{"id":979917,"date":"2023-10-26T13:45:23","date_gmt":"2023-10-26T20:45:23","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=979917"},"modified":"2023-11-27T13:19:41","modified_gmt":"2023-11-27T21:19:41","slug":"investigating-student-mistakes-in-introductory-data-science-programming","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/investigating-student-mistakes-in-introductory-data-science-programming\/","title":{"rendered":"Investigating Student Mistakes in Introductory Data Science Programming"},"content":{"rendered":"

Data Science (DS) has emerged as a new academic discipline where <\/span>students are introduced to data-centric thinking and generating <\/span>data-driven insights through programming. Unlike traditional intro<\/span>ductory programming education, which focuses on program syntax <\/span>and core\u00a0 computer Science (CS) topics (e.g., algorithms and data <\/span>structures), introductory DS education emphasizes skills such as <\/span>studying the data at hand to gain insights and making effective use <\/span>of programming libraries (e.g.,<\/span> re<\/span>,<\/span> NumPy<\/span>,<\/span> pandas<\/span>,<\/span> scikit-learn<\/span>). <\/span>To better understand learners\u2019 needs and pain points when they <\/span>are introduced to DS programming, we investigated a large online <\/span>course on data manipulation designed for graduate students who <\/span>do not have a CS or Statistics undergraduate degree. We qualita<\/span>tively analyzed incorrect student code submissions for computa<\/span>tional notebook-based programming assignments in Python. We <\/span>identified common mistakes and grouped them into the following <\/span>themes: (1) programming language and environment misconcep<\/span>tions, (2) logical mistakes due to data or problem-statement misun<\/span>derstanding or incorrectly dealing with missing values, (3) semantic <\/span>mistakes from incorrect usage of DS libraries, and (4) suboptimal <\/span>coding. Our work provides instructors valuable insights to under<\/span>stand student needs in introductory DS courses and improve course <\/span>pedagogy, along with recommendations for developing assessment <\/span>and feedback tools to better support students in large courses<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Data Science (DS) has emerged as a new academic discipline where students are introduced to data-centric thinking and generating data-driven insights through programming. Unlike traditional introductory programming education, which focuses on program syntax and core\u00a0 computer Science (CS) topics (e.g., algorithms and data structures), introductory DS education emphasizes skills such as studying the data at […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Technical Symposium on Computer Science 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