Student Learning Patterns Research Articles

Enhancing Education and Teaching Management Through Data Mining and Support Vector Machine Algorithm

This study investigates the effectiveness of the SVM+TLBO approach for predicting student performance and evaluating learning abilities in educational settings. By integrating Support Vector Machines (SVM) with Teaching-Learning-Based Optimization (TLBO), the research aims to enhance predictive accuracy and efficiency compared to traditional methods, including Decision Trees, Ant Colony Algorithms, Clustering Algorithms, Convolutional Neural Networks (CNN), Neural Networks, Support Vector Machine (SVM) without Optimization. Results indicate that the SVM+TLBO model significantly outperforms these methods, providing robust predictions and valuable insights into student learning patterns. The findings underscore the importance of data-informed decision-making in education, enabling educators to identify at-risk students and tailor instructional strategies effectively. This research contributes to the growing field of educational data mining, highlighting the potential of advanced machine learning techniques to optimize educational outcomes and foster personalized learning experiences.

Predicting Student Performance Using Educational Data Mining: A Review

Educational Data Mining (EDM) strategies facilitate the efficient and in-depth analysis of student data. EDM provides useful insights into comprehending student learning patterns and identifying factors that influence academic success. This review aims to evaluate the efficacy of classification algorithms popularly explored in EDM for predicting student performance and identifying common trends in existing EDM research. The review follows a systematic approach, relevant research articles have been cited following an inclusion and exclusion criteria to ensure the selection of studies that specifically address the use of EDM techniques for predicting student academic achievement. According to the review findings, most researchers have utilized the features of cumulative grade point average, internal and external assessment, and demographic information to predict student performance. The most common techniques in EDM for predicting students’ performance are Naïve Bayes and Decision Trees. The review also focuses on the potential for bias, key examination of challenges, and possible future directions in the field. In the context of student performance prediction, ethical considerations regarding privacy, data handling, and the interpretation of results are also identified

VIRTUAL LABORATORIES IN SCIENCE EDUCATION: UNVEILING TRAJECTORIES, THEMES, AND EMERGING PARADIGMS (2013-2023)

Virtual laboratories are transformative tools in science education, yet comprehensive reviews of their prospects are limited. This study addresses this gap through a bibliometric analysis of 218 articles published between 2013 and 2023. Key findings included: (1) research has evolved from initial exploration to rapid expansion; (2) virtual labs have demonstrated adaptability across various scientific disciplines, including chemistry, physics, biology, engineering, and medicine; (3) five research trajectories focus on virtual labs, science education, and computer-based learning; and (4) eight research topics, such as comparisons with physical labs and student performance, have gained prominence. These findings had implications for educational practices, research methodologies, and policy considerations. For educational practices, virtual laboratories offer benefits by removing the need for physical resources, providing flexible delivery, enabling safe exploration, fostering engagement through hands-on experimentation, and enhancing understanding with immersive experiences. For research, virtual labs improve learning and analytical skills, generate detailed data on student behaviors and learning patterns, and lead to innovations in teaching methods and curriculum design. For policymakers, strategic planning is needed to create supportive policies for the effective adoption of virtual labs, including teacher training, infrastructure development, and curriculum integration, making science education more accessible and equitable for all students. Keywords: bibliometrics analysis, science education, research trends, virtual laboratory

Autonomous learning patterns of students with special needs in quantum learning using flipbook media at Indonesia

Autonomous learning referred to students’ ways and processes to develop their learning skill independently and take responsibility of it. Autonomous learning encouraged students to have active responsibility in their learning process, develop their metacognitive skill, and be more independent and proactive learners. This study aimed to investigate autonomous learning pattern of students with special needs in quantum learning using flipbook media. This was qualitative research in form of case study. The participants were primary school students at Bojonegoro Regency, Tuban Regency, and Cepu Regency. Those regencies covered various students’ backgrounds and special needs. From all participants, five students were selected specifically. They were selected because they showed characteristics of autonomous learning skill that could be easily identified. Data was collected using observation and in-depth interview. Technique in analyzing data was thematic analysis technique. Data was validated using member checking. Result of study showed that students with special needs who utilized flipbook media in quantum learning tend to be more independent in setting learning goals, managing learning process, taking initiative, completing assignments, and conducting self-reflection to their progress. It could be concluded that quantum learning using flipbook media could be effectively create five patterns of autonomous learning for students with special needs at Indonesia.

