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	<id>https://www.pcla.wiki/index.php?action=history&amp;feed=atom&amp;title=Engagement_and_Affect_Detection</id>
	<title>Engagement and Affect Detection - Revision history</title>
	<link rel="self" type="application/atom+xml" href="https://www.pcla.wiki/index.php?action=history&amp;feed=atom&amp;title=Engagement_and_Affect_Detection"/>
	<link rel="alternate" type="text/html" href="https://www.pcla.wiki/index.php?title=Engagement_and_Affect_Detection&amp;action=history"/>
	<updated>2026-04-15T11:21:20Z</updated>
	<subtitle>Revision history for this page on the wiki</subtitle>
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	<entry>
		<id>https://www.pcla.wiki/index.php?title=Engagement_and_Affect_Detection&amp;diff=479&amp;oldid=prev</id>
		<title>Shruti: Addition</title>
		<link rel="alternate" type="text/html" href="https://www.pcla.wiki/index.php?title=Engagement_and_Affect_Detection&amp;diff=479&amp;oldid=prev"/>
		<updated>2023-10-01T04:38:07Z</updated>

		<summary type="html">&lt;p&gt;Addition&lt;/p&gt;
&lt;table style=&quot;background-color: #fff; color: #202122;&quot; data-mw=&quot;interface&quot;&gt;
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				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;Revision as of 00:38, 1 October 2023&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l5&quot;&gt;Line 5:&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;Line 5:&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52)&lt;/div&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52)&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors trained on combined population generally performed better for urban and suburban population (kappa = 0.18, 0.16; A′ = 0.62, 0.66) and not as well for rural population (kappa = 0.06; A′ = 0.54)&lt;/div&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors trained on combined population generally performed better for urban and suburban population (kappa = 0.18, 0.16; A′ = 0.62, 0.66) and not as well for rural population (kappa = 0.06; A′ = 0.54)&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-side-deleted&quot;&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;+&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;Chiu (2020) [https://files.eric.ed.gov/fulltext/EJ1267654.pdf pdf]&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-side-deleted&quot;&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;+&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-side-deleted&quot;&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;+&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;* Model identifies affective states (boredom, concentration, confusion, frustration, off task and gaming) of middle school students’ online mathematics learning in predicting their choice to study STEM in higher education.&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-side-deleted&quot;&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;+&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;* Model detects interaction with the ASSISTments system&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-side-deleted&quot;&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;+&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;* Model performs better for male students (AUC =0.641 for RFPS; AUC =0.571 for LR) than female students (AUC = 0.492 for RFPS; AUC=0.535 for LR)&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;

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		<author><name>Shruti</name></author>
	</entry>
	<entry>
		<id>https://www.pcla.wiki/index.php?title=Engagement_and_Affect_Detection&amp;diff=362&amp;oldid=prev</id>
		<title>Ryan: added detail</title>
		<link rel="alternate" type="text/html" href="https://www.pcla.wiki/index.php?title=Engagement_and_Affect_Detection&amp;diff=362&amp;oldid=prev"/>
		<updated>2022-06-10T10:09:50Z</updated>

		<summary type="html">&lt;p&gt;added detail&lt;/p&gt;
&lt;table style=&quot;background-color: #fff; color: #202122;&quot; data-mw=&quot;interface&quot;&gt;
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				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;Revision as of 06:09, 10 June 2022&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l2&quot;&gt;Line 2:&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;Line 2:&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;br/&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;br/&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Models detecting student affective states (boredom, confusion, engaged concentration, frustration) from the interaction with ASSISTment system&lt;/div&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Models detecting student affective states (boredom, confusion, engaged concentration, frustration) from the interaction with ASSISTment system&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-side-deleted&quot;&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;+&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;* Study involved urban, rural, and suburban learners&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52)&lt;/div&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52)&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors trained on combined population generally performed better for urban and suburban population (kappa = 0.18, 0.16; A′ = 0.62, 0.66) and not as well for rural population (kappa = 0.06; A′ = 0.54)&lt;/div&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors trained on combined population generally performed better for urban and suburban population (kappa = 0.18, 0.16; A′ = 0.62, 0.66) and not as well for rural population (kappa = 0.06; A′ = 0.54)&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;

