A high-level, ready-to-use R package for quiz & questions analysis of Moodle Grades Report
Installation
You can install the development version of moodleStats from GitHub with:
# install.packages("remotes")
remotes::install_github("Lightbridge-KS/moodleStats")Goal
The goal of this package it to provide a high-level functions for analysis of Moodle Grades Report such as calculation of descriptive statistics for quiz & questions, and performing an item analysis.
Workflow
Read Data
Read Moodle Grades Report from .csv file into a Data Frame.
grades_df <- readr::read_csv(moodleStats_example("grades_report.csv"))
#> Rows: 461 Columns: 19
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (17): Surname, First name, Email address, State, Started on, Completed, ...
#> lgl (2): Institution, Department
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(grades_df)
#> # A tibble: 6 × 19
#> Surname `First name` Institution Department `Email address` State `Started on`
#> <chr> <chr> <lgl> <lgl> <chr> <chr> <chr>
#> 1 el-Taha Joshua NA NA joshua.el_025@… Fini… 12 February…
#> 2 al-Ayd… Tyler NA NA tyler.al_192@e… Fini… 12 February…
#> 3 Sawyer Michael NA NA michael.saw_24… In p… 16 February…
#> 4 Snow Leeah NA NA leeah.sno_016@… Fini… 21 February…
#> 5 al-Hab… Lawrence NA NA lawrence.al_20… Fini… 22 February…
#> 6 el-Sal… Isaiah NA NA isaiah.el_140@… Fini… 23 February…
#> # … with 12 more variables: Completed <chr>, `Time taken` <chr>,
#> # `Grade/9.00` <chr>, `Q. 1 /1.00` <chr>, `Q. 2 /1.00` <chr>,
#> # `Q. 3 /1.00` <chr>, `Q. 4 /1.00` <chr>, `Q. 5 /1.00` <chr>,
#> # `Q. 6 /1.00` <chr>, `Q. 7 /1.00` <chr>, `Q. 8 /1.00` <chr>,
#> # `Q. 9 /1.00` <chr>Prepare Data
Cleaning and filtering data can be done in 1 step using prep_grades_report()
grades_df_preped <- prep_grades_report(grades_df,
choose_state = "Finished", # 1. choose only "Finished" attempt
choose_grade = "max", # 2. choose only the best score of each student
choose_time = "first" # 3. choose the first time that student submitted
)
head(grades_df_preped)
#> # A tibble: 6 × 16
#> Name Institution Department Email State Started Grade Q1 Q2
#> <chr> <lgl> <lgl> <chr> <chr> <dttm> <dbl> <dbl> <dbl>
#> 1 Josh… NA NA josh… Fini… 2021-02-12 13:03:00 5 1 1
#> 2 Leea… NA NA leea… Fini… 2021-02-21 20:09:00 9 1 1
#> 3 Isai… NA NA isai… Fini… 2021-02-23 19:51:00 6 0 1
#> 4 Eric… NA NA eric… Fini… 2021-02-23 21:57:00 0 0 0
#> 5 Euge… NA NA euge… Fini… 2021-02-24 12:25:00 7 1 1
#> 6 Anni… NA NA anni… Fini… 2021-02-25 01:39:00 4 0 1
#> # … with 7 more variables: Q3 <dbl>, Q4 <dbl>, Q5 <dbl>, Q6 <dbl>, Q7 <dbl>,
#> # Q8 <dbl>, Q9 <dbl>Quiz Metadata
Show quiz metadata such as maximum settings of quiz and questions by quiz_meta()
quiz_meta(grades_df_preped)
#>
#> ── Moodle Quiz Setting ──
#>
#> • Maximum grades of quiz: 9
#> • Maximum score of questions:
#>
#> Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9
#> 1 1 1 1 1 1 1 1 1
#>
#> ── Data Frame Filtering ──
#>
#> Data frame was filtered by the following orders:
#> 1. Choose "Finished" responses by 'State' column.
#> 2. Choose max grades of each students.
#> 3. Choose first attempt(s) of each student by 'Started on' column.Quiz Summary
summary_quiz() calculates various quiz summary statistics, and combine it into 1 row data frame.
quiz_report_df <- summary_quiz(grades_df_preped)
quiz_report_df
#> # A tibble: 1 × 14
#> n min max max_quiz median Q1 Q3 IQR MAD mean SD skewness
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 272 0 9 9 7 5 9 4 2.97 6.70 2.22 -0.884
#> # … with 2 more variables: kurtosis <dbl>, Cronbach_Alpha <dbl>Question Summary and Item Analysis
summary_questions() calculates various question summary statistics and item analysis into a data frame.
question_report_df <- summary_questions(grades_df_preped)Here is summary statistics for each questions.
question_report_df %>%
dplyr::select(Questions:SD)
#> # A tibble: 9 × 7
#> Questions n min max max_setting mean SD
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Q1 271 0 1 1 0.760 0.428
#> 2 Q2 269 0 1 1 0.941 0.237
#> 3 Q3 268 0 1 1 0.511 0.501
#> 4 Q4 269 0 1 1 0.822 0.384
#> 5 Q5 269 0 1 1 0.743 0.438
#> 6 Q6 269 0 1 1 0.684 0.466
#> 7 Q7 269 0 1 1 0.870 0.337
#> 8 Q8 267 0 1 1 0.708 0.456
#> 9 Q9 270 0 1 1 0.737 0.441And, here is an item analysis for Moodle Grades Report.
-
Difficulty_Indexis for Item Difficulty Index (p), a measure of the proportion of examinees who answered the item correctly. -
Discrimination_Indexis for Item Discrimination Index (r), a pairwise point-biserial correlation between the score of each questions and total score of the quiz.
question_report_df %>%
dplyr::select(Questions, Difficulty_Index:p.value)
#> # A tibble: 9 × 4
#> Questions Difficulty_Index Discrimination_Index p.value
#> <chr> <dbl> <dbl> <dbl>
#> 1 Q1 0.760 0.434 7.44e-14
#> 2 Q2 0.941 0.452 6.15e-15
#> 3 Q3 0.511 0.600 1.48e-27
#> 4 Q4 0.822 0.510 3.52e-19
#> 5 Q5 0.743 0.675 4.13e-37
#> 6 Q6 0.684 0.646 3.26e-33
#> 7 Q7 0.870 0.554 4.86e-23
#> 8 Q8 0.708 0.640 3.59e-32
#> 9 Q9 0.737 0.597 1.81e-27