Student Behavior Patterns Research Articles

Investigating behavioral patterns to facilitate performance predictions during online peer assessment through learning analytics approach

Insights into student behavioral patterns yield benefits for both educators and learners. Nevertheless, only a few studies have investigated the behavioral patterns of high-scoring (HS) and low-scoring (LS) students to facilitate predictions during peer assessment (PA). Therefore, we performed learning analytics to explore the behavioral patterns of HS and LS students in 52 university students from affective, cognitive, and metacognitive perspectives as these students engage in online PAs. The results indicated that on the affective dimension, HS students tended to exhibit negative affection while LS students tended to display positive affection. On the cognitive dimension, HS students demonstrated more intricate transformations compared to the LS students. Regarding the metacognitive dimension, LS students more likely reflected upon and accepted the reviews provided by others than the HS. Additionally, the findings also revealed that HS students were supported by focused social networks and overall stable activity engagement, whereas the LS students were less engaged at the beginning of the activity but had evolving social networks and were more engaged in the later stages of the activity. These findings offer valuable insights for educators in effectively predicting student performance based on behavioral patterns while providing new perspectives to support instructional decision-making processes.

Uncovering insights from big data: change point detection of classroom engagement

Expectations of big data across various fields, including education, are increasing. However, uncovering valuable insights from big data is like locating a needle in a haystack, and it is difficult for teachers to use educational big data on their own. This study aimed to understand changes in student participation rates during classes and teachers’ teaching styles by analyzing educational big data. In the analysis, data from 120 students and two mathematics class teachers at a public junior high school in Japan were used. We applied the pruned exact linear time (PELT) algorithm to automatically identify significant changes in student participation during class. Based on the information obtained, we analyzed the interaction logs of teachers’ e-book readers and clarified the relationship between student participation rates and teacher behavior patterns. Change point detection using the PELT algorithm showed a high F1-score of 0.7929, indicating good overall performance. We also investigated whether there was a relationship between class differences and teachers’ actions and found a statistically significant difference. The results provide clues for improving student learning engagement and teachers’ teaching styles, and they are expected to improve the quality of education by automatically identifying notable cases from educational big data. However, further research is required to improve data analysis methods, such as adjusting the parameters of algorithms based on the situation.

Analysis of the repercussions of Artificial Intelligence in the Personalization of the Virtual Educational Process in Higher Education Programs

This study examined how artificial intelligence (AI) has transformed the personalization of the virtual educational process in higher education programs. A systematic review of literature published between 2012 and 2023 was carried out, evaluating empirical studies, reports and review articles available in academic databases such as IEEE Xplore, SpringerLink and Google Scholar. Methods discussed include intelligent tutoring systems, learning analytics, and recommendation systems. The results showed that AI significantly improved the personalization of learning. Intelligent tutoring systems provide real-time adaptive feedback, adjusting content and pacing based on students' individual needs, improving their understanding and retention. Learning analytics helps identify student behavior patterns and predict academic issues, thereby facilitating timely interventions that help improve performance. Additionally, recommender systems personalize study materials based on student preferences and progress, thereby optimizing the educational experience. However, significant challenges have been identified, such as the need to protect data privacy and mitigate algorithmic biases that can affect the fairness and efficiency of these systems. In conclusion, the integration of AI into virtual higher education has enhanced the personalization of learning, improving both student satisfaction and academic performance. However, there is a need to continue to focus on developing ethical and equitable AI systems to address identified issues and maximize educational benefits

Optimization of English Classroom Interaction Models Incorporating Machine Learning