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		<author><name>Ryan</name></author>
	</entry>
	<entry>
		<id>https://www.pcla.wiki/index.php?title=Engagement_and_Affect_Detection&amp;diff=270&amp;oldid=prev</id>
		<title>Seiyon at 12:25, 18 May 2022</title>
		<link rel="alternate" type="text/html" href="https://www.pcla.wiki/index.php?title=Engagement_and_Affect_Detection&amp;diff=270&amp;oldid=prev"/>
		<updated>2022-05-18T12:25:19Z</updated>

		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;table style=&quot;background-color: #fff; color: #202122;&quot; data-mw=&quot;interface&quot;&gt;
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				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;Revision as of 08:25, 18 May 2022&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l1&quot;&gt;Line 1:&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;Line 1:&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;−&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;Ocumpaugh et al. (2014) &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;[&lt;/del&gt;[https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.12156 pdf&lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;]&lt;/del&gt;]&lt;/div&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;+&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;Ocumpaugh et al. (2014) [https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.12156 pdf]&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;br/&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;br/&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Models detecting student affective states (boredom, confusion, engaged concentration, frustration) from the interaction with ASSISTment system&lt;/div&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Models detecting student affective states (boredom, confusion, engaged concentration, frustration) from the interaction with ASSISTment system&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52)&lt;/div&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52)&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors trained on combined population generally performed better for urban and suburban population (kappa = 0.18, 0.16; A′ = 0.62, 0.66) and not as well for rural population (kappa = 0.06; A′ = 0.54)&lt;/div&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot;&gt;&lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;* Detectors trained on combined population generally performed better for urban and suburban population (kappa = 0.18, 0.16; A′ = 0.62, 0.66) and not as well for rural population (kappa = 0.06; A′ = 0.54)&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;

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		<author><name>Seiyon</name></author>
	</entry>
	<entry>
		<id>https://www.pcla.wiki/index.php?title=Engagement_and_Affect_Detection&amp;diff=111&amp;oldid=prev</id>
		<title>Seiyon: Created page with &quot;Ocumpaugh et al. (2014) https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.12156 pdf  * Models detecting student affective states (boredom, confusion, engaged concentration, frustration) from the interaction with ASSISTment system * Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52) * Detectors trained...&quot;</title>
		<link rel="alternate" type="text/html" href="https://www.pcla.wiki/index.php?title=Engagement_and_Affect_Detection&amp;diff=111&amp;oldid=prev"/>
		<updated>2022-02-17T06:56:58Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;Ocumpaugh et al. (2014) &lt;a href=&quot;/index.php?title=Https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.12156_pdf&amp;amp;action=edit&amp;amp;redlink=1&quot; class=&quot;new&quot; title=&quot;Https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.12156 pdf (page does not exist)&quot;&gt;https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.12156 pdf&lt;/a&gt;  * Models detecting student affective states (boredom, confusion, engaged concentration, frustration) from the interaction with ASSISTment system * Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52) * Detectors trained...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;Ocumpaugh et al. (2014) [[https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.12156 pdf]]&lt;br /&gt;
&lt;br /&gt;
* Models detecting student affective states (boredom, confusion, engaged concentration, frustration) from the interaction with ASSISTment system&lt;br /&gt;
* Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52)&lt;br /&gt;
* Detectors trained on combined population generally performed better for urban and suburban population (kappa = 0.18, 0.16; A′ = 0.62, 0.66) and not as well for rural population (kappa = 0.06; A′ = 0.54)&lt;/div&gt;</summary>
		<author><name>Seiyon</name></author>
	</entry>
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