[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120323-en":3,"doc-seo-120323-105":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},120323,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",6,"Technology","Course Portfolio: CSCE 478/878 Introduction to Machine Learning - Course Components and Peer Review Reflection","Course portfolio for CSCE 478/878: Introduction to Machine Learning developed through the Faculty-led Inquiry into Reflective and Scholarly Teaching (FIRST) project at the University of Nebraska–Lincoln. The course introduces foundational machine learning concepts, models, and programming tools for undergraduate and graduate learners across diverse academic backgrounds. Offered in Spring 2025, it includes peer review to support continuous teaching improvement and alignment to learning outcomes. The portfolio documents objectives, course structure, teaching strategies, resources, student activities, and formal assessments with peer feedback, then concludes with critical reflection based on assessment data and observations.","University of Nebraska-Lincoln  \nDigitalCommons@University of Nebraska-Lincoln  \n\n| UNL Faculty Course Portfolios | Faculty-led Inquiry into Reflective and Scholarly Teaching (FIRST) |\n| --- | --- |\n| Spring 5-25-2025\u003Cbr>Course Portfolio: CSCE 478/878 Introduction to Machine Learning\u003Cbr>Hongzhi Guo\u003Cbr>University of Nebraska-Lincoln, [hguo10@unl.edu](hguo10@unl.edu)\u003Cbr>Follow this and additional works at: [https://digitalcommons.unl.edu/prtunl](https://digitalcommons.unl.edu/prtunl)\u003Cbr> Part of the Curriculum and Instruction Commons, Higher Education Commons, and the Higher Education and Teaching Commons |  |\n\nGuo, Hongzhi, \"Course Portfolio: CSCE 478/878 Introduction to Machine Learning\" (2025) . UNL Faculty Course Portfolios. 252.  \n[https://digitalcommons.unl.edu/prtunl/252](https://digitalcommons.unl.edu/prtunl/252)  \nThis Portfolio is brought to you for free and open access by the Faculty-led Inquiry into Reflective and Scholarly Teaching (FIRST) at DigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in UNL Faculty Course Portfolios by an authorized administrator of DigitalCommons@University of Nebraska-Lincoln.  \nCourse Portfolio: CSCE 478/878 Introduction to  \nMachine Learning  \nHongzhi Guo  \nSchool of Computing  \nUniversity of Nebraska-Lincoln  \n[hguo10@unl. edu](hguo10@unl. edu)  \nMay 25, 2025  \nAbstract  \nThis course portfolio was developed for CSCE 478/878: Introduction to Machine Learning, offered by the School of Computing at the University of Nebraska–Lincoln, as part of the Faculty-led Inquiry into Reflective and Scholarly Teaching (FIRST) project. The course provides an introduction to foundational machine learning concepts, models, and programming tools. It enrolls both undergraduate and graduate students from a wide range of academic backgrounds, including computer science, computer engineering, software engineering, biological systems engineering, chemical engineering, business-related fields, etc. Offered in Spring 2025, the course was peer-reviewed to support continuous improvement in teaching. The primary goals of this portfolio are to enhance the instructor’s teaching practices and contribute to the development of high-quality course materials for the broader curriculum. This document outlines key components of the course, including its objectives, structure, teaching strategies, instructional resources, and student activities. In addition to presenting the course content, the portfolio evaluates student learning outcomes through formal assessmentsand peer feedback. The diversity of the student cohort informed efforts to tailor the course design and pedagogy to a range of learning needs. The final section of the portfolio offers a critical reflection on the course’s effectiveness, which identifies strengthsand areas for improvement based on assessment data, peer observations, and the instructor’s own experiences.  \nKeywords: Machine learning, inquiry-based learning, peer evaluation, learning outcomes.  \nContents  \n1 Objectives of Peer Review Course Portfolio 3  \n2 Description of the Course 3  \n3 Teaching Methods/Course Materials/Course Activities 4  \n4 Analysis of Student Learning 6  \n5 Reflection on the Course 8  \nA Appendix A: CSCE 478/878 Course Syllabus 11  \n1 Objectives of Peer Review Course Portfolio  \nThis course portfolio was developed as part of the Faculty-led Inquiry into Reflective and Scholarly Teaching (FIRST) project at the University of Nebraska-Lincoln. It documents and reflects upon the design, delivery, and impact of CSCE 478/878 Introduction to Machine Learning, a course offered by the School of Computing with a large number of enrollments. The portfolio serves as both a self-assessment and a means of engaging in peer-informed teaching improvement to promote effective, inclusive, and evidence-based teaching practices.  \nThe primary objective of this peer-reviewed course portfolio is to enhance the instructor’s reflective practice and to inform continuous i","cbCaivVD4k3Acd8Z","https://ap.wps.com/l/cbCaivVD4k3Acd8Z","pdf",323046,1,15,"English","en",105,"# Objectives of Peer Review Course Portfolio\n## Description of the Course\n## Teaching Methods/Course Materials/Course Activities\n## Analysis of Student Learning\n## Reflection on the Course\n## Appendix A: CSCE 478/878 Course Syllabus","[{\"question\":\"What is the purpose of the CSCE 478/878 course portfolio?\",\"answer\":\"The portfolio enhances the instructor’s reflective practice and supports continuous improvement of the course by documenting design, delivery, and impact.\"},{\"question\":\"What topics and tools does the course introduce?\",\"answer\":\"The course introduces foundational machine learning concepts, models, and programming tools, supported by course materials and instructional strategies.\"},{\"question\":\"How are student learning outcomes evaluated in the portfolio?\",\"answer\":\"Student learning outcomes are assessed through formal assessments and complemented by peer feedback, with results used to inform subsequent reflection and improvement.\"}]","Course Portfolio: CSCE 478/878 Introduction to Machine Learning - 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