Problematika Metode Pengajaran dalam Pendidikan Islam Modern

Teaching methods play an important role in achieving educational goals, including in modern Islamic education. However, in practice, there are a number of problems faced by educators. With a literature study approach, this paper aims to identify and analyze various challenges that arise in the application of teaching methods in modern Islamic education, such as the gap between the curriculum and social reality, the dominance of conventional teaching methods that are less interactive, and the limited use of technology. The influence of globalization and technological developments also affect student learning patterns and pose new challenges in maintaining Islamic identity and values. To overcome these problems, innovation is needed in teaching methods, integration of technology in learning, alignment of the curriculum with the needs of the times, and improvement of the quality of educators. Thus, modern Islamic education can be more relevant and effective in forming a generation of Muslims who are noble, intelligent, and able to compete in the global era.

Meningkatkan Hasil Belajar dan Berfikir Kritis PAKBP dengan Model PBL Berbantuan Media Audio Visual Fase B Kelas IV SDN Sawahan

The implementation of the "Merdeka Belajar" (Freedom to Learn) Curriculum faces various challenges, including in terms of curriculum implementation and adjustment. These challenges include changes in student learning patterns, the broader role of teachers, and efforts to meet the established educational standards. In the context of the Merdeka Curriculum, Catholic religious education in Indonesia focuses on developing students' understanding of religion, moral values, and Catholic spirituality, while also emphasizing the Pancasila student profile.The Merdeka Curriculum has also had impacts and posed challenges at SD Negeri Sawahan Surakarta regarding students' academic performance or learning outcomes. For example, in the cognitive aspect, when studying the Grade IV lesson on Developing My Abilities, the students' learning outcomes were unsatisfactory. Out of a total of 6 students, around 4 (67%) scored below the Minimum Completeness Criteria (KKM), which was set at 75. In this classroom action research, the researcher focuses on the dimension of critical thinking, based on the importance of building strong character and mental attitudes in the younger generation, especially in facing complex global challenges. Creative thinking skills will result in more effective learning and develop higher reasoning abilities that can be used to solve various problems in learning. Students with creative thinking skills tend to have high intrinsic motivation, strong drive, self-confidence, and better thinking abilities. In this model, the teacher acts as a facilitator who helps students develop deep understanding and relevant skills through motivation, stimulating questions, and support for meaningful learning experiences. PBL focuses on common everyday problems, and its process involves problem identification, data collection, and the use of data to solve problems. The primary goal of PBL is to develop critical thinking, problem-solving skills, and knowledge acquisition, helping to improve students' learning outcomes and critical thinking skills in the topic of developing self-abilities in Grade IV at SD Negeri Sawahan, Surakarta.

Revolutionizing Education: The Impact of AI-Enhanced Teaching Strategies

In recent years, the integration of Artificial Intelligence (AI) into educational settings has revolutionized traditional teaching methodologies, leading to the development of innovative teaching strategies. This paper explores how AI-enhanced approaches are transforming education by personalizing learning experiences, improving student engagement, and optimizing instructional methods. Through adaptive learning platforms, AI tailors educational content to meet individual student needs, fostering a more inclusive and effective learning environment. Intelligent tutoring systems provide real-time feedback and support, enabling students to progress at their own pace and achieve mastery in various subjects. Additionally, AI-driven analytics offer educators valuable insights into student performance and learning patterns, facilitating data-informed decisionmaking and targeted interventions. By leveraging AI technologies, educators can create dynamic, interactive, and studentcentered classrooms that cater to diverse learning styles and preferences. This paper discusses the potential benefits and challenges of AI-enhanced teaching strategies and highlights the importance of integrating AI thoughtfully and ethically to maximize its positive impact on education.

Zoom fatigue related to online learning among medical students in Thailand: Prevalence, predictors, and association with depression.

Amidst the COVID-19 pandemic, the learning pattern of medical students shifted from onsite to online. This transition may contribute to what has been called "Zoom fatigue." This study aimed to evaluate the prevalence of Zoom fatigue related to online learning, identify associated factors of Zoom fatigue, and explore its correlation with depression among medical students during the COVID-19 pandemic. This cross-sectional study was conducted among 1st to 6th-year Thai medical students. The online survey was administered using a demographic and health behavior questionnaire, the Patient Health Questionnaire-9 (PHQ-9), and the Thai version of the Zoom Exhaustion & Fatigue Scale (ZEF-T). Among the 386 participating students, 221 (57%) were female, with a mean age of 20.6 years. The prevalence of high Zoom fatigue was 9.6%. In the multivariable regression analysis, a lower academic year and a higher number of online learning sessions were significant predictors of Zoom fatigue (p < 0.001), while regular exercise emerged as a protective factor (p = 0.009). The prevalence of depressive disorder was 61.9%, and a significant correlation was found between having a depressive disorder and experiencing Zoom fatigue (p = 0.004). Zoom fatigue among medical students was correlated with depression. Consequently, medical students experiencing Zoom fatigue should undergo further assessment for depression. It is crucial to closely monitor medical students in lower academic years with a high number of online sessions for signs of Zoom fatigue. Additionally, implementing strategies, such as reducing the frequency of online sessions and promoting regular exercise, may help alleviate the symptoms.

