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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Survey of Knowledge Tracing: Models, Variants, and Applications · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2105.15106/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2105.15106/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Survey of Knowledge Tracing: Models, Variants, and Applications — mentioned by pona-a</title><link>https://news.ycombinator.com/item?id=44027560</link><guid isPermaLink="false">urn:hackerpapers:mention:2105.15106:44027560</guid><pubDate>Mon, 19 May 2025 08:16:06 GMT</pubDate><description>&lt;p&gt;You might be interested in reading the literature on Knowledge Tracing, which is a superset of spaced repetition, aiming to predict what the student knows from their answers. The simplest approach is Bayesian Knowledge Tracing [0], which is just a simple probability update and an Expectation Maximization optimizer to fit the latent factors. The standard version assumes an independent set of skills with no forgetting, but there are extensions for that [1] [2]. PyBKT [3] implements some common ones, so take a look there. Learning Factor Analysis [4] seems to work considerably better than naive…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2105.15106/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CY</category></item></channel></rss>
