[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125259-en":3,"doc-seo-125259-105":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},125259,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","ECONOMETRICS AND MACHINE LEARNING IN BUSINESS AND ECONOMICS EDUCATION: FACTS AND A GUIDELINE ON TEACHING PRACTICES","Econometrics and related courses are often perceived as highly challenging for undergraduate economics, business, and management students. Using a large international dataset of business and economics syllabi, the study tracks an upward trend in the inclusion of machine learning topics and a shifting emphasis away from traditional econometrics content. With enrollment growth among students from diverse backgrounds, the paper proposes actionable teaching guidelines and interventions to make econometrics more inclusive, accessible, and relevant.","ECONOMETRICS AND MACHINE LEARNING IN BUSINESS AND ECONOMICS EDUCATION: FACTS AND A GUIDELINE ON  \nTEACHING PRACTICES  \nCanh Thien Dang 1  \nAbstract  \nEconometrics, and related courses, are often thought of as the most challenging courses for many undergraduate economics, business, and management students. Using a large international dataset of business and economics syllabi, I show an upward trajectory in including machine learning topics within business syllabi, with a discernible shift of emphasis from econometrics topics. With the growing number of undergraduate students from diverse backgrounds, there is a growing need to improve the teaching of econometrics and make it more inclusive and applicable. I discuss and formalize actionable guidelines for practices and interventions that can improve econometrics teaching and make it accessible and relevant to increasingly diverse students in economics, business, and management schools.  \nKeywords:  \nJEL Classifications:  \nIntroduction  \nEconometrics is a crucial component of economics education, helping students understand and analyze real-world economic phenomena. However, the complexity and technicality of econometric concepts can sometimes make it a challenging subject for many undergraduate students to grasp (Conaway et al., 2018a) . As the number of undergraduate students from diverse backgrounds continues to increase, there is a growing need to improve the teaching of econometrics and make it more inclusive. Another challenge is the growing importance of machine learning in the field of economics and the labor market demand (Athey, 2019; Ishhakov et al., 2020) . It is imperative to offer a robust and nuanced education framework in econometrics and related courses such as data analytics and quantitative methods, and in business and economics education for pedagogical purposes and student employability. In this paper, I analyze data from a large number of syllabi from international sources to discuss the current trends of econometrics topics being taught in business and economics programs in comparison with the popular machine learning topics. Recognizing the disparities between the taught topics and an increasingly diverse cohort of economics and business students, I formulate guidelines for practices and interventions that can enhance the teaching of econometrics and promote a sense of belonging for diverse cohorts of undergraduate students.  \n1 Assistant Professor (Lecturer) of Economics, Department of Economics, King’s Business School, King’s College London, Bush House, 30 Aldwych, London WC2B 4BG UK  \nTo explore key topics in econometrics and machine learning introduced to economics and business students, I explored data from Open Syllabus to identify key topics in econometrics being taught in business and economics curricula worldwide. I focus the analysis on five keywords: “machine learning”,“causality”,“prediction”,“diversity” and “econometrics”, to identify the key trends in the economics curriculums around the world and particularly in English-taught courses. Considering the present-day requirements of economics students seeking to enter the labor market equipped with a robust set of analytical competencies for handling frequently unstructured data derived from businesses, I present an analysis of the pragmatic measures necessary to integrate these topics into econometrics syllabi. The data demonstrates an upward trajectory in the inclusion of machine learning topics within business syllabi, while thereis a decline in the presence of econometrics-related subjects across all countries. A discernible shift of emphasis from econometrics to machine learning emerges within the business curriculum.  \nI then evaluate my own experiences in delivering undergraduate econometrics courses indifferent programs at two leading universities in the UK. Despite the increasing importance of econometrics in recent years, the way it is taught in introductory courses has remained largel","cbCailMJg9ngCmZH","https://ap.wps.com/l/cbCailMJg9ngCmZH","pdf",1060496,1,18,"English","en",105,"# Introduction\n## Econometrics as a Core Yet Challenging Subject\n## Rising Role of Machine Learning in Economics\n# Syllabus Data and Trend Analysis\n## Keywords and International Comparisons\n## Integration Needs for Labor-Market Employability\n# Teaching Principles and Inclusive Guidelines\n## Two Overarching Approaches\n## Five-Pillar Framework for Inclusiveness","[{\"question\":\"What trends in course content does the paper identify using international syllabus data?\",\"answer\":\"It finds an upward trajectory in adding machine learning topics to business syllabi and a decline in econometrics-related subjects, especially indicating a shift of emphasis from econometrics toward machine learning.\"},{\"question\":\"Why does the paper emphasize improving econometrics teaching?\",\"answer\":\"Because increasing undergraduate diversity and labor-market demands require econometrics education that is more inclusive and applicable, helping students engage with real-world economic and business data.\"},{\"question\":\"What teaching framework does the paper propose to support inclusiveness in econometrics classes?\",\"answer\":\"It formalizes a five-principle framework: accessible and applicable content, content reflecting real-world diversity, clear student expectations, promoting relevance and belonging with a growth mindset, and providing a welcoming and supportive environment.\"}]","ECONOMETRICS AND MACHINE LEARNING IN BUSINESS AND ECONOMICS EDUCATION: FACTS AND A GUIDELINE ON TEACHING PRACTICES | 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trends in course content does the paper identify using international syllabus data?","Question",{"text":75,"@type":76},"It finds an upward trajectory in adding machine learning topics to business syllabi and a decline in econometrics-related subjects, especially indicating a shift of emphasis from econometrics toward machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why does the paper emphasize improving econometrics teaching?",{"text":80,"@type":76},"Because increasing undergraduate diversity and labor-market demands require econometrics education that is more inclusive and applicable, helping students engage with real-world economic and business data.",{"name":82,"@type":73,"acceptedAnswer":83},"What teaching framework does the paper propose to support inclusiveness in econometrics classes?",{"text":84,"@type":76},"It formalizes a five-principle framework: accessible and applicable content, content reflecting real-world diversity, clear student expectations, 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