Learning patterns among Arab college students: the relationship with academic achievement and the moderating role of cognitive engagement

ABSTRACT This study explored the learning patterns of undergraduate students in Kuwait using Vermunt’s model and investigated the relationships between these patterns, cognitive engagement, and academic achievement. The ILS was used to identify the learning patterns of 563 university students. The three identified learning patterns were active, passive, and undirected, with the passive pattern being the most prevalent among Kuwaiti undergraduates. Active learners used self- and external regulation strategies and managed their learning actively, while passive learners studied for tests to find a job and had idealistic learning expectations. The undirected pattern characterized students who did not have a specific learning strategy and required cooperation for learning. The study found that active and passive patterns were positively correlated with academic achievement. Moreover, cognitive engagement was found to moderate the relationship between learning patterns and academic achievement, with a stronger effect observed for passive learners. The study emphasizes the need to shift teaching practices in Kuwait towards problem-based learning to foster critical thinking and highlights the cultural gap between students and teachers.

Student Performance Analysis: A Systematic Research

In the rapidly evolving landscape of education, understanding and improving student performance is of paramount importance. This research paper explores the application of Machine Learning (ML) technologies within the realm of Big Data Analytics to comprehensively analyze and enhance student performance. Leveraging the vast amount of data generated within educational institutions, this study demonstrates how ML algorithms and techniques can be harnessed to gain insights into student learning patterns, predict academic outcomes, and develop data-driven strategies for educational improvement. The paper begins by highlighting the significance of student performance analysis in modern education and the challenges faced by institutions in managing and interpreting the growing volume of educational data. It then presents a comprehensive review of the state-of-the- art ML algorithms and data processing techniques relevant to student performance analysis. Furthermore, the research outlines a novel framework for student performance analysis, integrating various ML models such as regression, classification, and clustering, alongside advanced data preprocessing techniques. The proposed framework is designed to handle diverse educational datasets, including academic records, attendance records, socio-demographic information, and learning resources utilization[1] . Through a series of experiments and case studies, this paper demonstrates the practical application of ML in predicting student performance accurately, identifying at-risk students, and personalizing educational interventions. It also delves into the ethical considerations and data privacy concerns associated with the use of student data in ML-based educational analytics. The results and insights from this research offer valuable implications for educational institutions, policymakers, and researchers alike. By harnessing the power of Big Data and ML technologies, institutions can make data-driven decisions to enhance teaching methodologies, improve student support systems, and ultimately elevate student success rates. Additionally, this study contributes to the ongoing discourse on the responsible use of data in education, emphasizing the importance of transparency, fairness, and ethical considerations. In conclusion, this research paper presents a robust framework that showcases the potential of Machine Learning technologies within the realm of Big Data Analytics for student performance analysis. It offers a roadmap for educational institutions to harness the power of data to foster better learning outcomes and contributes to the ongoing dialogue on responsible data usage in education.

Artificial Intelligence in education: A comprehensive study

This comprehensive study delves into the multifaceted role of AI in education, exploring its applications, benefits, challenges, and future implications. The purpose of the study is to show how AI n education helps educators identify gaps in student knowledge and provide targeted feedback to improve learning outcomes. As a methodology, the library method and the study and review of various documents have been used in this research. The study examines the diverse range of AI technologies employed in educational settings, including intelligent tutoring systems, personalized learning platforms, educational chatbots, and virtual reality simulations. Furthermore, the study delves into the numerous benefits that AI brings to education. It highlights how AI-powered analytics and data-driven insights enable educators to gain deeper insights into student learning patterns, identify areas for improvement, and tailor instructional strategies accordingly. Additionally, AI-driven tools promote inclusivity by providing personalized support to learners with diverse needs and learning styles. Despite its transformative potential, the study also acknowledges the challenges and ethical considerations associated with integrating AI into education. Data privacy, algorithmic bias, and the digital divide are examined in detail, emphasizing the importance of responsible AI deployment and ethical guidelines. Looking ahead, the study explores the future implications of AI in education and the evolving role of educators in AI-enabled classrooms. It discusses how AI technologies will continue to evolve, offering new opportunities for collaborative learning, skill development, and lifelong education. In conclusion, this comprehensive study underscores the profound impact of AI on education and the need for thoughtful implementation strategies that prioritize equity, inclusivity, and ethical considerations. By harnessing the potential of AI, education systems can better prepare learners for the challenges and opportunities of the future.

