Quiz reports
Quiz reports tab includes three sub tabs:
Overview
This contains the list of quiz attempts arranged in four columns:
- First name / Surname (along with photos)
- Started on (which 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 (where 'x' is the maximum number of points for the test)
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):
- Show students with no attempts - the list will include all the course students no matter if they did the test or not.
- Show mark details - this extends the list with as many columns as there are questions in the test; each column is headed by '#x' (where 'x' 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.
Regrade attempts
That option will recalculate the quiz grades if you have changed the points possible for the quiz or a question.
Item analysis
This table presents processed quiz data in a way suitable for anayzing and judging the performance of each question for the function of assessment. The statistical parameters used are calculated as explained by classical test theory (ref. 1)
- 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 / uncorrect 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 stadard 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 analyzed item is calculated for the groups of top and bottom performers, and the average scored substracted. The matematical 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 tha 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 Sun(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. The advantage of Discrimination Coefficient vs. Discrimitation 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.
