the learning behavior. Data was utilized to analyze the effectiveness of the personalized learning platform in transforming the teaching of Civics and Political Science courses in colleges and universities using S University as the research object. The study found that the number of focused learners in category 1 of the online learning behavior reached 303, and the teaching of the Civic and Political Science course based on the personalized learning platform helped the students’ will cultivation score increase by 2.49 points and more than 95% of the students recognized the platform. The customized service learning platform for teaching Civics courses established based on big data can realize the innovative transformation of Civics courses in colleges and universities and improve the teaching quality and level of Civics courses in colleges and universities.

Exploring the Teaching Reform Path of Ideological and Political Education in Colleges and Universities in the Context of Deep Learning

Abstract Deep learning significantly enhances the comprehension of ideological and political education (IPE) concepts and facilitates the advancement of IPE teaching in colleges and universities through bilinear blended teaching reforms. This innovative teaching approach employs association rules from big data and an enhanced Apriori algorithm to analyze students’ online learning data. An analytical model of student learning behaviors in IPE, utilizing a Support Vector Machine (SVM) for learner classification, forms the basis for developing an online learner profile. This profile integrates assessments of learning outcomes, group identification, and style classification to tailor learning activities to individual needs. The efficacy of this reform was tested over one year, comparing the impacts of IPE across four different regions. Results indicate that students in the experimental group significantly outperformed their counterparts in the control group. For instance, in Shanghai, scores from the experimental group averaged 89.63 compared to 83.25 in the control group, yielding a significant difference of 6.38 points. Statistical analysis showed a t-value of 6.21 and a p-value of 0.000, affirming the positive impact of the bilinear blended teaching reform on enhancing student ideological engagement and promoting educational reform in colleges.

Design of Customized Teaching Strategies Based on User Behavior Analysis in the Digital Transformation of Health Education

Abstract With the greater popularity of the Internet, on the one hand, online education platforms have flourished and education informatization has entered a new era, with massive learning behavior data generated by learners in different learning platforms. This paper constructs a portrait of student learning behavior based on the characteristics of their learning behavior in the health education class. Based on the clustering analysis method in the big data mining algorithm, the user’s behavioral characteristics are analyzed. On this basis, the algorithm is improved by using collaborative filtering personalized recommendation and introducing the LDA model, and a customized teaching model is constructed in the context of health education, and its application effect is explored. From the four dimensions of “course completion characteristics”, “teaching interaction characteristics”, “learning input characteristics,” and “learning achievement characteristics”, we analyzed different groups of students and investigated the effect of its application. “The behavioral characteristics of different groups of students were analyzed, and it was concluded that Group A and Group B performed better, but Group C accounted for a higher percentage of 24%. Finally, according to the collaborative filtering-based digital customized teaching on the students’ health education test scores for the pre and post-test, it was concluded that the average scores of the experimental subjects increased after the post-test. The mean scores for the third post-test were 4.27, 4.44, and 4.35, respectively. It can be concluded that the customized teaching model has a significant improvement in the students’ classroom teaching effectiveness.

Construction of Basic Teaching Organization Construction Model under the background of Medical Practitioner qualification Examination in China

The medical practitioner qualification examination serves as an admittance standard to assess the ability of medical graduates to practice medicine in China. Ensuring the quality of medical professionals and selecting talents fairly requires improving the teaching level of medical universities. This paper studies the key concepts, major theories of basic teaching organizations, and investigates the current situation of basic teaching organizations in medical colleges and universities. Intervention measures were taken based on the actual situation of the schools to observe the effectiveness of basic teaching organization and the exam-taking ability of licensed doctors. By utilizing management by objectives (MBO) and learning organization theories, the study constructs a learning basic teaching organization model. This model aims to enhance lecturers' enthusiasm for teaching, teaching ability, and teaching quality while improving the learning attitude, behavior, and exam-taking ability of medical students, the ultimate goal is to increase the passing rate of medical students.

