Relevant bibliographies by topics / Student clustering

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Student clustering'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Student clustering"

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Bani Riyan, Ade, Mochamad Fikri Rifai, and Christina Juliane. "Analysis and Design of Student Point Systems to Improve Student Achievement using The Clustering Method." Journal of World Science 2, no. 3 (2023): 597–603. http://dx.doi.org/10.58344/jws.v2i3.155.

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Introduction: The student points system is an application for recording students' achievement and offense points. The lack of recording and dissemination of information on achievement results makes students less motivated to improve achievement, and the distribution of scholarships for outstanding students is inappropriate. To improve student achievement, an application program is needed that can record and disseminate student achievement data in real-time, accurate, and effective. So, the purpose in this study is to know and analyze the design of the student point system to improve student ac
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Mawarni, Qorik Indah, and Eko Setia Budi. "Implementasi Algoritma K-Means Clustering Dalam Penilaian Kedisiplinan Siswa." Jurnal Sistem Komputer dan Informatika (JSON) 3, no. 4 (2022): 522. http://dx.doi.org/10.30865/json.v3i4.4242.

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Education has a very important role for students, not just potential but noble character in the form of discipline, therefore it is necessary to group each school based on student discipline. Implementing a clustering system with the K-means method which is used to classify and determine the value of student discipline which produces a clustering output of student discipline that is beneficial for the school to prevent students from misbehaving early on. Analysis of data needs used in this study in the form of primary data obtained from a questionnaire given to students. The attributes used ar
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Qomariyah and Maria Ulfah Siregar. "Comparative Study of K-Means Clustering Algorithm and K-Medoids Clustering in Student Data Clustering." JISKA (Jurnal Informatika Sunan Kalijaga) 7, no. 2 (2022): 91–99. http://dx.doi.org/10.14421/jiska.2022.7.2.91-99.

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Universities as educational institutions have very large amounts of academic data which may not be used properly. The data needs to be analyzed to produce information that can map the distribution of students. Student academic data processing utilizes data mining processes using clustering techniques, K-Means and K-Medoids. This study aims to implement and analyze the comparison of which algorithm is more optimal based on the cluster validation test with the Davies Bouldin Index. The data used are academic data of UIN Sunan Kalijaga students in the 2013-2015 batch. In the k-Means process, the
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Agung, Triayudi, Oktri Widyarto Wahyu, Kamelia Lia, Iksal, and Sumiati. "CLG clustering for dropout prediction using log-data clustering method." International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (2021): 764–70. https://doi.org/10.11591/ijai.v10.i3.pp764-770.

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Implementation of data mining, machine learning, and statistical data from educational department commonly known as educational data mining. Most of school systems require a teacher to teach a number of students at one time. Exam are regularly being use as a method to measure student’s achievement, which is difficult to understand because examination cannot be done easily. The other hand, programming classes makes source code editing and UNIX commands able to easily detect and store automatically as log-data. Hence, rather that estimating the performance of those student based on this lo
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Li, Li, Xiangfeng Luo, and Haiyan Chen. "Clustering Students for Group-Based Learning in Foreign Language Learning." International Journal of Cognitive Informatics and Natural Intelligence 9, no. 2 (2015): 55–72. http://dx.doi.org/10.4018/ijcini.2015040104.

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Big data make it possible to mine learning information for insights regarding student performance in foreign language learning (FLL). Group-based learning is a usual method to improve FLL, whose effectiveness is greatly influenced by student groups. The general grouping method is to divide students into groups by their teacher manually, which is not timely or accurate. To overcome the shortcomings of manual methods, this paper proposes an automatic grouping method based on clustering technologies. First, the student profile is built to model the student's knowledge level, which can be updated
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Yin, XueHong. "Construction of Student Information Management System Based on Data Mining and Clustering Algorithm." Complexity 2021 (May 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/4447045.

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Data mining is a new technology developed in recent years. Through data mining, people can discover the valuable and potential knowledge hidden behind the data and provide strong support for scientifically making various business decisions. This paper applies data mining technology to the college student information management system, mines student evaluation information data, uses data mining technology to design student evaluation information modules, and digs out the factors that affect student development and the various relationships between these factors. Predictive assessment of knowled
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Asroni, Asroni, Dita Kurniasari, and Aprilia Kurnianti. "The Implementation of Clustering Method With K-Means Algorithm In Grouping Data of Students’ Course Scores at Universitas Muhammadiyah Yogyakarta." Emerging Information Science and Technology 1, no. 3 (2020): 75–83. http://dx.doi.org/10.18196/eist.v1i3.13172.