Zoom fatigue related to online learning among medical students in Thailand: Prevalence, predictors, and association with depression

Background Amidst the COVID-19 pandemic, the learning pattern of medical students shifted from onsite to online. This transition may contribute to what has been called “Zoom fatigue.” This study aimed to evaluate the prevalence of Zoom fatigue related to online learning, identify associated factors of Zoom fatigue, and explore its correlation with depression among medical students during the COVID-19 pandemic. Methods This cross-sectional study was conducted among 1st to 6th-year Thai medical students. The online survey was administered using a demographic and health behavior questionnaire, the Patient Health Questionnaire-9 (PHQ-9), and the Thai version of the Zoom Exhaustion &amp; Fatigue Scale (ZEF-T). Results Among the 386 participating students, 221 (57%) were female, with a mean age of 20.6 years. The prevalence of high Zoom fatigue was 9.6%. In the multivariable regression analysis, a lower academic year and a higher number of online learning sessions were significant predictors of Zoom fatigue (p &lt; 0.001), while regular exercise emerged as a protective factor (p = 0.009). The prevalence of depressive disorder was 61.9%, and a significant correlation was found between having a depressive disorder and experiencing Zoom fatigue (p = 0.004). Conclusion Zoom fatigue among medical students was correlated with depression. Consequently, medical students experiencing Zoom fatigue should undergo further assessment for depression. It is crucial to closely monitor medical students in lower academic years with a high number of online sessions for signs of Zoom fatigue. Additionally, implementing strategies, such as reducing the frequency of online sessions and promoting regular exercise, may help alleviate the symptoms.

How Are Students Learning Patterns Influenced by the CEE

The College Entrance Exam (the CEE) in China has been in operation for several decades. However, there are increasing complaints about this education system in recent years, claiming the CEE can harm students learning patterns. This article aims to review the students affected and changed learning patterns in the context of Chinese College Entrance Exam (CEE) as well as Chinese cultural background. First, this paper introduces the basic information about the CEE and its score-oriented system, in order to better analyse in the following contents. Second, this paper describes how scholars define a learning pattern and what can influence a learning pattern. Then, the paper points out the gap that few researchers have explored how students learning patterns can be influenced by such an exam system. Lastly, after reviewing past papers, the results show that learning patterns of students can be negatively influenced either directly or indirectly in the context of the CEE. This study shows the importance of an education system as well as parenting or teaching styles in affecting the students learning habits and skills like critical thinking. Findings from this study also provide new insights for instructors and learners to optimize the learning patterns.

Analyzing novice and competent programmers' problem-solving behaviors using an automated evaluation system

Background and ContextIn today's tech-driven world, programming courses are crucial. Yet, teaching programming is challenging, leading to high student failure rates. Understanding student learning patterns is key, but there's a lack of research utilizing tools to automatically collect and analyze interaction data for insights into student performance and behaviors. ObjectivesStudy aims to compare problem-solving behaviors of novice and competent programmers during coding tests, identifying patterns and exploring relationships with program correctness. MethodWe built an online system with programming challenges to collect behavior data from novice and competent programmers. Our system analyzed data using various metrics to explore behavior-program correctness relationships. FindingsAnalysis showed distinct problem-solving behavior patterns. Competent programmers had fewer syntax errors, spent less time fixing bugs, and had higher program correctness. Novices made more syntax errors and spent more time fixing coding errors. Both groups used tabs for code structure, but competent programmers introduced unfamiliar variables more often and commented on them afterward. Emphasizing iterative revisions and active engagement enhances problem-solving skills and programming proficiency. Radar charts are effective for identifying improvement areas in teaching programming. The relationship between behavior and program correctness was positively correlated for competent programmers but not novices. ImplicationsStudy findings have implications for programming education. Radar charts help teachers identify course improvement areas. Novices can learn from competent programmers' behavior. Instructors should encourage continuous skill improvement through revisions and engagement. Identified unfamiliar programming aspects offer insights for targeted learning.

