Difference between revisions of "Quiz reports"
Note: You are currently viewing documentation for Moodle 2.3. Up-to-date documentation for the latest stable version is available here: Quiz reports.
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− | The quiz '''Results''' tab includes several sub tabs, one for each report. | + | The quiz '''Results''' tab includes several sub tabs, one for each report, plus a regrade and manual grading tabs. The number of subtabs shown, depends on the number of [http://moodle.org/mod/data/view.php?d=13&perpage=10&search=Quiz+report&sort=46&order=ASC report plugins] your Moodle administrator has installed. |
==Overview== | ==Overview== |
Revision as of 16:55, 24 February 2008
The quiz Results tab includes several sub tabs, one for each report, plus a regrade and manual grading tabs. The number of subtabs shown, depends on the number of report plugins your Moodle administrator has installed.
Overview
This contains the list of quiz attempts arranged in four columns:
- First name / Surname
- Started on - that contains the information about the exact time the test was started
- Time taken - the amount of time it took a given student to do the test
- Grade/x - the number of points students scored; 'x' is the maximum number of points students could score
The default view lists only the students who attempted the test. You can, however, change the display settings checking either of the two boxes (followed by clicking Go):
- Which users to include - there are four options available:
- Show Students with attempts only - list only the students on the course who have done test
- Show Students with no attempts only - list students who have not yet attempted the quiz
- Show all Students - list all the students on the course no matter if they did the test or not
- Show all attempts - like 'Show Students with attempts only', but this also includes attempts by users who used to be students on the course, but have since been unenrolled.
- Show mark details - this extends the list with as many columns as there are questions in the test; each column is headed by 'n' (where 'n' stands for the question number)
With the Select all / Deselect all options you can check / uncheck all the names in the list, and, with selected, delete.
To sort the results by two columns, first click on the column heading you want to be the second key, and then click on the column heading you want to be the primary key.
Regrade
That tab will recalculate the quiz grades. This may become necessary if you have changed one of the questions or the grade possible for the quiz or a question.
Manual grading
Template:Moodle 1.6 The grade of any question in a quiz can be manually overridden, and a comment added. A teacher can do this from the review page. There are also some question types that Moodle does not grade automatically (at the moment only the Essay question). This report helps you grade questions of these types by listing just the questions that still need to be graded.
Item analysis
This table presents processed quiz data in a way suitable for analysing and judging the performance of each question for the function of assessment. The statistical parameters used are calculated as explained by classical test theory.
- Facility Index (% Correct)
This is a measure of how easy or difficult is a question for quiz-takers. It is calculated as:
FI = (Xaverage) / Xmax
where Xaverage is the mean credit obtained by all users attempting the item, and Xmax is the maximum credit achievable for that item. If questions can be distributed dicotomically into correct / incorrect categories, this parameter coincides with the percentage of users that answer the question correctly.
- Standard Deviation (SD)
This parameter measures the spread of answers in the response population. If all users answers the same, then SD=0. SD is calculated as the statistical standard deviation for the sample of fractional scores (achieved/maximum) at each particular question.
- Discrimination Index (DI)
This provides a rough indicator of the performance of each item to separate proficient vs. less-proficient users. This parameter is calculated by first dividing learners into thirds based on the overall score in the quiz. Then the average score at the analysed item is calculated for the groups of top and bottom performers, and the average scored subtracted. The mathematical expression is:
DI = (Xtop - Xbottom)/ N
where Xtop is the sum of the fractional credit (achieved/maximum) obtained at this item by the 1/3 of users having the highest grades in the whole quiz (i.e. number of correct responses in this group), and Xbottom is the analog sum for users with the lower 1/3 grades for the whole quiz.
This parameter can take values between +1 and -1. If the index goes below 0.0 it means that more of the weaker learners got the item right than the stronger learners. Such items should be discarded as worthless. In fact, they reduce the accuracy of the overall score for the quiz.
- Discrimination Coefficient (DC)
This is another measure of the separating power of the item to distinguish proficient from weak learners. The discrimination coefficient is a correlation coefficient between scores at the item and at the whole quiz. Here it is calculated as:
DC = Sum(xy)/ (N * sx * sy)
where Sum(xy) is the sum of the products of deviations for item scores and overall quiz scores, N is the number of responses given to this question, sx is the standard deviation of fractional scores for this question and, sy is the standard deviation of scores at the quiz as a whole.
Again, this parameter can take values between +1 and -1. Positive values indicate items that do discriminate proficient learners, whereas negative indices mark items that are answered best by those with lowest grades. Items with negative DC are answered incorrectly by the seasoned learners and thus they are actually a penalty against the most proficient learners. Those items should be avoided.
- Quiz effectiveness
Both DC and DI can be used as powerful methods of evaluating the effectiveness of the quiz when assessing differentiation of learners. The advantage of using Discrimination Coefficient as opposed to Discrimination Index is that the former uses information from the whole population of learners, not just the extreme upper and lower thirds. Thus, this parameter may be more sensitive to detect item performance.