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Student grades can be a reference. A large number of student grade data in a university causes data accumulation; thus, data are grouped with data mining. This study aims to classify student grade data in the second semester. Grouping student grade data was performed using the clustering method with the K-means algorithm. The research data were derived from the database of Universitas Muhammadiyah Yogyakarta. The data were students’ grades in the academic years of 2010/2011, 2011/2012, 2012/2013, 2013/2014, and 2014/2015. The analysis process was carried out using WEKA software, SQL Server 201
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Wang, Zhihui. "Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm." Computational Intelligence and Neuroscience 2022 (July 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/4703975.

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Monitoring and guiding instructional management require student performance evaluation. Traditional evaluation and analysis methods based on absolute scores, on the other hand, have certain flaws and are unable to fully reflect the information contained in student performance, thus limiting the impact of student performance evaluation on teaching and learning management. Data mining is regarded as the backbone technology for future information processing, and it introduces a new concept to the way humans use data. Schools must analyse and evaluate the performance of students in the same grade
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Purba, Winda Nia, Rinaldi Syahputra, Fine Reza Nainggolan, and Gabriel Immanuel Manullang. "IMPLEMENTASI DATA MINING CLUSTERING DALAM MENGUKUR KEPUASAN TERHADAP PELAYANAN PERPUSTAKAAN DI UNIVERSITAS PRIMA INDONESIA." Jurnal Teknik Informasi dan Komputer (Tekinkom) 7, no. 1 (2024): 318. https://doi.org/10.37600/tekinkom.v7i1.1213.

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This research aims to measure student satisfaction with Prima Indonesia University library services using data mining methods. Clustering techniques are used to group student satisfaction data based on various attributes such as service quality, resource availability, facility comfort, and interaction with library staff. Data was collected through questionnaires distributed to students. The clustering results revealed significant patterns in student satisfaction, which were analyzed to identify key factors influencing satisfaction levels. These results provide library managers with valuable in
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Li, Guozhang, Rayner Alfred, and Xue Wang. "Student Behavior Analysis and Research Model Based on Clustering Technology." Mobile Information Systems 2021 (November 5, 2021): 1–6. http://dx.doi.org/10.1155/2021/9163517.

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Now, entering the 21st century, with the continuous improvement of my country’s higher education level, the enrollment rate of all colleges and universities across the country is increasing year by year. Faced with the information management of a large number of students, the workload and work pressure of consultants at various universities have doubled. The rapid and effective development of modern computer software and hardware has also initiated and effectively developed the informatization process of universities. The student management system is the core and foundation of the entire schoo
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Dissertations / Theses on the topic "Student clustering"

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Von, Tish Kelsey Leigh. "Interpretation and clustering of handwritten student responses." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/77003.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 81-82).<br>This thesis presents an interpretation and clustering framework for handwritten student responses on tablet computers. The ink analysis system is able to capture and interpret digital ink strokes for many types of classroom exercises, including graphs, number lines, and fraction shading problems. By approaching the problem with both online and offline ink interpretation methods, releva
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Lindell, Johan. "Identifying student stuck states in programmingassignments using machine learning." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-103993.

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Intelligent tutors are becoming more popular with the increased use of computersand hand held devices in the education sphere. An area of research isinvestigating how machine learning can be used to improve the precision andfeedback of the tutor. This thesis compares machine learning clustering algorithmswith various distance functions in an attempt to cluster together codesnapshots of students solving a programming task. It investigates whethera general non-problem specific implementation of a distance function canbe used to identify when a student is stuck solving an assignment. Themachine l
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Singelmann, Lauren Nichole. "Using Classification and Clustering to Predict and Understand Student Behavior in an Innovation-Based Learning Course." Thesis, North Dakota State University, 2020. https://hdl.handle.net/10365/31885.

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One of the Grand Challenges for Engineering is advancing personalized learning, but challenges remain to identify and understand potential student pathways. This is especially difficult in complex, open-ended learning environments such as innovation-based learning courses. Student data from an iteration of an innovation-based learning course were analyzed using two educational data mining techniques: classification and clustering. Classification was used to predict student success in the course by creating a model that was both interpretable and robust (accuracy over 0.8 and ROC AUC of over 0.
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Carbone, Rego Felipe. "Exploring and Identifying Student Engagement and Performance Profiles in A Learning Environment." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23250.

