[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125717-en":3,"doc-seo-125717-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125717,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Understanding Student Perceptions of Flipped Linear Algebra Classrooms via Interpretable Machine Learning","Flipped classroom pedagogy is widely adopted in undergraduate mathematics, but it is unclear whether students experience it in systematically different ways. This study analyzes student perceptions from an introductory linear algebra course using survey data collected across multiple semesters. An interpretable machine learning approach uncovers stable perception patterns and checks their consistency under repeated analysis, revealing a clear separation when grouped by gender. The model highlights the engagement and instructional design aspects driving these differences, supporting inclusive design and the value of interpretable methods in education research.","arXiv :2309 . 16259v2 [math .HO] 8 May 2026  \nUnderstanding Student Perceptions of Flipped Linear Algebra Classrooms  \nvia Interpretable Machine Learning  \nSudip Laudari  \nDepartment of Civil Engineering  \nThe University of Sydney, New South Wales 2006, Australia  \n[sudip.laudari@sydney.edu.au](sudip.laudari@sydney.edu.au)  \nN. Karjanto  \nDepartment of Mathematics, College of Natural Science  \nSungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea Humanities, Arts, and Social Sciences Division, Underwood International College Yonsei University, Songdo, Incheon 21983, Republic of Korea  \n[natanael@skku.edu](natanael@skku.edu)  \nAbstract  \nFlipped classroom pedagogy is widely used in undergraduate mathematics to promote active learning, yet it remains unclear whether students experience it in systematically different ways. In this study, we analyze student perceptionsfrom an introductory linear algebra course using survey data collected across multiple semesters. Using an interpretable machine learning approach, we examine patterns across questionnaire responses and evaluate their consistency under repeated analysis. Our results reveal a clear and stable separation in perception patterns when grouped by gender, suggesting that these differences arise from structured combinations of factors rather than isolated responses. The model also identifies key aspects of engagement and instructional design that contribute most to this separation. These findings highlight opportunities for more inclusive flipped classroom design and demonstrate the value of interpretable methods in educational research.  \n1. Introduction  \nAs educators, we aim to support students’ learning by promoting knowledge retention, problem-solving ability, and critical thinking skills. Active learning approaches have received increasing attention as alternatives to traditional lecture-based instruction. The flipped classroom has emerged as one of the most widely adopted pedagogical models within this framework [1, 2, 14] . In a flipped classroom, students review new material before class through  \nvideos or readings. This allows in-class time to be devoted to discussion, problem solving, and other higherorder learning activities.  \nActive learning strategies have been shown to enhance instructional effectiveness and student engagement across many disciplines. In mathematics education, and particularly in linear algebra, flipped classrooms have been implemented using a variety of instructional designs [32] . Empirical comparisons between flipped and traditional instruction frequently report improvements in conceptual understanding and learning attitudes [16, 17] . Similar findings have been observed across different institutional contexts, including increased participation and improved self-directed learning skills [11, 12, 18, 21] . These outcomes are also consistent with constructivist theories which emphasize active knowledge construction and peer interaction [24] .  \nDespite strong evidence supporting flipped classrooms, much of the existing literature focuses on aggregate outcomes such as average examination performance [9, 28], overall course achievement [11, 16, 17], and broad measures of student satisfaction [22, 29] . Such analyses often treat student populations as homogeneous groups and may obscure differences in how students experience the same instructional design. Relatively little attention has been paid to whether different groups of students experience flipped learning in systematically different ways. Differences related to gender or prior preparation may remain hidden when analyses rely primarily on aggregate statistics. Understanding these differences is important for refining pedagogical design and ensuring that active learning remains inclusive [5, 7] .  \nFrom a methodological perspective, survey-based studies in education are commonly analyzed using item-byitem comparisons. While these approaches provide useful summaries, they ma","cbCaie1WEaFMq7Ie","https://ap.wps.com/l/cbCaie1WEaFMq7Ie","pdf",2197131,1,9,"English","en",105,"# Introduction\n## Background and motivation\n## Methodological perspective\n# Methodology\n## Data collection","[{\"question\":\"What problem does the study address about flipped linear algebra classrooms?\",\"answer\":\"The study examines whether students’ perceptions differ in systematic ways, rather than assuming uniform experiences across the student population.\"},{\"question\":\"How are student perceptions analyzed in this work?\",\"answer\":\"Survey responses are analyzed using an interpretable machine learning approach based on a linear SVM, supported by principal component analysis.\"},{\"question\":\"What do the results show regarding perception patterns?\",\"answer\":\"The results reveal a clear and stable separation in perception patterns when grouped by gender, indicating differences stem from structured factor combinations.\"}]","Understanding Student Perceptions of Flipped Linear Algebra Classrooms via Interpretable Machine Learning | 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