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JSON.php
README
blame.php
condition.php
course-usage.ods
onelog.php
pearson-workshop.odp
reward.nb
state.php
step-model.php
time.php
training.php

README

This is a list of candidate features to be used
to define the student "state" at a given step.

The following is an attempt to match features listed
in Chapter 6 of Min Chi's thesis.

********************************************************************

fraction of terminal hints (hints without accompanying 
explain-more), in response to help request, over number opportunities 
for the student during current session.  
[sort of matches tellsSinceElicitA]

fraction of terminal hints (hints without accompanying 
explain-more), in response to a help request, over number opportunities 
for the student overall.

time since last application of current KC. Probably take 
log.  Need to consider case where there is no previous
case and spanning between sessions.  [durationKCBetweenDecisionT]

sessionTime: Total time spent in current session.  [TimeInSessionT]

Time between this session and previous session.  Probably 
take log.  [TimeBetweenSessionT]

Number of SystemEntries for current problem.  Run Andes, store
in data file.  [SimpleProblemPS]

Number of previous opportunities for current KC in session.  [NKCsPS]

Number of previous opportunities for current KC overall. 

Change in number of SystemEntries since previous problem. 
[newLevelDifficultyPS]

Average time spent on current problem, averaging over all students.  From datamining log files. [conceptDifficultyPS]

Average error rate on current problem, averaging over all students.  From datamining log files. [conceptDifficultyPS]

Number of SystemEntries solved on this problem, so-far [numPhysConceptsTutorDialogueSessionPS].

Number of hint requests, so far, for this problem.  [tutAverageConceptsSessionPS]

Fraction of correct entries for first attempt of each opportunity, for student, over all KCs. [pctCorrectPM]

Fraction of correct entries for first attempt of each opportunity, for student, for current KC. [pctCorrectPM]

Fraction of correct entries for first attempt of each opportunity, for student, current session, all KCs. [pctCorrectPM]

Fraction of correct entries for first attempt of each opportunity, for student, current session, current KC. [pctCorrectPM]

Average "assistance score"  (number of incorrects plus help requests), averaged over all sessions, all KCs, for student.  [nCorrectKCPM].

Average "assistance score"  (number of incorrects plus help requests), current session, all KCs, for student.  [nCorrectKCPM].

Average "assistance score"  (number of incorrects plus help requests), averaged over all sessions, current KC, for student.  [nCorrectKCPM].

Average "assistance score"  (number of incorrects plus help requests), current session, current KC, for student.  [nCorrectKCPM].

Number of opportunities for this KC, all sessions, for this student.  [nCorrectKCPM]

Number of opportunities for this KC tried, all sessions, for this student. 

Number of opportunities for this KC tried, this session, for this student [nCorrectKCSessionPM]


*************************************************************************

The following matches stuff I had:

*************************************************************************

nowFlounderTime: Time of floundering, at this moment

nowFlounderSteps:  Steps of floundering, at this moment

fracSessionFlounderTime:  Fractional Time of flounder, this session.

Fractional time of floundering, so far, this student.

nowStepTime:  Time for this past entry.

sessionCorrect:  Number of correct entries, this session.

sessionIncorrect:  Number of incorrect entries, this session.

sessionHelp:  Number of help requests, this session.

fracSessionCorrect:  Of correct entries over total entries, this session.

State of past entry (incorrect/correct/help/none)

Does step model detect learning for this KC?

If learning occurred, according to step model, 
how many opportunities ago?

Current slip/guess rate for this KC, according to step model.

***************************************************************************

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