assess_coa: Assess course objective achievement

In Keng: Knock Errors Off Nice Guesses

Assess course objective achievement

Description

Assess course objective achievement

Usage

assess_coa(data, session_weights, objective_weights1, ...)

Arguments

data	A wide-format data.frame that only contains student's grades of each session. assess_coa assumes the highest score for each session and the final grade is 100, given that weights are assigned by users.
session_weights	A vector that Weights sessions for the final grade. The length of session_weights is the number of sessions. Typically, the final grade is based on the regular grade and final exam grade, in which condition the length of session_weights is 2. The range of each weight should be 0-1. The sum of session_weights should be 1.
objective_weights1	A vector that Weights course objectives for session 1. The length of objective_weights1 is the number of course objectives. The range of each weight should be 0-1. The sum of objective_weights1 should be 1.
...	objective_weights2, objective_weights3, ... Other vectors that Weight course objectives for session1, session2, ... The number of objective_weights* arguments should be equal to the length of session_weights.

Value

A data.frame containing grades of each session, final grades, and achievements of each objective. This data.frame also has an attribute named "weights" that contains a list of session_weights, objective_weights_matrix, and weighted_objective_weights_matrix

Examples

data <- data.frame(
  session1 = 60 + sample.int(40, 100, 1),
  session2 = 60 + sample.int(40, 100, 1),
  session3 = 60 + sample.int(40, 100, 1)
)
session_weights    <- c(0.2, 0.3, 0.5)
objective_weights1 <- c(0.1, 0.4, 0.5)
objective_weights2 <- c(0.2, 0.2, 0.6)
objective_weights2 <- c(0.3,   0, 0.7)
coa <- assess_coa(
  data,
  session_weights,
  objective_weights1,
  objective_weights2,
  objective_weights2
)
head(coa)
attr(coa, "weights")
colMeans(coa[row.names(attr(coa, "weights")[[2]])])

Keng documentation built on Sept. 1, 2026, 5:07 p.m.