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<p><font size="2" face="sans-serif">Leonghwee Teo's thesis and, more directly, the subsequent CHI paper had great predictive power for new users of a website. In 36 tasks, it predicted 93% of the easy tasks (95% of the users would finish successfully within 3 minutes and 93% of the hard tasks (>75% of humans couldn't complete within 3 minutes) with 14% false alarms (incorrectly identifying easy or medium tasks as hard).</font><br>
<font size="2" face="sans-serif">Looks like I neglected to put it in the ACT-R repository, so I'll send it to you in a separate email.</font><br>
<font size="2" face="sans-serif">Bonnie</font><br>
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<img width="16" height="16" src="cid:1__=0ABBF65BDFC5CDF78f9e8a93df938@us.ibm.com" border="0" alt="Inactive hide details for Ken Koedinger ---04/28/2014 09:51:49 AM---I'm looking for references for the following statement and "><font size="2" color="#424282" face="sans-serif">Ken Koedinger ---04/28/2014 09:51:49 AM---I'm looking for references for the following statement and figure the ACT-R community may have some</font><br>
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<font size="1" color="#5F5F5F" face="sans-serif">From: </font><font size="1" face="sans-serif">Ken Koedinger <koedinger@cmu.edu></font><br>
<font size="1" color="#5F5F5F" face="sans-serif">To: </font><font size="1" face="sans-serif">act-r-users@actr-server.hpc1.cs.cmu.edu</font><br>
<font size="1" color="#5F5F5F" face="sans-serif">Date: </font><font size="1" face="sans-serif">04/28/2014 09:51 AM</font><br>
<font size="1" color="#5F5F5F" face="sans-serif">Subject: </font><font size="1" face="sans-serif">[ACT-R-users] Ref for empirical tests of cognitive models by predicting task difficulty</font><br>
<font size="1" color="#5F5F5F" face="sans-serif">Sent by: </font><font size="1" face="sans-serif">"ACT-R-users" <act-r-users-bounces@actr-server.hpc1.cs.cmu.edu></font><br>
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<tt><font size="2"><br>
I'm looking for references for the following statement and figure the <br>
ACT-R community may have some:<br>
<br>
"One way to empirically evaluate the quality of a cognitive model is to <br>
test whether it can be used to accurately predict differences in task <br>
difficulty."<br>
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I'm particularly interested in references to models that predict error <br>
rates (but reaction time prediction is ok too) across a number of <br>
related tasks. Models that predict errors at steps in tasks and/or <br>
specific strategy or error differences are even better. One such <br>
reference is our own tech report below -- see constraint C3 in Table 1.<br>
<br>
Koedinger, K.R., & MacLaren, B. A. (2002).Developing a pedagogical <br>
domain theory of early algebra problem solving.CMU-HCII Tech Report <br>
02-100.[PDF <br>
<</font></tt><tt><font size="2"><a href="http://pact.cs.cmu.edu/koedinger/pubs/Koedinger,%20McLaren%20.pdf">http://pact.cs.cmu.edu/koedinger/pubs/Koedinger,%20McLaren%20.pdf</a></font></tt><tt><font size="2">>]<br>
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Others? Including your own work?<br>
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Thanks!<br>
Ken<br>
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ACT-R-users@act-r.psy.cmu.edu<br>
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