Research on the Impact of Elective Subjects of College Entrance Examination on Talent Cultivation of Higher Vocational Colleges Based on Students’ Cognitive Behavioral Characteristics

Abstract The reform of the optional subjects of the college entrance examination under the new situation puts forward higher challenges to the talent cultivation of higher vocational colleges. In this paper, for students entering higher vocational colleges and universities after the new college entrance examination, the random forest framework is improved by mining students’ homework and quiz data with PCA dimensionality reduction. Behavioral feature extraction using Random Forest and coding definitions are proposed after feature screening. Based on general feature coding, the behavioral group differentiation extraction of higher education students was constructed, which finally provided label classification of students’ cognitive-behavioral features. After analyzing the impact of the optional subjects on the quality of higher vocational students, the learning behaviors of higher vocational students were dissected. The probability density distribution plots of students’ entry rates show that the overall probability density distribution curves of entry for different students are significantly different. The wave heights of the first, second, third and fourth academic years were 10.25, 16.48, 13.24 and 12.61, respectively, and the study behavior of senior students after the selection of subjects in the college entrance examination showed a unique pattern. In response to the behavioral performance of higher vocational students, institutions need to scientifically use assessment reports to launch talent development work.

The Performance of Primary School Students in English Learning in the Online Teaching Mode

Since around 2000, many online courses have appeared on the market. Depending on the needs and groups of people attending the class, there are many ways to take the course. Among all the class groups, this article focuses on analyzing the behavior of elementary school students in English lessons. By interviewing the parents of seven ordinary Chinese primary school students and the teachers of the online courses, this article hopes to further reveal the learning behavior of primary school students in ordinary families and the expectations of parents for the curriculum. Online English classes for elementary school students have unexpected demands on the learning atmosphere in the classroom, and in the primary school classroom, the interaction between teachers and students is particularly important.

Penerapan Teori Belajar Behaviorisme dan Teori Belajar Sosial Bandura dalam Pembelajaran

This research focuses on the Application of Behaviorism Learning Theory and Bandura's Social Learning Theory in Learning. The purpose of this research is to reveal behaviorism learning theory and Albert Bandura's social learning theory and implement them in learning. The content of behaviorism learning theory is that learning is a change in behavior as a result of the interaction between the stimulus as a result of the interaction between stimulus and response. The content of social learning theory emphasizes direct experience or observation, because according to Albert Bandura, namely people learn from what they read, hear, and see in the media, and also from other people and their environment. environment. The method used in the research is a qualitative method that intends to understand the object of research with sophisticated efforts through the art of speaking, the art of writing and the art of presenting what has been researched. In the study there are results, namely social learning theory Social learning theory helps understand the occurrence of aggression and psychological deviations and how to modify behavior. And the result of applying behavioristic theory and Bandura's social theory is the formation of a behavior that is aggression. This is the formation of a desired behavior. The desired behavior gets positive reinforcement and inappropriate behavior gets negative rewards and learner behavior. The learning behavior of students is the result of the ability of students to interpret knowledge or information, interpret a model that is not appropriate for them. information, interpret a model that is imitated, then process cognitively and determine actions according to the desired goals. determine the action according to the desired goal.

Learning Motivation of English Based on Sociocultural Theory: A Study of Chinese College Students in English Majors

Sociocultural theory emphasizes the use of sociocultural contexts and cultural artifacts to regulate interpersonal relationships and facilitate thinking and language acquisition. Based on this theory, research on foreign language learning has shown that learning a foreign language is not a personal effort invested blindly, but rather is realized and internalized through language as a cognitive tool with the help of other supports. Learning motivation is an important factor that directly affects the learning behavior and effect of college students, and it is the internal motivation and need that drives students to learn. To understand the current situation of learning motivation of Chinese English majors under the sociocultural theory, this study adopts a questionnaire to investigate the learning motivation status of 150 students majoring in English in a comprehensive undergraduate university. Based on the problems found, this study makes suggestions to both students and teachers to stimulate students learning motivation.

