Calculate questions summary and item analysis of Moodle Grade Report (See Details).
Usage
summary_questions(data, cor_method = c("pearson", "kendall", "spearman"))Arguments
- data
(GradesReport) A data frame of class "GradesReport"
- cor_method
(Character) A character string indicating which correlation coefficient is to be used for calculating
Discrimination_Index. Default is Pearson's correlation ("pearson"). Other types can be specified such as "kendall", or "spearman", can be abbreviated.
Details
summary_questions() calculate overall summary grouped by each questions of Moodle Grade Report, and return the result as a data frame.
The data frame has a column "Questions" for questions number in the quiz. Other columns are the followings.
Basic summary statistic
n: number of students who answered each questions.
min: student's minimum score of each questions.
max: student's maximum score of each questions.
max_setting: maximum score possible of each questions (from the quiz setting).
mean: mean score of each questions.
SD: standard deviation of each questions score.
Item analysis
Difficulty_Index: Item Difficulty Index (p) is a measure of the proportion of examinees who answered the item correctly. It ranges between 0.0 and 1.0, higher value indicate lower question difficulty, and vice versa.
Discrimination_Index: Item Discrimination Index (r) is a measure of how well an item is able to distinguish between examinees who are knowledgeable and those who are not. It is a pairwise point-biserial correlation between the score of each questions ("Q" columns) and total score of the quiz ("Grade" column). It range between -1.0 to 1.0. Negative values suggest a problem, indicating that score of the particular question is negatively correlated with total quiz score; therefore, revision of the question is suggested.
p.value: A level of significant (p-value) of
Discrimination_Index.p.signif: A Symbol indicating level of significant of
Discrimination_Index.
See also
summary_quiz()for quiz summary statisticsquestions_stats()for question summary statistics with more parameters.item_discrim()for Item discrimination index with more parameters.
Examples
# Prepare
grades_df_preped <- prep_grades_report(grades_df)
# Question Summary
summary_questions(grades_df_preped)
#> # A tibble: 9 × 11
#> Questions n min max max_setting mean SD Difficulty_Index
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Q1 271 0 1 1 0.760 0.428 0.760
#> 2 Q2 269 0 1 1 0.941 0.237 0.941
#> 3 Q3 268 0 1 1 0.511 0.501 0.511
#> 4 Q4 269 0 1 1 0.822 0.384 0.822
#> 5 Q5 269 0 1 1 0.743 0.438 0.743
#> 6 Q6 269 0 1 1 0.684 0.466 0.684
#> 7 Q7 269 0 1 1 0.870 0.337 0.870
#> 8 Q8 267 0 1 1 0.708 0.456 0.708
#> 9 Q9 270 0 1 1 0.737 0.441 0.737
#> # … with 3 more variables: Discrimination_Index <dbl>, p.value <dbl>,
#> # p.signif <chr>