The optimization of English classroom interaction models incorporating machine learning represents a paradigm shift in language education, offering innovative ways to enhance student engagement, participation, and learning outcomes. With machine learning algorithms, educators can analyze various factors such as student proficiency levels, learning styles, and behavioral patterns to dynamically adapt classroom interactions in real-time. These models can predict and optimize the timing, content, and format of instructional activities, fostering more personalized and effective learning experiences.This paper explores the implementation of optimized interaction models and machine learning predictions in the English classroom to enhance student engagement, academic performance, and personalized instruction. Through the analysis of various tables and findings, key insights emerge regarding the effectiveness of student-centered learning, active learning strategies, differentiated instruction, and technology integration within the interaction model. Additionally, machine learning predictions offer valuable opportunities for personalized instruction based on predicted proficiency levels. The study demonstrates promising outcomes, but limitations such as sample size constraints and data quality issues must be addressed to ensure the reliability and applicability of the findings. Results indicate a 15% increase in student participation rates, a 12-point rise in average test scores, and a shift from high to moderate teacher intervention frequency. Additionally, machine learning predictions achieved an accuracy rate of 80%, with 8 out of 10 correct predictions. Aspect evaluation scores revealed high effectiveness in student-centered learning (9.2), formative assessment (9.3), and teacher facilitation (9.0). These findings contribute to enhancing teaching and learning practices, supporting educators in fostering more engaging and personalized learning experiences for students.

Sequence Modeling with Recurrent Neural Networks (RNNs) for Student Learning Behavior Pattern Recognition in a Flipped Classroom

The flipped classroom model has become increasingly popular in education, altering the traditional methods of teaching. It is important to understand and acknowledge how students learn within this framework in order to optimize instructional strategies and promote personalized learning. This study investigates the use of Sequence Modeling with Recurrent Neural Networks (RNNs) to identify patterns in student learning behavior within a flipped classroom setting. The proposed deep learning architecture utilizes RNNs to analyze sequential patterns in students' interactions with the flipped classroom materials, while also incorporating attention mechanisms to better detect important patterns and temporal dynamics in the learning process. Multimodal learning techniques are also employed, combining data from various sources to gain a comprehensive understanding of student behavior. Additionally, clustering techniques using autoencoders are explored to group students with similar learning behaviors. Predictive models, such as RNN or LSTM networks, are developed to forecast future learning behaviors and provide insights into potential challenges or successes for individual students. The effectiveness of this framework is evaluated using real-world data from flipped classroom implementations, with performance metrics like recall, precision, and accuracy used to assess the success of the sequence modeling approach in recognizing and predicting student behavior patterns. Overall, the application of deep learning methods, specifically sequence modeling with RNNs, demonstrates potential for improving personalized learning experiences and facilitating proactive interventions to support diverse student needs.

Penerapan Konsep Arsitektur Perilaku: Ruang Kelas Pondok Pesantren Al-Hidayah Kota Jambi

Along with the pace of community development, Islamic boarding school education, both in terms of location, form and substance, has undergone many changes. Islamic boarding schools are no longer as simple as people describe them. Islamic boarding schools can experience changes according to the growth and development of the times. One of them is the khalafiyah or modern pesantren, which implements a classical teaching system by providing general knowledge and religious knowledge, as well as providing skills education. One of the Islamic boarding schools that use this education system is the Al Hidayah Islamic Boarding School in Jambi City. The considerations made are related to the behavior patterns of MI students which need to be reformed and improved for the progress of Islamic boarding schools by applying patterns that are appropriate to student behavior. This study aims to obtain a good arrangement of spatial patterns for students to learn. The method is done by taking a simple random sample or called simple random sampling. The results of the study provide recommendations for design patterns that can be applied in the interior space.

Identifying Student Behavior Patterns Based on LMS Data

In contemporary education, data analysis methods to establish patterns of student behavior for subsequent optimization of the educational process are becoming increasingly relevant. This paper presents research aimed at identifying patterns of student behavior based on the processing of data obtained from Learning Management Systems (LMS). The article examines data collection and analysis issues from LMS, including activity logs, grades, participation in forums, and other interactive elements. Additionally, methods such as statistical analysis and machine learning, applied to identify patterns of student behavior, are discussed. The text describes the identified patterns of student behavior in the electronic educational environment, which are subsequently linked to students' academic performance levels. The paper's concluding section presents the research findings and potential scenarios for their application. Key Words: adaptive learning, machine learning, learning management systems, student behavior patterns.

