This is a very brief look at a sample of WRCA courses. Examined first is the general structure of the courses, including the number of lessons per course and number of activities per lesson.
The next section examines the Universal Design for Learning attributes, particularly around providing multiple modes of representation and expression. Examined are a coded list of verbs and a coded list of media with which students are expected to engage.
The final section examines the depth of knowledge attributed to each activity. Provided are the distribution of DOK and some explorations of relationships between DOK and number of lessons, number of activities, and sequence of activities.
Course Structures
Number of Lessons per Course
wrca_lessons <- wrca_frame |>select(course, lesson) |>na.omit() |>group_by(course) |>filter(lesson ==max(lesson)) |>ungroup() |>unique() |>arrange(lesson, course)Desc(wrca_lessons$lesson, plotit =TRUE, main ="Lessons per Course")
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Lessons per Course
length n NAs unique 0s mean meanCI'
7 7 0 5 0 7.86 4.82
100.0% 0.0% 0.0% 10.90
.05 .10 .25 median .75 .90 .95
5.30 5.60 6.50 7.00 7.50 10.80 12.90
range sd vcoef mad IQR skew kurt
10.00 3.29 0.42 1.48 1.00 1.34 0.28
value freq perc cumfreq cumperc
1 5 1 14.3% 1 14.3%
2 6 1 14.3% 2 28.6%
3 7 3 42.9% 5 71.4%
4 8 1 14.3% 6 85.7%
5 15 1 14.3% 7 100.0%
' 95%-CI (classic)
Number of Activities per Lesson
wrca_activities <- wrca_frame |>select(course, lesson, sequence) |>na.omit() |>group_by(course, lesson) |>filter(sequence ==max(sequence)) |>ungroup() |>arrange(course, lesson)Desc(wrca_activities$sequence, main ="Number of Activities Per Lesson", plotit =TRUE)
wrca_frame$dok <-factor(wrca_frame$dok, level =c("1", "2", "3", "4"))Desc(wrca_frame$dok, plotit =TRUE, main ="Depth of Knowledge")
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Depth of Knowledge
length n NAs unique levels dupes
445 293 152 3 4 y
65.8% 34.2%
level freq perc cumfreq cumperc
1 2 179 61.1% 179 61.1%
2 1 76 25.9% 255 87.0%
3 3 38 13.0% 293 100.0%
4 4 0 0.0% 293 100.0%
Depth of Knowledge Sequences
The probability that one DOK will be followed by another.
markovchainFit(wrca_frame$dok)$estimate
MLE Fit
A 3 - dimensional discrete Markov Chain defined by the following states:
1, 2, 3
The transition matrix (by rows) is defined as follows:
1 2 3
1 0.7000000 0.2500000 0.05000000
2 0.1038961 0.8246753 0.07142857
3 0.0000000 0.2962963 0.70370370
Number of Lessons and Average Depth of Knowledge
nol_frame <- wrca_frame |>select(course, lesson, dok) |>na.omit() |>group_by(course) |>filter(lesson ==max(lesson)) |>mutate(dok =median(as.integer(dok))) |>ungroup() |>unique() |>mutate(dok =factor(dok, levels =c("1", "2", "3", "4")))Desc(lesson ~ dok, nol_frame, main ="Number of Lessons and DOK")
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Number of Lessons and DOK
Summary:
n pairs: 7, valid: 7 (100.0%), missings: 0 (0.0%), groups: 3
1 2 3
mean 10.000 5.667 7.000
median 8.000 6.000 7.000
sd 4.359 0.577 <NA>
IQR 4.000 0.500 0.000
n 3 3 1
np 42.857% 42.857% 14.286%
NAs 0 0 0
0s 0 0 0
Kruskal-Wallis rank sum test:
Kruskal-Wallis chi-squared = 4.963, df = 2, p-value = 0.08362
Number of Activities and Average Depth of Knowledge
