scholarly journals The Cognitive Tutor Authoring Tools (CTAT): Preliminary Evaluation of Efficiency Gains

2006 ◽  
pp. 61-70 ◽  
Author(s):  
Vincent Aleven ◽  
Bruce M. McLaren ◽  
Jonathan Sewall ◽  
Kenneth R. Koedinger
Keyword(s):  
Preliminary Evaluation ◽  
Authoring Tools ◽  
Efficiency Gains ◽  
Cognitive Tutor

The Effect of Assistance on Learning and Affect in an Algebra Tutor

2019 ◽  
Vol 57 (8) ◽  
pp. 2032-2052 ◽  
Author(s):  
Giuliana Borracci ◽  
Erica Gauthier ◽  
Jay Jennings ◽  
Kyle Sale ◽  
Kasia Muldner
Keyword(s):  
Problem Solving ◽  
Negative Affect ◽  
Higher Learning ◽  
The Other ◽  
Authoring Tools ◽  
Cognitive Tutor ◽  
Similarity Problem ◽  
The Impact ◽  
Primary Form

We investigated the impact of assistance on learning and affect during problem-solving activities with a computer tutor we built using the Cognitive Tutor Authoring Tools framework. The tutor delivered its primary form of assistance in the form of worked-out examples. We manipulated the level of assistance the examples in the tutor provided, by having similar problem-example pairs in one version of the tutor (high-assistance condition) and reduced similarity problem-example pairs in the other version (reduced-assistance condition). The reduced-assistance condition resulted in significantly higher learning, without increasing negative affect like frustration.

Preliminary Evaluation of a Weibull Function for Fitting Slow-Component Eye Velocity over the Time Course of Caloric-Induced Nystagmus

1989 ◽  
Vol 32 (3) ◽  
pp. 681-687 ◽  
Author(s):  
C. Formby ◽  
B. Albritton ◽  
I. M. Rivera
Keyword(s):  
Time Course ◽  
Slow Component ◽  
Weibull Function ◽  
Preliminary Evaluation ◽  
Mathematical Function ◽  
Before And After ◽  
Best Fitting ◽  
Eye Velocity ◽  
Fitting Parameters ◽  
Weibull Equation

We describe preliminary attempts to fit a mathematical function to the slow-component eye velocity (SCV) over the time course of caloric-induced nystagmus. Initially, we consider a Weibull equation with three parameters. These parameters are estimated by a least-squares procedure to fit digitized SCV data. We present examples of SCV data and fitted curves to show how adjustments in the parameters of the model affect the fitted curve. The best fitting parameters are presented for curves fit to 120 warm caloric responses. The fitting parameters and the efficacy of the fitted curves are compared before and after the SCV data were smoothed to reduce response variability. We also consider a more flexible four-parameter Weibull equation that, for 98% of the smoothed caloric responses, yields fits that describe the data more precisely than a line through the mean. Finally, we consider advantages and problems in fitting the Weibull function to caloric data.

Sign in / Sign up

Export Citation Format

Share Document