Islamic Career Guidance for Academic Procrastination Behavior of Students at Muhammadiyah University Enrekang Class of 2017

Academic procrastination such as tending to postpone assignments and not being able to manage time well so as to delay or complete the assignments given. The aim of the research is to determine student procrastination behavior and service patterns towards these students. Method used namely a qualitative field approach (field research). Data collection techniques are through interviews and observation. The results of the research show that the academic procrastination behavior found was: Being late for class, postponing carrying out assignments given by the lecturer in charge of the course, carrying out other activities and permission to study in class, attendance not being optimal, and also being too engrossed in the organizational world so that it is threatened DO. Then to the procrastination is provided with Islamic career guidance services which include recognizing one's potential. where in each session they are guided to make a live proposal. The live proposal will determine when, where and how the career will be achieved. Then doing Affirmations is intended to help students create positive sentences written in "life proposals". The positive sentences are in the form of encouraging sentences. The sentences are short so they are easy and easy to remember. Like I learn from every experience and become wiser every day. I am a source of inspiration for my peers. I deserve to be happy with a more meaningful life, according to the instructions of the Al-Qur'an and Hadith.

The varied restorative values of campus landscapes to students’ well-being: evidence from a Chinese University

BackgroundThe literature on therapeutic landscapes highlights that the university campus landscape has restorative effects on students. This deserves more scholarly attention since mental health has become an important issue among university students. However, existing empirical studies have revealed mixed evidence with little attention to the heterogeneity across the design and, therefore, the potential therapeutic effects across different landscapes.MethodThis research examined how 13 landscape sites on a university campus might be differentially related to student well-being. These sites were identified from a variety of sources (campus design documents, photos used in the university’s social media posts, and interviews with a small group of students) to represent a comprehensive list of places that students might visit. The data was collected in a large online survey of a Chinese university (n = 2,528). We asked about students’ use of individual landscape sites and the associated motivations for visits, and measured well-being using a perceived stress scale and overall evaluation of the happiness level. Bivariate analysis was used to explore the zero-order associations between landscape use and well-being. OLS (for stress) and logistic regressions (for happiness) were conducted to further evaluate the associations after controlling the student background variables and potential correlations of uses across different landscapes.ResultsAmong 13 landscape sites, four sites had significant positive associations with either or both measures of well-being after controlling for the student characteristics and use of the other landscape sites. There was also an additive benefit of visiting more landscapes. Compared to those who did not frequently visit any of the sites, well-being had a significant stepwise increase among those who frequently visited one or two and more sites. One site that was significantly related to both measures of well-being only offered distant views of landscapes, but it was right next to the study areas.ConclusionsThis study demonstrates the heterogeneity of restorative effects across different landscapes on campus. The findings suggest that effective landscape design that aims to promote student well-being should be placed close to stressors (i.e., where they study), and between where they study and live to offer students opportunities to break from the common routines and to relax. The findings hold greater relevance for universities in China and institutions with similar student campus lifestyles, occupancies, and behavior patterns worldwide.

Association of sleep patterns and water intake with cognitive functions in adults in an urban environment.

