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
FireFox 18.104.22.168 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 applet here is SPA_BeBiApplet.class, a class dedicated to the predictive score distribution only, in its basic form. This applet was finished April 2005. However, the fundamental classes that it makes use of—such as to evaluate the binomial distribution or the likelihood function of mastery—will be more recent or the most recent versions, as described in the text on the particular modules.
Remember that the predictive score distribution is conditional on the likelihood function of mastery, so at least in the simulation the likelihood function will be simulated first, using the givens on number correct on a preliminary test of a certain length —or information value. Nothing in the report on the simulated predictive function and its statistics directly refers to that likelihood, yet it is there, it might have been plotted or printed also but to avoid clutter this kind of information on in-between results of the analysis or simulation will be withheld. In the case of analysis only, it is known that the predictive score distribution is a beta binomial distribution, obviating the need to evaluate the likelihood function of mastery—a beta function—first.
The number of test items is the number of items in the test that stands to be predicted.
The reference is a score level that in some sense is critical. With pass-fail scoring it is the cutoff score.
The other new menu items will be better explained in the next module on utility functions. Compensation has the usual meaning of a lower score compensating for a higher one, and vice versa; scores may be grouped together for this purpose, the 'item group' declares the number of items in every group worth one point in compensation. The values chosen here make the situation one of pass-fail scoring, in which case the expected utility equals the probability of passing the test on the first occasion: the probabilities are reported as epected uilities (expU) below the figure.
For historical reasons—originally the predictive score distribution was the fourth module, the utility function the third—the fundamental classes evaluate the expectation, returning also the predictive score distribution used in that evaluation.
The simulation uses the class SPA_Expectations.Simulate, itself using SPA_Basics.Simulate(5 parameters), the fast likelihood simulation (= the more recent Basics.getFastSimulatedLikelihood), and simulating a predictive score for every mastery value sampled from under the likelihood function.
a faster procedure is possible making use of the fact that the likelihood function itself already is the result of a simulation, so again random sampling is a rather silly thing to do.
The analysis uses the class SPA_Expectations.Bebi, this class uses a recursion formula to evaluate the beta binomial distribution function directly. [See, for example, my 1980 Toetsen, par 8.4 p. 107 formula 5]
The applet here is SPA_a_ProjPredApplet.class, a class dedicated to the predictive score distribution only, in its advanced form. The applet uses the same fundamental classes as other applets do, only the household chores and the menu differ from other applets that might be used to eveluate predictive score distributions. I will make those alternative applets available also, if only to have available some alternative just in case this particular applet might not function properly.
October 2008. Something tricky here. The applet does not seem to agree in its results with the straight 3 applet above. How come? It might have something to do with the learning function/learning episodes.
In fact, this is the advanced applet belonging to the Expectations module 6. It contains utility and learning parameters. To use it as the advanced applet for immediate predictions, put the value(s) for the number of episodes at one.
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