[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117447-en":3,"doc-seo-117447-105":30,"detail-sidebar-cat-0-en-105":89},{"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},117447,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",4,"Exam","Machine Learning Dataset Generator Authentic Assessment - Teaching Case Study","This teaching case study documents an authentic assessment built around the MLDG (Machine Learning Dataset Generator) to create student-centered datasets that are unique yet fair. The assessment supports both in-class and remote summative testing for a final-year Applied Machine Learning course, targeting understanding of preprocessing in a realistic context. The approach provides evidence of improved fairness and academic integrity, and the same assessment design can be extended to related subjects such as databases for broader computing education research value.","Technological University Dublin  \nARROW@TU Dublin  \nCase studies: Assessment and Feedback Teaching and Learning  \n2024-03-30  \nMachine Learning Dataset Generator Authentic Assessment  \nKeith Quille  \nTechnological University Dublin  \nFollow this and additional works at: [https://arrow.tudublin.ie/teachingcaseass](https://arrow.tudublin.ie/teachingcaseass)  \n Part of the Educational Methods Commons  \nRecommended Citation  \nQuille, Keith, \"Machine Learning Dataset Generator Authentic Assessment\" (2024) . Case studies: Assessment and Feedback. 5.  \n[https://arrow.tudublin.ie/teachingcaseass/5](https://arrow.tudublin.ie/teachingcaseass/5)  \nThis Other is brought to you for free and open access by the Teaching and Learning at ARROW@TU Dublin. It has been accepted for inclusion in Case studies: Assessment and Feedback by an authorized administrator of ARROW@TU Dublin. For more information, [please contact vera.ki](please contact vera.ki)[lshaw@tudublin.ie](lshaw@tudublin.ie).  \nThis work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.  \nLearning Teaching & Assessment Resources  \n| Authentic Assessment Exemplar: |  |\n| --- | --- |\n| Assessment Title | MLDG-Machine Learning Dataset Generator |\n| Author(s) | Keith Quille, Keith Nolan, Lidia Vidal-Meliá and Brett A.\u003Cbr>Becker |\n| Module Title that Assessment Delivered on | Applied Machine Learning |\n| Primary Student Cohort (Year on Programme / FT or PT or Both / UG or PG or AP |  |\n\n\n| Overview of Assessment (Max 100 words) |\n| --- |\n| This assessment focuses on the development of a tool for generating student-centered datasets (using the MLDG-Machine Learning Dataset Generator) which are unique but fair to allow for both in-class and remote summative assessment for a final-year Machine Learning course, simultaneously. The results are encouraging and provide evidence that MLDG and the assessment approach used to promote a fair assessment with academic integrity for Machine Learning students. This tool and assessment approach could also be applied in other subjects such as databases thus having value outside of Machine Learning courses for the Computing Education Research community. |\n\n\n| What Change was Made to Assessment to Enhance its Authenticity? (Max 100 words) |\n| --- |\n| Other than all students getting the same data set with same errors in a closed book setting within a lab, the students each got a subset of the same data set however it was modified for each student. Thus each student would not be able to use another students answers especially giving there was a time constraint. This allowed students to have a truly open book assessment where the goal was to examine their understanding of preprocessing in a more real world context. Some students prefer to sit the assessment in the lab and orders at home where they had their own equipment, mimicking how data scientists might work authentically. |\n\n\n| What was the Impact on Student Engagement / Performance? (Max 100 words) |\n| --- |\n| Full paper at [https://arrow.tudublin.ie/diraacon/17/ where 26 of the 27 students said the open-book assessment method should continue. The students were asked two](https://arrow.tudublin.ie/diraacon/17/ where 26 of the 27 students said the open-book assessment method should continue. The students were asked two)\u003Cbr>[questions](questions), [any positive or negative feedback. In the positive feedback](any positive or negative feedback. In the positive feedback), [students suggested that it was a fair assessment](students suggested that it was a fair assessment), [reduced anxiety and allowed](reduced anxiety and allowed) students to focus on\u003Cbr>the tasks at hand rather than trying to debug errors. Examining the disadvantages, students cited concerns such as lack of time be it for the assessment or for time to upload the assessment. |\n\n\n| One Thing you would do Differently Next Time (Max 50 words) |\n| --- |\n| Additional time would support students m","cbCaiuKiOscBOk6V","https://ap.wps.com/l/cbCaiuKiOscBOk6V","pdf",238136,1,2,"English","en",105,"# Overview of Assessment\n# What Change was Made to Assessment to Enhance its Authenticity?\n# What was the Impact on Student Engagement / Performance?\n# One Thing you would do Differently Next Time\n# Authenticity Indicators","[{\"question\":\"What assessment is presented in this case study?\",\"answer\":\"The assessment is titled MLDG - Machine Learning Dataset Generator, delivered on the Applied Machine Learning module.\"},{\"question\":\"How did the assessment change to enhance authenticity?\",\"answer\":\"Instead of giving every student the same closed-book dataset with identical errors, each student received a subset of the dataset modified for them, enabling a truly open-book format focused on preprocessing understanding.\"},{\"question\":\"What impact did the authentic assessment have on student engagement and performance?\",\"answer\":\"Most students indicated the open-book method should continue, citing perceived fairness and reduced anxiety, and they could focus on the tasks rather than debugging errors, while concerns included time constraints and upload time.\"}]","Machine Learning Dataset Generator Authentic Assessment - 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