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 22.214.171.124 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
In the following it will be assumed that menu items from earlier modules are known. Look back if you are not sure about the meaning of one or another menu item not mentioned.
The information available to the student is represented as the number correct on a preliminary test of chosen length. The assumption here is that the results on a preliminary test are known - immediately after sitting the test. The number correct is the score obtained on the preliminary test or pretest, the next menu item is the number of items in the preliminary test. The preliminary test is assumed to be a random sample from the same knowledge domain that the summative test will be sampled from too.
The preliminary test score may be used as a factual device to inform the student. Alternatively, the student may express the information available to her as the number correct on a virtual preliminary test of a certain length. Test length represents the strength of the information available.
If the information includes guessing, the assumed probability to guess the right answer on items not known can be specified. The likelihood evaluated or simulated then will be the likelihood of mastery itself. It is possible, of course, to keep the guessing probability at zero, knowing that it is in fact substantial; the likelihood then is the likelihood of the combination of mastery and guessing equalling the horizontal value that still will be called 'mastery.'
The number of mastery grid bars determines the 'grain' of the likelihood: the higher the number, the smoother the analytical plot will look, and the longer the simulation will take.
The horizontal scale is that of mastery, running from almost zero to almost one. The scale is divided in bars of equal width, their number is the mastery grid. In the pictured case the grid is 100, the rightmost bar represents mastery 0.995.
The options option is offered here for convenience. See the 'new menu items' rubric under the advanced applet for the possibilities offered.
The vertical scale by definition of the concept of a likelihood runs from one to zero. In many cases the simulation will seem to lie below the analytical distribution, because for the simulation also the highest likelihood will be set equal to one.
The mean and standard deviation of the likelihoods are reported, as well as the mastery value that has the maximum likelihood, and the probability that given this mastery the test score will equal the number correct as declared by the user.
Option 204 prints out function values.
Option 205 uses the beta density in the analytical case. Mean and standard deviation, however, are evaluted using the vector values (see option 206 if you want the mean and standard deviation evaluated directly from the formulas).
Option 206 prints the standard deviation evaluated according to the beta density formula, as a check on the regular results evaluated on the basis of the actual likelihood function.
Option 207 makes the binomial parameter m + ( 1 - m ) * ( r + r * m ), in other words, the guessing probability gets higher with higher mastery. It is best used with r representing the guessing probability in case the student does not yet know anything of the course content.
Option 208 is a fast way to produce results without guessing, even when the guessing parameter is declared to be positive.
Option 209 evaluates or simulates the likelihood using the test-level model. Because the test-level model is equivalent to the item-level model, the results will be the same to that of the item-level model. To mark the results as being produced by the test-level method, the analytic plot will be shown in blue instead of red.
Option 210 will use the full simulation method instead of the fast simulation one. It might be used when the number of runs is chosen to be rather low; in such a case the fast method might give visibly 'clustered' results, not looking very 'randomly' produced. Needless to say, the full simulation method is very much slower than the fast method.
Mail your opinion, suggestions, critique, experience on/with the SPA