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Many studies illustrate the potential of utilizing data and advanced analytics techniques in specific and particular educational and learning settings. Much less focus is devoted to applying broader and more general approaches to exploratory data analysis in the field of Learning Analytics (LA). This thesis presents an additional contribution in this space. It demonstrates a general approach to exploring and predicting students’ profiles in an online learning setting. Its intention is to contribute to the growing literature of exploratory research in the field of LA by applying robust approa
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MAQSOOD, RABIA. "ANALYZING AND MODELING STUDENTS¿ BEHAVIORAL DYNAMICS IN CONFIDENCE-BASED ASSESSMENT." Doctoral thesis, Università degli Studi di Milano, 2020. http://hdl.handle.net/2434/699383.

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Confidence-based assessment is a two-dimensional assessment paradigm which considers the confidence or expectancy level a student has about the answer, to ascertain his/her actual knowledge. Several researchers have discussed the usefulness of this model over the traditional one-dimensional assessment approach, which takes the number of correctly answered questions as a sole parameter to calculate the test scores of a student. Additionally, some educational psychologists and theorists have found that confidence-based assessment has a positive impact on students’ academic performance, knowledge
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Іскандарова-Мала, Анастасія Оруджівна, та Anastasiya Iskandarova-Mala. "Інформаційна технологія забезпечення контролю якості знань". Thesis, Національний авіаційний університет, 2021. https://er.nau.edu.ua/handle/NAU/48948.

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У дослідженні запропоновано підходи до забезпечення контролю якості знань.У результаті теоретичного аналізу предметної області розкрито поняття «сучасна теорія тестів» та її переваги по відношенню до стандартного класичного підходу; проведено порівняння існуючих підходів до побудови рейтингів учнів та з’ясовано, які методи використовуються для групування та дослідження результатів зрізів знань. Запропоновано методи оцінки якості тестів та їх результатів на основі вдосконалення побудови профілів завдань. І як наслідок, приведено алгоритм налагодження адаптивного тестування та обчислення склад
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Kim, Young Il. "Essays on Volatility Risk, Asset Returns and Consumption-Based Asset Pricing." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211912340.

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Al-Jabri, Sameer S. "The effects of semantic and thematic clustering on learning English vocabulary by Saudi students." Open access to IUP's electronic theses and dissertations, 2005. http://hdl.handle.net/2069/52.

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Shaari, Ahmad Jelani. "The Interactive Effects of Color Realism, Clustering, and Age on Pictorial Recall Memory among Students in Malaysia." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30458.

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This study investigates the effects of clustering or format of presentation (categorized and uncategorized lists), level of color realism of graphics (color pictures, black and white pictures and line drawings), and age (10 year old, 16 year old and adults) on the pictorial recall memory among students in Malaysia. Three hundred sixty students of three age groups were randomly assigned to one of the six stimulus treatments (categorized color, uncategorized color, categorized black and white, uncategorized black and white, categorized line drawing, and uncategorized line drawing).<P> There was
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Crissman, Jennifer Lynne. "The impact of clustering freshman seminars with English composition courses on new students' grade point average and retention rates." Adobe Acrobat reader required to view the full dissertation, 1999. http://www.etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-8/index.html.

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Books on the topic "Student clustering"

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Grigor'ev, Anatoliy, and Evgeniy Isaev. Methods and algorithms of data processing. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.

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The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects).&#x0D; The second edition is supplem
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Bessonov, Aleksey. The study of criminal activity using artificial intelligence. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2195488.

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The monograph describes the technology of building digital crime models, including the preparation of data on criminal acts for study using mathematical statistics and artificial intelligence methods, the features of studying such data through various artificial intelligence methods, including neural networks, gradient boosting, decision trees, random forest, clustering, etc. Special attention is paid to the use of mathematical statistics and artificial intelligence methods in the study of serial crimes in science and practice. It is intended for scientists and practitioners of law enforcement
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Peebles, P. J. E. The Large-Scale Structure of the Universe. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691209838.001.0001.