To explore the behavior patterns of students and working adults regarding the duration and quality of sleep and water intake in an urban environment and to identify the relationship between nighttime sleep extent and water intake with mood and cognitive performance. This was a descriptive correlational study conducted at Islamic International Medical College, Riphah University at the Pathology department from March to June 2022. A total of 160 participants with age range of 20 to 50 years completed a self-report questionnaire regarding sleep patterns, water intake, and perceptions of mood, concentration, and memory. Analyzed the relationship between sleep duration, water intake, mood, mindfulness/concentration, and memory using Kendall's Tau-b correlation coefficient in SPSS 22. A significant number (28.7%) of participants had a sleep duration of ≤ 6 hours, with 41.3% sleeping after midnight. 82.5% of the participants switch off-screen just before sleeping. 63.7% have a routine water intake of less than 2 L/day. An aberrant statistically significant negative correlation between total sleeping hours and mood (τb = -.313, p = 0.004) was identified, showing a negative effect on mood with a sleeping time of ≥ 9 hours when compared with the other two groups (6-9 hours, τb = - .689, p = 0.001, ≤ 6hours, τb = - .697, p = 0.001). A significant correlation between daily water intake and concentration was found, wherein a decrease in daily water intake showed a negative effect on concentration (τb = .289, p = 0.008). Sleep duration and water intake patterns may affect mood and cognitive performance. Regular sufficient nighttime sleep and adequate hydration may help improve cognitive functioning and mood.

TRANSFORMASI PERILAKU KONSUMTIF MAHASISWA DI PERANTAUAN (STUDI KASUS MAHASISWA EKONOMI SYARIAH UINFAS BENGKULU )

The aim of this research is to determine and explain student consumption behavior patterns, and to determine the factors that cause the transformation of student consumptive behavior in overseas sharia economics study programs at Fatmawati Sukarno State Islamic University, Bengkulu. This research uses a qualitative method with a case study approach, data collection techniques are interviews, observation and documentation. The social situation used in this research was 15 sharia economics study program students class of 2019. The data analysis technique used was the Miles and Huberman model. From the results of the research and discussion, it was found that the transformation of consumptive behavior of overseas students at Fatmawati Sukarno University, Bengkulu, sharia economics study program, among others, in terms of prioritizing consumption, they prioritize needs rather than desires, in terms of choosing goods for consumption, many of them do not pay attention to the type of goods, cleanliness. or the price, using branded goods to gain appreciation and high social status, discounting prices, bargaining, and spending free time hanging out in cafes and coffee shops. Coffee shops are just places to relax and have fun. This is what makes them trapped in consumer behavior. The aim of this research is to determine and explain student consumption behavior patterns, and to determine the factors that cause the transformation of student consumptive behavior in overseas sharia economics study programs at Fatmawati Sukarno State Islamic University, Bengkulu. This research uses a qualitative method with a case study approach, data collection techniques are interviews, observation and documentation. The social situation used in this research was 15 sharia economics study program students class of 2019. The data analysis technique used was the Miles and Huberman model. From the results of the research and discussion, it was found that the transformation of consumptive behavior of overseas students at Fatmawati Sukarno University, Bengkulu, sharia economics study program, among others, in terms of prioritizing consumption, they prioritize needs rather than desires, in terms of choosing goods for consumption, many of them do not pay attention to the type of goods, cleanliness. or the price, using branded goods to gain appreciation and high social status, discounting prices, bargaining, and spending free time hanging out in cafes and coffee shops. Coffee shops are just places to relax and have fun. This is what makes them trapped in consumer behavior.

The identification of potential sources of influence on the formation of healthy food behavior patterns of university students in modern educational conditions

The identification of potential sources of influence on the formation of healthy food behavior patterns of university students in modern educational conditions

Reform and Innovation of Ideological and Political Education Teaching Mode in Colleges and Universities in the Context of Information Technology Empowerment

Abstract In the rapidly evolving landscape of higher education, integrating digital advancements presents challenges and opportunities for ideological and political education. This research delves into the intersection of information technology and educational methodology, proposing a novel approach to teaching civics and politics. We accurately identify student behavioral patterns by applying a sophisticated clustering analysis through density optimization K-means, NN-Stacking, and Stacking fusion techniques, thereby tailoring educational content more effectively. Implementing this data-driven teaching model significantly boosts student outcomes in civics education, evidenced by an average score increase of 13.306 points. Furthermore, statistical Analysis (P = 0.0433 < 0.05) validates the enhanced impact of this method compared to traditional pedagogies. This study underscores the transformative power of information technology in enriching ideological and political education, paving the way for innovative teaching strategies.