Generating personalised profiles of student engagement to predict student performance and support student learning using LMS data

With the rising popularity of delivering educational content in the online space, this study set out to better understand how students are increasingly engaging with Learning Management Systems (LMSs) for their learning (Gupta, Muralidharan, & Raghavan, 2021; Henrie, Halverson, & Graham, 2018; Conijn, Snijders, Kleingeld, & Matzat, 2017). A data mining approach was used to investigate whether student activity data from LMSs could be used, first in generating profiles of student engagement and, second, in predicting student academic performance (Romero & Ventura, 2013; Tempelaar, Rienties, & Giesbers, 2015; Conijn et al., 2017). LMS activity data was collected throughout a semester for a cohort of undergraduate students (N=534) enrolled in a biomedical science subject. This activity data comprised of total views and downloads of lecture recordings and average time spent viewing lecture recordings, total number of page clicks, total number of LMS course and content page clicks (e.g., lecture lessons), total number of discussion forum posts and views, and total number of formative quiz attempts and reviews (Wang, Chen, & Anderson, 2019; Conijn et al., 2017). K-means cluster analysis was performed to generate profiles of student engagement using this digital LMS data. Measures of student activity derived from this digital LMS data were used to predict academic performance (students' final unit totals), using linear regression and ANOVA. We demonstrate the utility of LMS log data in constructing 'profiles' of student engagement, and in establishing predictors of academic performance. In line with existing literature, average time spent viewing lecture recordings was determined to be a significant predictor of academic performance. Clustering analyses revealed three distinct clusters, or ‘profiles’ of student engagement, each representing key differences in student engagement activity, with greater homogeneity in student engagement behaviour within clusters, and greater heterogeneity across clusters. The current findings provide valuable insights on how our students are increasingly engaging with LMSs for their learning. As delivery of education towards the online space continues to gain momentum, evidence-based approaches such as those utilized in this study will help inform and enhance teaching practices (Jovanovi?, Gaševi?, Pardo, & Dawson, 2020; Ullmann, Wild, & Scott, 2018; Conijn et al., 2017). A better understanding of how students are differentially engaging with LMSs will also help support the development of teaching resources that more closely mirror and complement online student learning behaviors (Dyckhoff, Zielke, Bültmann, Chatti, & Schroeder, 2013; Conijn et al., 2017).

Determinants of College Students’ Online Fragmented Learning Effect: An Analysis of Teaching Courses on Scientific Research Software on the Bilibili Platform

In the era of mobile Internet, online fragmented learning has become one of the mainstream ways for college students to study independently. Analyzing the online fragmented learning effect (OFLE) has essential reference value for guiding the online learning behavior of college students and optimizing the allocation of online learning platform resources. It also provides sustainable solutions for enhancing college students’ online independent learning effect. For this paper, we took college students who have studied teaching courses on scientific research software (TCSRS) on Bilibili as our research subjects, constructed a theoretical model of college students’ OFLE based on the theory of online learning and fragmented learning, verified the model through using questionnaire data, and analyzed the determinants of college students’ OFLE and its mechanisms. Our results indicate the following: (1) Most college students spend a dispersed amount of time learning TCSRS, showing the characteristics of “fragmentation”. (2) Learning motivation (LM), self-efficacy (SE), and fragmented time utilization (FTU) have a significant positive effect on college students’ OFLE. (3) Knowledge fragmentation (KF) affects college students’ OFLE, but it is not significant. These dimensions provide a theoretical reference for quantitative research on the impact of college students’ OFLE. Finally, also in this paper, improvement countermeasures are proposed from the perspective of improving college students’ OFLE and ability.

Based on the application of contextual teaching method in the rule of law education strategy and the cultivation of college students’ awareness of the rule of law

Abstract This paper clarifies the relationship between the rule of law education strategy, college students’ legal awareness, and contextual pedagogy and proposes that the teaching decision of contextual pedagogy based on reinforcement learning cultivates college students’ legal awareness. Based on the contextual pedagogy of the rule of law education, it puts forward the teaching strategy of the rule of law in the contextual display from students’ and teachers’ perspectives. Q-learning, two-stage separation and single-stage solving optimal decision function are selected to analyze the learning behavior of college students using the teaching decision of the rule of law education with reinforcement learning, and the analysis of college students’ law consciousness is subdivided into right consciousness and obligation consciousness, rule consciousness and procedure consciousness. College students’ awareness of rights scored 14.56±1.96, awareness of obligations scored 13.06±1.45, awareness of rules scored 18.63±1.85, and awareness of procedures scored 15.26±2.02, which shows that college student’s awareness of the law gradually awakening.