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An instant landmark on its publication, this book remains the essential introduction to this vital area of research. Written by one of the world's most esteemed theoretical cosmologists, it provides an invaluable historical introduction to the subject, and an enduring overview of key methods, statistical measures, and techniques for dealing with cosmic evolution. With characteristic clarity and insight, the author focuses on the largest known structures — galaxy clusters — weighing the empirical evidence of the nature of clustering and the theories of how it evolves in an expanding universe. A
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Book chapters on the topic "Student clustering"

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Varela, Noel, Edgardo Sánchez Montero, Carmen Vásquez, et al. "Student Performance Assessment Using Clustering Techniques." In Data Mining and Big Data. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9563-6_19.

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Tanai, Mirwais, Jongwan Kim, and Joong Hyuk Chang. "Model-Based Clustering Analysis of Student Data." In Convergence and Hybrid Information Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24082-9_81.

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Genova, Vincenzo Giuseppe, Giuseppe Giordano, Giancarlo Ragozini, and Maria Prosperina Vitale. "Clustering Student Mobility Data in 3-Way Networks." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_17.

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AbstractThe present contribution aims at introducing a network data reduction method for the analysis of 3-way networks in which classes of nodes of different types are linked. The proposed approach enables simplifying a 3-way network into a weighted two-mode network by considering the statistical concept of joint dependence in a multiway contingency table. Starting from a real application on student mobility data in Italian universities, a 3-way network is defined, where provinces of residence, universities and educational programmes are considered as the three sets of nodes, and occurrences
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Susanti, Martini Dwi Endah, and Rindu Puspita Wibawa. "Clustering Student Understanding Levels In Software Engineering Courses." In Advances in Economics, Business and Management Research. Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-525-6_76.

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Salles, Isadora, Paola Mejia-Domenzain, Vinitra Swamy, Julian Blackwell, and Tanja Käser. "Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection." In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64315-6_35.

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Mohd, Wan Maseri Wan, A. H. Beg, Tutut Herawan, Ahmad Noraziah, and Haruna Chiroma. "Multi-dimensional K-Means Algorithm for Student Clustering." In Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015). Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1799-6_14.

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Meng-Yang. "Student Card Consumption Behavior Based on Clustering Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4258-6_200.

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Bhurre, Shraddha, Sunny Raikwar, Shaligram Prajapat, and Deepika Pathak. "Analyzing and Comparing Clustering Algorithms for Student Academic Data." In Advances in Intelligent Systems and Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47508-5_49.

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Flamia Azevedo, Beatriz, Ana Maria A. C. Rocha, Florbela P. Fernandes, Maria F. Pacheco, and Ana I. Pereira. "Evaluating Student Behaviour on the MathE Platform - Clustering Algorithms Approaches." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-24866-5_24.

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Giri, Anish, Lakshmi Anand, Malavika P. Pillai, A. Sherin, and K. S. Krishnaveni. "A graph clustering model to predict student retention using DAG." In Smart Computing. CRC Press, 2021. http://dx.doi.org/10.1201/9781003167488-28.

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Conference papers on the topic "Student clustering"

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Qiu, Shuang, Qingzhong Gao, Yujiao Wang, and Qihang Zhao. "Hierarchical Clustering Algorithm Based User Group Structure Segmentation Method." In 2024 IEEE 7th Student Conference on Electric Machines and Systems (SCEMS). IEEE, 2024. http://dx.doi.org/10.1109/scems63294.2024.10756462.

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Yanti, Novi, Sarjon Defit, and Okfalisa. "Clustering the Academician Knowledge Qualification: K-Means for Performance Measurement Analysis." In 2024 IEEE 22nd Student Conference on Research and Development (SCOReD). IEEE, 2024. https://doi.org/10.1109/scored64708.2024.10872735.

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Masbahah, Trisna Ari Roshinta, Darmawan Lahru Riatma, Yusuf Fadlila Rachman, and Sevyra Nanda Octavianti. "Comparison of Clustering Algorithms with Categorical Data in Profiling Student Characteristics." In 2024 Beyond Technology Summit on Informatics International Conference (BTS-I2C). IEEE, 2024. https://doi.org/10.1109/bts-i2c63534.2024.10941977.

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Siahaan, Steven Gianmarg Haposan, and Masayu Leylia Khodra. "Sentence Embedding-based Soft Clustering for Analyzing Texts of Student Outcomes." In 2024 IEEE International Conference on Data and Software Engineering (ICoDSE). IEEE, 2024. https://doi.org/10.1109/icodse63307.2024.10829879.