A STUDY OF THE RELATIONSHIP BETWEEN APARTMENT OCCUPANCY PATTERNS AND STUDENT BEHAVIOR PATTERNS Case Study: Gading Icon Apartment

Apartment is a room or several rooms (rooms) that are functioned or designated as a place to live, located in a building that usually has other rooms or rooms and the like. This study attempts to explore and analyze the relationship between residential space patterns and student behavior patterns in Jakarta. The behavioral patterns of students that affect the pattern of vertical residential spaces not only have an impact on the behavior and form of space but also see how student behavior shapes architecture in residential space patterns and architecture shapes student behavior. The purpose of this study is to understand the theory related to the relationship between vertical residential space patterns and student behavior patterns, to understand the characteristics of the relationship between residential space patterns and student behavior, to understand environmental factors for student behavior. The research method taken on the research approach uses a qualitative descriptive method. The data studied and obtained are physical and non-physical data obtained from interviews, observations, and mapping of student behavior patterns in residential spaces of Jakarta apartments. The approach that will be used is the Human Behavior approach to students in apartment housing. From the analysis of behavior patterns carried out, student activities are quite dense, causing the use of furniture according to needs and has ergonomic characteristics.

Value systems of representatives of different generations in the process of decision-making on choosing a university and educational program

Purpose: is to propose the methodological approach to modeling the behavior of the representatives of generations Y and Z in the process of making decisions about choosing a university and educational program based on differences in their value systems.Methods: the theoretical and methodological basis for studying the value systems of potential and actual university students is the theory of generations. The findings of the study are based on the methods of analysis of secondary and primary data collected through an online survey of students belonging to generations Y and Z on representative samples of 380 (Y) and 788 (Z) observations in 20 large universities in Russia, as well as the methods of analysis of statistically significant differences in value systems of two generations using T-test for independent samples.Results: the authors proposed the methodological approach to modeling the behavior of consumers of generations Y and Z in the process of making decisions on choosing a university and educational program. Based on the results of desk and field (empirical) research, at each stage of the decision-making process on selecting an educational institution and program, differences were identified in the value systems of students of generations Y and Z, which determine their behavior: statistically significant differences were identified in 19 out of 65 variables. The study provides description of the behavior patterns of students of the two generations at each stage of the decision-making process which formed the basis for dividing students into target groups. The guidelines for differentiating marketing interactions with target groups of representatives of two generations in the process of educational interactions while making decisions are proposed.Conclusions and Relevance: differences in the value systems of representatives of generations Y and Z cause differences in behavioral patterns at each stage of the decision-making process. Understanding these differences enables us to model the behavior of the two target groups, differentiate marketing approaches to interaction with them at the stages of the decision-making process while choosing a university and educational program as well as increase the effectiveness of educational interactions in the process of education.

The use of psychological and psychiatric methods in determining valeological competence formation in non-medical students of Ukraine

Background. Valeological competence is the ability to lead a healthy lifestyle. Teaching the basics of a healthy lifestyle in higher education institutions of Ukraine is regulated by higher education standards. Valeological competence is formed in non-medical students during the teaching of valeological disciplines. At the Ukrainian Engineering Pedagogics Academy (Kharkiv) such a valeological discipline is "Health Pedagogy", at the National Technical University "Kharkiv Polytechnic Institute" (Kharkiv) and at the National University of Civil Defense of Ukraine (Kharkiv) – "Fundamentals of Medical Knowledge and Health-Saving". Confidential questionnaires are used to assess the success of the formation of the motivational-value and personal components of valeological competence, the list of questions of which allows studying the behavior patterns of non-medical students. Aim. To establish the conformity of the questions of the questionnaires, which are used to study the formation of the motivational-value and personal components of the valeological competence in non-medical students, to the existing psychological and psychiatric methods. Materials and Methods. The bibliosemantic method and the system analysis method were used in the study. Results. Correspondence was established between the questions used to assess the success of the formation of the motivational, value and personal components of valeological competence, with the questions of such psychological and psychiatric methods questionnaires by Rogers K. and Diamond R. (social-psychological adaptation), Boyko V.V. (level of emotional burnout), Burtyanskyi D.L. and Krystal V.V. (levels of socio-pedagogical and sexual-behavioral adaptation of a married couple or sexual partners), Navran L. (satisfaction with married life or life with a sexual partner) and the CAGE-AID screening test for alcohol and/or drug addiction. Conclusions. The questions of the questionnaires used for assess the success of the formation of the motivational-value and personal components of valeological competence in non-medical students, correspond to existing psychological and psychiatric methods, the modification of which to the needs of assessing the formation of the components of valeological competence requires further research. Keywords: qualitative factor-criterion model, health-saving, valeological education.

