{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116207"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116207","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards scalable, diverse, and secure assessment in college stem education","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Binglin Chen, accepted the attached license on 2022-07-11 at 14:30.","The student, Binglin Chen, submitted this Dissertation for approval on 2022-07-11 at 15:03.","This Dissertation was approved for publication on 2022-07-12 at 17:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18218 on 2022-11-15 at 17:38:42","Assessment is a key component of education. Routine grading of students’ work, however, is time consuming. Automating the grading process allows instructors to spend more of their time helping their students learn and engaging their students with more open-ended, creative activities. One way to automate grading is through computer-based assessment, where questions can be randomly parameterized and deterministic algorithms can be implemented to grade students’ responses to these questions. Computer-based assessment is particularly well suited for courses in STEM, where many of the building block skills can be formulated as algorithmically-scored tasks. This thesis focuses on computer-based assessment in three directions. The first direction concerns the adoption of computer-based assessments in a summative context, where I demonstrate that randomization of questions and a centralized proctored facility result in secure asynchronous computer-based summative assessments. In the second direction, I attempt to extract patterns from computer-based assessment data that allow instructors to adjust the assessments for the better. Lastly, I present an attempt to expand the set of autograded questions to include “Explain in Plain English” questions where students are asked to give natural language descriptions of what a piece of code does. I discuss the lessons learned from the attempt and propose how automated short answer grading should be evaluated in general."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards scalable, diverse, and secure assessment in college stem education"]}]}],"canonical_facts":{"dc:contributor":["Zilles, Craig","West, Matthew","Forsyth, David","Zhai, Chengxiang","Fox, Armando"],"dc:creator":["Chen, Binglin"],"dc:date":["2022-08","2022-07-12"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. 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