assess_coa() aims to ease the calculation of course
objective achievement in Outcome-Based Education. Course objective
achievements are actually proportions of objectives’ actual grades in
objectives’ full grades.
assess_coa() has the following arguments:
data, session_weights, and
objective_weights1. ... indicates
objective_weights2, objective_weights3 … You
could pass as many objective_weights* as you need.
data is a data.frame that ONLY contains students’ grades
in each session. Typically, students’ final grades are bases on their
regular grades and final exam grades, where the number of session is
two. You may use more sessions, for example, 4, which are: listening,
speaking, reading, and writing. data should be in the
common wide-format, in which one student occupies a row, and one session
occupies a column. The number of columns in data
corresponds to the number of sessions you need. You may import data from
external data file like “.csv” file or Excel files. Here we simulate the
grades of two sessions for 100 students.
The full score of each session is implicitly assumed to be 100, since
the weights of each session within the final grade is assigned through
session_weights by users. session_weights is a
vector of sessions’ weights, which determine the weights of
corresponding sessions in determining the final grade. Obviously, the
sum of all session weights should be 1. The order of elements in
session_weights should strictly correspond to the order of
columns in data. The data above has two sessions, hence the length of
session_weights is 2. We specify the following
session_weights, in which 0.4 is the weight of column 1 in
data:
To assess course objective achievement, the most troublesome work is
to tackle the weights of course objectives, and
assess_coa() simplifies this work. what we need to do is
assigning weights to objectives within each session using
objective_weights* arguments, one
objective_weights* vector for one session. The element of
objective_weights* is the weight of corresponding objective
in determining the grade of corresponding session, and obviously, the
sum of all objective weights within each session should be 1. The length
of objective_weights* should be the number of the
objectives. You may have as many course objectives as you need, and here
we simulate 3 objectives. Importantly, the order of elements in
objective_weights* should strictly corresponds to the order
of objectives. Here, we have two sessions, and we need to assign weights
using objective_weights1 and
objective_weights2. If you has 10 sessions, then you should
assign objective_weights1 to
objective_weights10.
Before we assign objective_weights*, it’s helpful to
write the objective weight matrix out:
| session1 | session2 | |
|---|---|---|
| objective1 | 0.5 | 0.1 |
| objective2 | 0.3 | 0.3 |
| objective3 | 0.2 | 0.6 |
Then we specify:
It’s time for assess_coa() to play its role. Pass
arguments to it, assign it’s return, a data.frame, to
coa.
Now, let’s see the data in coa:
head(coa)
## session1 session2 final objective1 objective2 objective3
## 1 83 64 71.6 0.7861538 0.716 0.6745455
## 2 61 83 74.2 0.6607692 0.742 0.7900000
## 3 75 77 76.2 0.7546154 0.762 0.7663636
## 4 91 85 87.4 0.8961538 0.874 0.8609091
## 5 63 81 73.8 0.6715385 0.738 0.7772727
## 6 68 84 77.6 0.7169231 0.776 0.8109091coa has grades of each session (i.e.,
session1, session2), final grade (i.e.,
final), and achievements of each objectives (i.e.,
objective1, objective2,
objective3).
coa also has an attribute named “weights”, which
contains session_weights,
objective_weights_matrix, and
weighted_objective_weights_matrix.
weighted objective_weights matrix is calculated by
weighting objective_weights matrix using
session_weights, and was used in assess_coa()
to calculate achievements. Make sure
objective_weights_matrix is the same as you intended.
attr(coa, "weights")
## $session_weights
## [1] 0.4 0.6
##
## $objective_weights_matrix
## session1 session2
## objective1 0.5 0.1
## objective2 0.3 0.3
## objective3 0.2 0.6
##
## $weighted_objective_weights_matrix
## session1 session2
## objective1 0.20 0.06
## objective2 0.12 0.18
## objective3 0.08 0.36Then we could report overall achievements:
coa_overall <- data.frame(
objective = factor(1:nrow(attr(coa, "weights")[[2]])),
achievement = round(colMeans(coa[row.names(attr(coa, "weights")[[2]])]), 2)
)
ggplot(coa_overall, aes(objective, achievement)) +
geom_bar(aes(fill = objective), stat = "identity") +
geom_text(aes(y = achievement, label = achievement), vjust = 0) +
coord_cartesian(ylim = c(0.6, 1)) +
theme_minimal()
Then we could report individual achievements:
# add individual ID
coa$ID <- as.numeric(row.names(coa))
# wide format to long format
coa_long <- pivot_longer(coa,
cols = starts_with("objective"),
values_to = "achievement",
names_to = "objective",
names_prefix = "objective")
# plot individual achievement around the overall mean for each objective
ggplot(coa_long , aes(ID, achievement, color = objective)) +
geom_point(size = 2) +
geom_hline(aes(yintercept = achievement, color = objective), linewidth = 2, data = coa_overall) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_wrap(~ objective) +
coord_cartesian(ylim = c(0, 1)) +
theme_minimal()
# visualize the distribution of achievements for each objective
ggplot(coa_long, aes(achievement, fill = objective)) +
geom_histogram(aes(y = after_stat(count / sum(count))), bins = 20) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_wrap(~ objective) +
theme_minimal()