Learning Behavior Analysis Research Articles

OP002 Topic: AS16–Medical, Nursing and AHP Workforce & Education: Staff Education and Development/Equity, Diversity, Inclusion/Other: INSIGHTS INTO PEDIATRIC MECHANICAL VENTILATION SIMULATION TRAINING: A DATA-DRIVEN ANALYSIS OF LEARNING PATTERNS AND BEHAVIORS OF RESIDENTS

OP002 Topic: AS16–Medical, Nursing and AHP Workforce & Education: Staff Education and Development/Equity, Diversity, Inclusion/Other: INSIGHTS INTO PEDIATRIC MECHANICAL VENTILATION SIMULATION TRAINING: A DATA-DRIVEN ANALYSIS OF LEARNING PATTERNS AND BEHAVIORS OF RESIDENTS

Examining self-regulation models of programming students in visual environments: A bottom-up analysis of learning behaviour

Examining self-regulation models of programming students in visual environments: A bottom-up analysis of learning behaviour

Analysis of Students' Learning Behavior Based on Clustering Algorithms

Analysis of Students' Learning Behavior Based on Clustering Algorithms

The crowd in MOOCs: a study of learning patterns at scale

ABSTRACT The increasing availability of learning activity data in Massive Open Online Courses (MOOCs) enables us to conduct a large-scale analysis of learners' learning behavior. In this paper, we analyze a dataset of 351 million learning activities from 0.8 million unique learners enrolled in over 1.6 thousand courses within two years. Specifically, we mine and identify the learning patterns of the crowd from both temporal and course enrollment perspectives leveraging mutual information theory and sequential pattern mining methods. From the temporal perspective, we find that the time intervals between consecutive learning activities of learners exhibit a mix of power-law and periodic cosine function distribution. By qualifying the relationship between course pairs, we observe that the most frequently co-enrolled courses usually fall in the same category or the same university. We demonstrate these findings can facilitate manifold applications including recommendation tasks on courses. A simple recommendation model utilizing the course enrollment patterns is competitive to the baselines with 200× faster training time.

Research on the Application of Big Data and Cloud Computing Technology in Smart Campus

The current education field is experiencing an innovation driven by big data and cloud technologies, and these advanced technologies play a central role in the construction of smart campuses. Big data technology has a wide range of applications in student learning behavior analysis, teaching resource management, campus safety monitoring, and decision support, which improves the quality of education and management efficiency. Cloud computing technology supports the integration, distribution, and optimal use of educational resources through cloud resource sharing, virtual classrooms, intelligent campus management systems, and Infrastructure-as-a-Service (IaaS) models, which reduce costs and increase flexibility. This paper comprehensively discusses the practical application of big data and cloud computing technologies in smart campuses, showing how these technologies can contribute to the development of smart campuses, and laying the foundation for the future innovation of education models.

Learning Behavior Analysis and Prediction Based on Convolutional Neural Networks

With the development of information technology and the change of educational needs, personalized learning and intelligent education have become an important direction of current educational research. Traditional educational assessment and teaching methods are limited by subjectivity and limitations, and it is difficult to fully analyze and understand learners individual needs and behavior patterns. The analysis and prediction method of learning behavior based on convolutional neural networks provides new tools and perspectives for educational decision makers and teachers through automated and data-driven methods, which is expected to deepen the understanding of the laws behind learners behaviors and achieve the goal of education personalization and effect optimization. This paper will discuss the application status and future development direction of CNN in learning behavior analysis, and contribute new theoretical and practical experience to the innovation and progress of educational technology.

Analysis of Student Learning Behavior using Process Mining and Spectrogram

Analysis of Student Learning Behavior using Process Mining and Spectrogram

Investigating behavioral and cognitive patterns among high-performers and low-performers in Co-viewing video lectures

Co-viewing video lectures, where students watch lectures simultaneously with one or more remote peers and engage in interpersonal communication on learning tasks online, is becoming increasingly popular. However, there is a dearth of studies examining students' behavioral and cognitive patterns during the co-viewing process. This study employed eye-tracking and screen recording methods to examine undergraduate students' learning processes in co-viewing video lectures. It specifically focused on how students used video lectures and engaged in discussions to complete a design task. Lag sequential analysis of learning behaviors revealed that, compared to low-performers, high-performers were more engaged in behavioral sequences involving clarifying learning tasks. Furthermore, epistemic network analysis of conversations revealed that high-performers demonstrated stronger connections between deep-level knowledge and a high level of uptake in design skills. In contrast, low-performers exhibited more connections between superficial-level knowledge and a low level of uptake in these skills. The findings suggest that in co-viewing video lectures, it's crucial to engage students in clarifying behavioral sequences. Furthermore, the co-occurrence of deep-level knowledge, design skills, and a high level of uptake in shared ideas significantly enhances learning. Our research on learning processes suggests important strategies for students during co-viewing of video lectures: teachers are encouraged to design effective interactive activities and create suitable support interventions to enhance student learning performance in video lectures.

