[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122004-en":3,"doc-seo-122004-105":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},122004,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Examining Course Achievement in an Undergraduate Psychology Statistics Course Through the Lens of Machine Learning Techniques - Dissertation Abstract","The introductory to psychology statistics course presents a persistent challenge for psychology majors and often functions as a gatekeeper. This dissertation advances two goals using machine learning techniques: determining the determinants of overall course achievement as reflected in course grades, and testing how statistics anxiety and statistics self-efficacy jointly shape course grades when both are present. A self-reported questionnaire measured anxiety, self-efficacy, and additional demographic and academic background variables. Using factor importance analysis with mixed numerical and categorical data, ensemble vote count and average partial dependence ranking scores were applied across regression and classification methods. The results identify statistics self-efficacy and statistics anxiety as top determinants, with self-efficacy positively related to achievement and anxiety negatively related to course grades, alongside observed gender and ethnic differences, offering actionable guidance for educators and policymakers.","Claremont Colleges  \nScholarship @ Claremont  \n\n| CGU Theses & Dissertations | CGU Student Scholarship |\n| --- | --- |\n| 2024\u003Cbr>Examining Course Achievement in an Undergraduate Psychology Statistics Course Through the Lens of Machine Learning Techniques\u003Cbr>Sunny Nguyet Le\u003Cbr>Claremont Graduate University\u003Cbr>Follow this and additional works at: [https://scholarship.claremont.edu/cgu_etd](https://scholarship.claremont.edu/cgu_etd)\u003Cbr> Part of the Education Commons, and the Mathematics Commons |  |\n\nRecommended Citation  \nLe, Sunny Nguyet. (2024) . Examining Course Achievement in an Undergraduate Psychology Statistics Course Through the Lens of Machine Learning Techniques. CGU Theses & Dissertations, 786. [https://scholarship.claremont.edu/cgu_etd/786](https://scholarship.claremont.edu/cgu_etd/786) .  \nThis Open Access Dissertation is brought to you for free and open access by the CGU Student Scholarship at Scholarship @ Claremont. It has been accepted for inclusion in CGU Theses & Dissertations by an authorized administrator of Scholarship @ Claremont. For more information, please contact [scholarship@claremont.edu](scholarship@claremont.edu).  \nEXAMINING COURSE ACHIEVEMENT IN AN UNDERGRADUATE PSYCHOLOGY STATISTICS COURSE THROUGH THE LENS OF MACHINE LEARNING TECHNIQUES  \nby  \nSunny Nguyet Le  \nClaremont Graduate University  \n2024  \nCopyright© Sunny Nguyet Le, 2024 All rights reserved  \nApproval of the Dissertation Committee  \nThis dissertation has been duly read, reviewed, and critiqued by the Committee listed below, which hereby approves the manuscript of Sunny Nguyet Le as fulfilling the scope and quality requirements for meriting the degree of Doctor of Philosophy in Mathematics and Education.  \nJohn Angus  \nClaremont Graduate University  \nProfessor of Mathematics  \nDavid Drew  \nClaremont Graduate University  \nProfessor of Education  \nCherie Ichinose  \nCalifornia State Univeristy, Fullerton  \nProfessor of Mathematics  \nQidi Peng  \nClaremont Graduate University  \nResearch Associate Professor of Mathematics  \nAbstract  \nExamining Course Achievement in an Undergraduate Psychology Statistics Course Through the  \nLens of Machine Learning Techniques  \nBy  \nSunny Nguyet Le  \nClaremont Graduate University: 2024  \nThe Introductory to Psychology Statistics course stands as a notable challenge for psychology majors, often acting as a gatekeeper course. This study embarks on two primary objectives using machine learning techniques: (1) to identify the determinants of overall course achievement, specifically course grade, and (2) to investigate the influence of statistics anxiety and statistics self-efficacy, when both are present, on overall course grade. Employing a machine-learning approach, both objectives were effectively addressed.  \nThe study involved the development of a self-reported questionnaire consisting of perceptions of statistics anxiety and statistics self-efficacy, along with other demographic and academic background variables. Conducted at California State University, Fullerton, with its diverse population of approximately 40,000, this study subjected the collected data to a factor importance analysis to assess their impact on course grades. With the dataset containing mixed data types, including numerical and categorical variables, analyzing their relative importance posed a significant challenge. To address this challenge, novel “model-free” rankings scores were employed: ensemble vote count and average partial dependence ranking score. These innovative ranking scores, applicable across regression and classification methods, as well as numerical and categorical factors, were derived from a voting ensemble comprising four competitive base learners: forward and backward stepwise subset selections, LASSO, and random forest.  \nUtilizing the ensemble vote count derived from various machine learning algorithms, this study unveils a robust methodology for identifying significant factors contributing to academic achievement ","cbCaibTJ7tOSpKhG","https://ap.wps.com/l/cbCaibTJ7tOSpKhG","pdf",3240462,1,119,"English","en",105,"# Abstract\n## Study objectives\n## Data collection\n## Machine-learning ranking approach\n## Findings and implications","[{\"question\":\"What are the two main objectives of the dissertation?\",\"answer\":\"The study aims to identify determinants of overall course achievement (course grade) and to examine how statistics anxiety and statistics self-efficacy, together, influence overall course grade.\"},{\"question\":\"How were course-related psychological factors measured?\",\"answer\":\"A self-reported questionnaire measured perceptions of statistics anxiety and statistics self-efficacy, alongside demographic and academic background variables.\"},{\"question\":\"Which factors most strongly predicted course grades, and how?\",\"answer\":\"Statistics self-efficacy and statistics anxiety emerged as the top determinants: higher self-efficacy correlated positively with academic success, while higher anxiety was associated with lower course grades.\"}]","Examining Course Achievement in an Undergraduate Psychology Statistics Course Through the Lens of Machine Learning Techniques - 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