Financial Education, Parent's Income, Financial Literacy on Financial Management Behavior through Self-Financial Efficacy in Students

Globalization is marked by the rapid advancement of technology, which will impact human thinking patterns and tend to encourage individuals to get trapped in consumer behavior. The lack of financial literacy makes society vulnerable to financial problems. Therefore, the importance of the role of both the community and the government in providing education on financial literacy is crucial. One of the government's roles, through OJK, is to provide education on Financial Literacy to the public, especially students and scholars. The community's role can start from within the family circle, by educating children about family financial management from an early age. Consequently, financial education from a young age will inadvertently benefit children because they can manage their finances, such as pocket money, effectively. The methods used include quantitative methods combined with path analysis, and the results show that there is a direct influence of Family Financial Education, Parent's Income, and financial knowledge on financial management behavior through self-financial efficacy. Then, the results of the Path Analysis with the Sobel test show that Family Financial Education (0.495 > 0.387), Parent's Income (0.158 > -0.194), and Financial Literacy (0.176 > -0.289) affect these variables. The conclusion from the test results indicates that Family Financial Education, Parent's Income, and Financial Literacy directly and indirectly influence the financial behavior patterns of students."

Continuous Student Improvement Using Machine Learning Techniques

Abstract: Student Improvement model aims in identifying student behavior traits and patterns using multiple psychometrictests followed by suggesting ways to improve areas of their interest along with overall psychological balance by continuous testing at constant intervals of time. To identify the subject’s strengths, weaknesses, and behavioral interests, assessments are carried out on the principle of psychological assessments which assess the students by making them imagine their reactions in various psychological situations. We use different classification methods to predict psychological factors and the efficiency ismeasured by performance factors like recall, precision, and F1 score. It is mainly based on 3 Psychometric tests that are Big Five Personality, Benziger Thinking style assessment, and KOLBlearning preferences. These tests have a huge role to play in the fields of career guidance and employability. Our model evaluates an individual’s personality traits and learning styles and provides a descriptive analysis which helps in identifying students’ overall improvement.

Investigating behavior patterns of students during online self-directed learning through process mining

Investigating behavior patterns of students during online self-directed learning through process mining

PENGARUH PENGGUNAAN MEDIA SOSIAL TERHADAP POLA PERILAKU KOMUNIKASI SISWA SMA AR-RAHMAN MEDAN

The purpose of this study is to determine the effect of the use of social media and to determine how much influence occurs between the use of social media. social media and to find out how much influence occurs between the use of social media on student communication behavior patterns at ArRahman Medan High School. This research uses quantitative research which aims to determine the effect of Social Media Use on Student Communication Behavior Communication Behavior of Students at Ar-Rahman High School Medan. This research will involve 2 (two) variables, namely variable Y: communication behavior and variable X: social media. social media. From the results of the research, the researcher can conclude two things, namely the variable the use of social media has a significant effect on Communication behavior of Ar-Rahman Medan Private High School students and the amount of influence given by the use of social media is 51.5 percent. Given by the use of social media is 51.2% on Communication Behavior, which means there is still 48.2%. Communication Behavior, which means that there are still 48.2% other factors that can influence the Communication Behavior of students.