Analysis of Self-Regulated Learning Behaviors in Game-Based Learning Using Ordered Network Analysis

Analysis of Self-Regulated Learning Behaviors in Game-Based Learning Using Ordered Network Analysis

An Analysis of Radio Frequency Transfer Learning Behavior

Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML). This work systematically evaluates how the training domain and task, characterized by the transmitter (Tx)/receiver (Rx) hardware and channel environment, impact radio frequency (RF) TL performance for example automatic modulation classification (AMC) and specific emitter identification (SEI) use-cases. Through exhaustive experimentation using carefully curated synthetic and captured datasets with varying signal types, channel types, signal to noise ratios (SNRs), carrier/center frequencys (CFs), frequency offsets (FOs), and Tx and Rx devices, actionable and generalized conclusions are drawn regarding how best to use RF TL techniques for domain adaptation and sequential learning. Consistent with trends identified in other modalities, our results show that RF TL performance is highly dependent on the similarity between the source and target domains/tasks, but also on the relative difficulty of the source and target domains/tasks. Results also discuss the impacts of channel environment and hardware variations on RF TL performance and compare RF TL performance using head re-training and model fine-tuning methods.

Online Learning Behavior Analysis and Prediction Based on Spiking Neural Networks

Online Learning Behavior Analysis and Prediction Based on Spiking Neural Networks

Correlation Analysis of Learning Behavior with Learning Outcomes of 6th Grade Students in Banten Province

Student behavior is very influential on the achievement of learning outcomes because learning is adding and acquiring a certain amount of knowledge. Learning outcomes are generally defined as the relationship between learners and the surrounding system during the course of the schooling process, which leads to behavior change in the areas of cognition, behavior, and skills, which are represented by symbols or numbers. The research conducted focuses on analyzing the relationship or correlation between learning behavior and learning outcomes of elementary school students in Banten Province. The purpose of this research is to analyze the relationship or correlation between learning behavior and learning outcomes of elementary school students in Banten Province. Data collection techniques were obtained using a learning behavior questionnaire and observation of student learning outcomes. Sampling was carried out using simple random sampling technique in three elementary schools in Banten Province, which amounted to a sample of 90 students. The study results showed that the average value of student learning outcomes was 83.60. Prerequisite testing, normality test is done by normal probability plot test which is quite effective to detect a normally distributed research or not. According to the test, the data is normally distributed because the points on the diagram always follow and approach the diagonal. The significance value of the learning behavior variable and the learning outcome variable is 0.000 where 0.000 < 0.05, so there is a correlation between the variable learning behavior and the learning outcomes of students. The test results show that there is a significant relationship or correlation between the learning behavior variable and student learning outcomes.

Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective: To analyze the technical indexes of students’ online learning behavior analysis based on Kirkman’s evaluation model, sort out the basic indexes of online learning behavior, and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis. Methods: The online learning behavior data of Physiology of nursing students from 2021–2023 and the first semester of 22 nursing classes (3 and 4) were collected and analyzed. The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation. Results: The study found that the demand for online learning of nursing students from 2021–2023 increased and the effect was statistically significant. Compared with the stage assessment results, the online learning effect was statistically significant. Conclusion: The main indicators for evaluating and classifying online learning behaviors were summarized. These two indicators can help teachers predict which part of students need learning intervention, optimize the teaching process, and help students improve their learning behavior and academic performance.

Analysis and Modelling Of English Learning Behaviour Based on Data Mining Technology

This paper presents an in-depth exploration into the analysis and modelling of English learning behaviour leveraging data mining technology. In today's digital age, understanding the intricacies of how individuals acquire a second language is paramount for optimizing educational strategies and enhancing learning outcomes. By employing advanced data mining techniques, we aim to uncover patterns, trends, and underlying dynamics within English learning datasets, shedding light on the diverse behaviours exhibited by learners. Furthermore, this research transcends analysis, as we endeavour to construct robust models capable of simulating and predicting English learning trajectories. By leveraging insights derived from extensive data exploration, we aim to develop predictive models that offer personalized recommendations and adaptive learning pathways tailored to individual learner profiles. Ultimately, this paper contributes to the ongoing discourse surrounding language acquisition by providing valuable insights into English learning behaviours. By harnessing the power of data mining technology, we aspire to empower educators, policymakers, and digital learning platforms with the tools necessary to cultivate more efficient and inclusive approaches to English language education.