A review of machine learning methods used for educational data

AbstractIntegrating machine learning (ML) methods in educational research has the potential to greatly impact upon research, teaching, learning and assessment by enabling personalised learning, adaptive assessment and providing insights into student performance, progress and learning patterns. To reveal more about this notion, we investigated ML approaches used for educational data analysis in the last decade and provided recommendations for further research. Using a systematic literature review (SLR), we examined 77 publications from two large and high-impact databases for educational research using bibliometric mapping and evaluative review analysis. Our results suggest that the top five most frequently used keywords were similar in both databases. The majority of the publications (88%) utilised supervised ML approaches for predicting students’ performances and finding learning patterns. These methods include decision trees, support vector machines, random forests, and logistic regression. Semi-supervised learning methods were less frequently used, but also demonstrated promising results in predicting students’ performance. Finally, we discuss the implications of these results for statisticians, researchers, and policymakers in education.

THE EFFECT OF DIGITALIZATION EDUCATION USING ELECTRONIC LEARNING MEDIA ON STUDENT LEARNING PATTERNS

Science and Technology (IPTEK) is developing very rapidly in various fields, one of which is in the world of education which is known as the digitalization of education. One of the characteristics of digitalization of education is the use of electronic learning media in learning. Examples of electronic learning media that have been widely developed among students include online simulations using Phet Colorado, digital pocket books, virtual classes using video conference, and others. The digitalization of education has an impact on student learning patterns. Therefore, it is necessary to conduct research to determine use of electronic learning media as a form of digitalization of education on student learning patterns. The research uses a descriptive qualitative method using literature study. Digitalization of education provides many benefits to students' learning patterns. Based on the results and discussion, it was found that digitalization of education has an effect on student learning patterns, namely that learning is more interactive and efficient because it can be done anywhere and at any time. However, it should be noted that in the digital era there needs to be a role for families, teachers and the community to supervise and direct students in using technology.

Implementation of Learning Innovations in Society 5.0 to Stimulate Creative Thinking Skills at Higher Education

The high need for the internet for reference is increasingly changing student learning patterns. In the era of Society 5.0, learning models need innovation to keep up with technological developments. This study aims to describe the influence between Society 5.0 learning innovations on creative thinking skills in higher education. Respondents in this study amounted to 50 students from the Accounting, Management and Economic Education Study Programs who took the Introduction of Management course. The data collection technique used an e-questionnaire. Data analysis using multiple linear regression analysis. The results showed that learning innovations of Society 5.0 consisting of learning model innovations; learning media innovations; learning strategy innovations; and assignment innovations had an effect on student creative thinking skills.

An Efficient Hybrid Deep Learning Framework for Predicting Student Academic Performance

Introduction: educational data analysis with data mining techniques for enhanced learning is increasing. Voluminous data available through institutions, online educational resources and virtual educational courses could be useful in tracking learning patterns of students. Data mining techniques could be helpful for predicting students’ academic performance from raw data. Conventional Machine Learning (ML) techniques have so far been widely used for predicting this. Methods: however, research available on the Convolutional Neural Networks (CNNs) architecture is very scarce in the context of the academic domain. Therefore, in this work a hybrid CNN model involving 2 different CNN models for forecasting academic performance. The one-dimensional data is converted into two-dimensional equivalent to determine efficiency of the hybrid model which is subsequently compared with many existing. Result: the experimental results are evaluated using various performance metrics like precision, accuracy, recall and F-Score. Conclusion: the proposed hybrid model outperforms-Nearest Neighbour (K-NN), Decision Trees (DTs), and Artificial Neural Network (ANN) in terms of precision, accuracy, recall and F-Score

Exploring Active Learning Patterns of Technical and Vocational Education Training Students in Practical Activities Based on Internet of Things

Exploring Active Learning Patterns of Technical and Vocational Education Training Students in Practical Activities Based on Internet of Things

Promoting teacherpreneurship in creative education through Instagram

Teacherpreneurs use their high risk-taking entrepreneurial leadership skills to create an innovative classroom using modern technologies. Given the changing learning patterns of modern-day students, and the influence of social media in highlighting their creative achievements, Being a popular social media platform, Instagram has the potential to play a valuable role in teaching and learning, particularly in the field of creative education. The objective of this paper is to explore the role of Instagram in promoting teacherpreneurship in tertiary level creative education. Using a digital ethnography approach, Sri Lankan teacherpreneurs’ practices were explored. Higher education plays a critical role in Sri Lankan economic development and has demonstrated it’s potential to develop creative industries. A purposive sample of twelve participants were observed and interviewed over a period of two months. Qualitative content analysis was used to understand this rich visual and textual data. It was revealed that Instagram promotes teacherpreneurship by facilitating edutainment (education + entertainment) via a flipped classroom pedagogy, breaking away from hierarchical structures and snail pace policy changes, fostering innovation, diversifying professional pathways and careers and seamless connections in and out of cyberspace.