<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Artificial Neural Networks |</title><link>http://kalz.cc/tags/artificial-neural-networks/</link><atom:link href="http://kalz.cc/tags/artificial-neural-networks/index.xml" rel="self" type="application/rss+xml"/><description>Artificial Neural Networks</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 29 Sep 2023 11:02:28 +0200</lastBuildDate><image><url>http://kalz.cc/media/icon_hu_1c0e9cb08cfb822a.png</url><title>Artificial Neural Networks</title><link>http://kalz.cc/tags/artificial-neural-networks/</link></image><item><title>A data-driven approach for the identification of features for automated feedback on academic essays</title><link>http://kalz.cc/publication/a-data-driven-approach-for-the-identification-of-features-for-automated-feedback-on-academic-essays/</link><pubDate>Fri, 29 Sep 2023 11:02:28 +0200</pubDate><guid>http://kalz.cc/publication/a-data-driven-approach-for-the-identification-of-features-for-automated-feedback-on-academic-essays/</guid><description>&lt;p&gt;Abbas, M., Van Rosmalen, P. &amp;amp; Kalz, M. (2023). A data-driven approach for the identification of
features for automated feedback on academic essays. &lt;em&gt;IEEE Transactions on Learning Technologies&lt;/em&gt;.
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