The guessing parameter is not yet available in applets 3 and higher.
The proper working of stratified sampling in the higher applets has not been checked for a long time now, be not amazed to get rubbish as results.
If you come across an applet that is not functioning properly, please mail me. It is not possible always to check all applets for unintended consequences of changes in classes. As this is a project in progress, such changes are made on a routine basis.
Applets are known to work correctly under:
Internet Explorer under Windows XP
Firefox 1.0.7 under Windows XP
Safari 1.2 under MacOSX 10.3.9
It might be the case that the applets do not open properly in browsers under Windows, or in browsers other than Safari under MacOS X: the applet field remains gray or blank.
In module chapters original applets have been replaced with screenshots; therefore applet problems should not hinder readers of the SPA project. Readers not able to use the applets in their browser, and yet willing to do so, may contact me, if preferences of the browser pertaining to Java do not seem to be the problem.
Information about Java, and applets in particular:
MacOS X: There is a problem with Java versions 1.4 for browsers other than Safari. See http://javaplugin.sourceforge.net/Readme.html; http://developer.apple.com/documentation/Java/Conceptual/Java131Development/deploying/chapter_3_section_5.html; simile.mit.edu/repository/ misc/java_embedding_plugin/readme.rtf
MacOS X: Opera, version 8.5, produces 'java.lang.UnsupportedClassVersionError: Spa_BinomialApplet (Unsupported major.minor version 48.0)
MacOS X: Internet Explorer 5.2 for Mac, [preferences: enable Java on; cookies: never ask; web content: enable plug-ins on] produces 'java.lang.UnsupportedClassVersionError: Spa_BinomialApplet (Unsupported major.minor version 48.0)
Windows: Java Applet plug-in makes it possible for your computer (including Windows¨ XP, Me, NT, 2000, 98, or 95) to run applets in your browser. http://www.mcdonalds.com/search/help/plug_play/sunmicro.html
1 1a Generator 1o. 1oa advanced 2 2a Mastery Envelope 3 3a Predictor 4 4a Ruling 5 5a Learning 6 6a Expectations 6b. special 6.1a. advanced 7 7a Last Test 8 8a Strategy 9 9a True Utility
The mastery is the mastery supposed known after learning for one period, or a particular mastery chosen from the likelihood (module two).
There is a choice of two learning models offered, the accumulation model and the replacement model, described in Mazur and Hastie, 1978. Together they cover most of the learning of basic elements of knowledge. In actual practice not much is known about what learning models could be valid; therefore use the available models to study how sensitive strategic choices are to the kind of model that really applies (they are!).
Learning is defined on the basic elements of the knowledge domain. Test items typically involve two or more of those items at once, the precise number is the complexity of the items in the item domain.
The vertical scale is zero to one. Learning is supposed to begin at zero mastery, its ceiling is perfect mastery, i.e. one.
The horizontal scale is fixed by the one period assumed to lie between the beginning and the mastery specified. Otherwise it may be streched at will into the future, but in applications only the second episode will be of any import. To refine results, refine the grid by specifying a larger number of bars per episode.
Learning curves of any form might be used in the SPA model; no such options have been implemented however.
Mail your opinion, suggestions, critique, experience on/with the SPA