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Gao, Jiaying, Fausto Giunchiglia, Tongyu Zhao, and Hao Xu. "Annealing Distillation Algorithm for Transferring Unsupervised Clustering Knowledge to Supervised Student Models." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890534.

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Sun, Xiaoxia, Yuxia Liu, and Qidi Lu. "Construction of University Student Information Management System Based on Constrained Clustering Algorithm." In 2025 International Conference on Digital Analysis and Processing, Intelligent Computation (DAPIC). IEEE, 2025. https://doi.org/10.1109/dapic66097.2025.00138.

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Senen, Adri, Nadiatuljanah, and Ginas Alvianingsih. "Application of K-Means Clustering in The Determination of Electrical Profile Characteristics Based on Geographic Information System (GIS)." In 2024 IEEE 22nd Student Conference on Research and Development (SCOReD). IEEE, 2024. https://doi.org/10.1109/scored64708.2024.10872743.

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Otey, Jood, Laura Biester, and Steven R. Wilson. "Representing and Clustering Errors in Offensive Language Detection." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-srw.36.

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Udhan, Tejal. "Emotion Recognition using Fuzzy Clustering Analysis." In GS4 Student Scholars Symposium 2016. Georgia Southern University, 2016. https://doi.org/10.20429/gs4.2016.038.

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Vadivelou, G., E. Ilavarasan, and S. Brinda. "Dynamic Discovery of Web Services using WSDL Clustering." In Student Research Symposium 2012. Research Publishing Services, 2012. http://dx.doi.org/10.3850/978-981-07-3043-7_085.

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Reports on the topic "Student clustering"

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Collins, Abigail. Predicting Student Success at a Large State University using Multiple Linear Regression and Hierarchical Clustering. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-1143.

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Bednall, Timothy. A Gentle Introduction to Python. Instats Inc., 2023. http://dx.doi.org/10.61700/ywg7hgz3gf12y469.

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This seminar teaches the basics of Python without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work, manipulate data, and create figures/plots. We will also showcase basic examples of using Python for prediction with regression analysis, classification, dimensionality reduction, and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Bednall, Timothy. A Gentle Introduction to Python. Instats Inc., 2023. http://dx.doi.org/10.61700/oma5ikdj8xru1469.

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This seminar teaches the basics of Python without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work, manipulate data, and create figures/plots. We will also showcase basic examples of using Python for prediction with regression analysis, classification, dimensionality reduction, and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Bednall, Timothy. A Gentle Introduction to R. Instats Inc., 2022. http://dx.doi.org/10.61700/nkdwj37n3trpc469.

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This seminar teaches the basics of R without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work using R Markdown, manipulate data using the tidyverse, and create great figures using ggplot2. We will also showcase basic examples of using R for prediction, classification, dimensionality reduction and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Bednall, Timothy. A Gentle Introduction to R. Instats Inc., 2022. http://dx.doi.org/10.61700/8851t6mqarw95469.

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This seminar teaches the basics of R without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work using R Markdown, manipulate data using the tidyverse, and create great figures using ggplot2. We will also showcase basic examples of using R for prediction, classification, dimensionality reduction and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.
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Raykov, Tenko. Latent Class Analysis and Mixture Modeling. Instats Inc., 2023. http://dx.doi.org/10.61700/tkd5fah8evykd469.

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Abstract:
Latent class analysis (LCA) and mixture models (MM) are an applied statistical method for examining heterogeneity in studied populations. The method can be used to evaluate whether a studied population consists of an initially unknown number of several subpopulations (latent classes, types, clusters) that differ in important ways. This workshop introduces participants to the general field of classification (clustering), using LCA as a model-based version of cluster analysis and moving on to more general mixture modeling with latent variables. Hands-on examples with best practices for analysis
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Пахомова, О. В. Using Scaffolding Strategy for Teaching Creative Writing. Маріупольський державний університет, 2018. http://dx.doi.org/10.31812/0564/2145.

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Abstract:
The article deals with scaffolding strategy for teaching creative writing in the English classroom. The importance of using the creative writing technique, which is an effective means of optimization and intensification of the process of foreign language study, for forming students' communicative competence in writing is highlighted. It is supposed that an elaborated scaffolding strategy might help lecturers to organize the educational process with maximum capacity and successful results. A variety of techniques such as intensive usage of graphic organizers ("Plan Think Sheet", "Mind-map", "Co
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