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This is an announcement of Jonathan Liu's MS Presentation</div>
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Candidate: Jonathan Liu</div>
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Date: Thursday, May 30, 2024</div>
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Time: 9:30 am CT</div>
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Remote Location: <a href="https://urldefense.com/v3/__https://uchicago.zoom.us/j/96355814566?pwd=S2c3Kys5c0NiNWxreHhrM1VzWW1pdz09__;!!BpyFHLRN4TMTrA!92M-oiIcTRW2oZxIsF0BQXZfPhHdoRDRxoNF2OfrUJS3KkRgQV3MjDCxlHNtpxBrl5F76NqogeLnN5sOgWwRm_gEVfIf4u8oUC7pqRu-nw$" id="OWA0c1480f0-f5fb-e1f9-5553-09a2de9cc045" class="OWAAutoLink" data-loopstyle="linkonly">
https://uchicago.zoom.us/j/96355814566?pwd=S2c3Kys5c0NiNWxreHhrM1VzWW1pdz09</a></div>
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Location: JCL 354</div>
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Title: What Do We Know About Teaching Algorithm Design?</div>
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Abstract: Algorithm design is a vital skill developed in most undergraduate CS programs, but few research studies focus on pedagogy related to algorithms coursework. To understand the work that has been done in the area, we present a systematic survey and literature
review of CS Education studies. We find a broad sparsity of papers which indicates that many open questions remain about teaching algorithm design. We also note the need for papers using rigorous research methods.</div>
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We then synthesize the results of the existing literature to give insights into what the corpus reveals about how we should teach algorithms. Broadly, we find that much of the literature explores implementing well-established practices, such as active learning
or automated assessment, in the algorithms classroom. However, there are algorithms-specific results as well: a number of papers find that students may under-utilize certain algorithmic design techniques, and studies describe a variety of ways to select algorithms
problems that increase student engagement and learning</div>
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The results we present, along with the publicly available set of papers collected, provide a detailed representation of the current corpus of CS Education work related to algorithm design and can orient further research in the area.</div>
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Advisors: Diana Franklin</div>
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Committee Members: Diana Franklin, Lorenzo Orecchia, and Craig Zilles</div>
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