Overview of Online Learning Behavior Analysis in the Context of Big Data

Under the Internet era, various technologies such as big data and cloud computing are becoming more and more a hot issue of concern. Big data provides a large number of network resources with great economic as well as social value, which has a certain impact on various fields in society, and its use in education has received more attention from scholars. After the outbreak of the new crown epidemic, online learning has become a normalized way of learning, online learning platform records a large number of learners' learning data, through the processing and analysis of these massive data, not only is conducive to the realization of students' personalized learning, but also helps teachers to understand and master the current situation of students' learning, and then adjust their teaching. In order to have a clear understanding of the current research status of online learning behavior analysis in the context of big data, the article uses the visual analysis software CiteSpace to analyze the literature selected from the China Knowledge Network (CKN), from which the current status of the research, research hotspots, and future research trends in this field are obtained. The analysis results show that the number of publications has gradually decreased in the past two years, and there is less cooperation and communication between the core publication authors as well as the publication institutions; the analysis of learning behavior data on MOOC platforms has become a research hotspot at this stage, and in-depth learning, multi-scenario, and focusing on the students' learning experience are the future research trends.

An analysis of English learning behavior based on big data and its impact on teaching

This study explores the application of big data in the field of English learning, focusing on its influence on the analysis of English learning behavior and the effect of teaching intervention. Through experimental design and data analysis, the research results show that big data analysis can reveal the learning behavior pattern of learners, and provide personalized teaching intervention according to individual characteristics, so as to improve the learning effect. The study also found that the experimental group received personalized teaching intervention, English learners’ academic performance and learning motivation significantly improved. However, this study faces the limitations of sample representativeness and consistency of teaching interventions. Future studies can further expand the sample size and strengthen teacher training to improve the generalization and reliability of research results.

Analysis of Learning Behavior and Academic Achievement of Junior High School Students in the Province of Banten

The academic achievement of students is influenced by various factors encompassing learning attitudes, motivation, strategies, memory, and metacognition. This study specifically delves into learning attitudes, motivation, and strategies, which were acquired through respondent-completed instruments. The subsequent analysis aims to evaluate the factors that impact students' academic success. The primary objective of this research is to establish the correlation between students' learning attitudes, motivation, strategies, and their academic achievement. The findings from this study can guide schools in enhancing students' academic performance by tailoring interventions to address learning attitudes, motivation, and strategies. Employing a quantitative research approach, data collection involves survey instruments pertaining to learning attitudes, motivation, strategies, and academic achievement, with statistical techniques applied for data analysis. The results indicate that there is no statistically significant correlation between learning attitudes (X1) and academic achievement (Y), a statistically significant negative correlation between learning motivation (X2) and academic achievement (Y) at the 0.01 significance level, and no statistically significant correlation between learning strategies (X3) and academic achievement (Y).

Design and Implementation of Remote Piano Teaching Based on Attention-Induced Multi-Head Convolutional Neural Network Optimized with Hunter–Prey Optimization

The continuous progress of multimedia technology in music educational institutions has led to the recognition of its importance in our country and society. The traditional approach to piano teaching has its limitations, which can be overcome by adopting alternative approaches to the instrument, using advances in science and technology. For pianist, expressing emotions and thoughts through music is crucial, and teachers can now use multimedia tools to exemplify their musical skills to students effectively. This manuscript proposes the Remote Piano Teaching Based on Attention-Induced Multi-Head Convolutional Neural Network Optimized with Hunter–Prey Optimization to improve the piano-teaching quality. At first, input data is taken from Piano Triad Wavset dataset. Afterward, the data are fed to preprocessing stage. The preprocessing stage involve data cleaning or scrubbing that is the process of identifying errors, inconsistencies, and incorrectness in a dataset with the help of adaptive distorted Gaussian matched filter. Then, the preprocessed output is fed to Attention-Induced Multi-Head Convolutional Neural Network (AIMCNN) for effectively predict the piano-teaching quality. The hunter–prey optimization (HPO) algorithm is proposed to optimize the parameters of Attention-Induced Multi-Head Convolutional Neural Network. The performance of the proposed technique is evaluated under performance metrics like accuracy, computational time, learning skill analysis, learning activity analysis, learning behavior analysis; student performance ratio and teaching evaluation analysis are evaluated. The proposed RPT-AIMCNN-HPO attains better prediction accuracy 12.566%, 12.075% and 15.993%, higher learning skill 15.86%, 15.26% and 16.25% compared with existing methods.

Analysis on the Design and Timeliness of English Teaching Models in Colleges and Universities under the Background of Informatization

Abstract The informationization era has accelerated people’s learning changed their thinking and cognitive styles, and the English teaching mode is also evolving and innovating. The purpose of this paper is to develop an English learning behavior analysis model, select an improved K-means algorithm for clustering learners, and examine the association rules between learning behaviors and learning effects. In addition, based on the sequence of learners’ behaviors, the article also combines the EduHawkes prediction model with multi-type features to quantify the learning effect in terms of learning engagement and predict the learning effect of students. The blended English teaching model is designed by combining learning behavior analysis and learning effect prediction models. In the experimental study of the timeliness of the English teaching model, the dimensions of students’ English literacy in terms of thinking quality, learning ability, and language ability showed significant differences before and after the experiment (P<0.05). In the evaluation of teaching methods, the dimensions of teaching materials, teaching tools, evaluation of learning outcomes assessment methods, and evaluation of teaching level showed significant differences (P<0.05). The English teaching model proposed in this paper is timely.

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.