Effect of content quality and learning engagement on student satisfaction in online courses

PLS-SEM (seminr) for Education and Learning

Field: Education and LearningLevel: Doctoral dissertation (S3)Method: PLS-SEM (seminr)

0 views

Testing the path from content quality to learning engagement to student satisfaction with PLS-SEM using synthetic data for demonstration. PLS-SEM (Partial Least Squares Structural Equation Modelling) tests the measurement model of latent constructs and the structural links among them, including mediation, using bootstrapping. It suits questionnaire-based theses with Likert items, moderate samples and a predictive or exploratory aim.

Effect of content quality and learning engagement on student satisfaction in online courses: result sheet

Project info

Field of studyEducation and Learning
LevelDoctoral dissertation (S3)
Method typeSEM and Psychometrics
Estimated build time± 7 working days
Toolsseminr::constructs, seminr::composite, seminr::relationships, seminr::estimate_pls, seminr::bootstrap_model, seminr::specific_effect_significance, ggplot2
PriceUS$31 - US$200

Research goal

Testing the path from content quality to learning engagement to student satisfaction with PLS-SEM using synthetic data for demonstration.

Variables

Role	Variable	Unit
Main	Student satisfaction	Likert 1-5
Supporting 1	Content quality	Likert 1-5
Supporting 2	Learning engagement	Likert 1-5

Example result

0.451R2 Student satisfa.
0.37Path Content quality -> Y
<0.001Path p-value X1 -> Y
0.221Indirect effect
0.649Min AVE
0.76Max HTMT

The constructs are reliable and valid (minimum rhoC 0.858, minimum AVE 0.649, maximum HTMT 0.760). Content quality has a direct effect on Student satisfaction (beta = 0.370, p < 0.001), and its indirect effect through Learning engagement is 0.221 (95% CI 0.114 to 0.334), which is significant. The model explains 45.1% of the variance in Student satisfaction.

Computed from illustrative synthetic data, not from a real study.

About PLS-SEM (seminr)

PLS-SEM (Partial Least Squares Structural Equation Modelling) tests the measurement model of latent constructs and the structural links among them, including mediation, using bootstrapping. It suits questionnaire-based theses with Likert items, moderate samples and a predictive or exploratory aim.

Steps

1Prepare the Likert item data and assign each item to its construct
2Define the reflective measurement model (constructs, composite, mode_A) and the structural model (relationships)
3Estimate the model with seminr::estimate_pls
4Assess the measurement model: outer loadings, rhoC, AVE and HTMT
5Run bootstrap_model to obtain t statistics, p-values and confidence intervals for the paths
6Test the indirect effect with specific_effect_significance
7Report R2 and the path coefficients in a path diagram

Assumptions to check

Reflective indicators reflect their construct (loadings ideally above 0.7)
Convergent validity (AVE above 0.5) and discriminant validity (HTMT below 0.85 to 0.90) hold
No severe multicollinearity among predictor constructs
The sample is large enough for the largest number of paths pointing at one construct

Reading and reporting the results

Report the measurement model first (loadings, rhoC, AVE, HTMT), then the structural model (path coefficients, bootstrap t and p, and R2). The indirect effect is significant when its 95% bootstrap interval excludes zero; compare it with the direct effect to judge partial or full mediation.

Example R code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

Show code

# Contoh kode R: PLS-SEM (seminr)
suppressPackageStartupMessages({
  library(ggplot2)
  library(patchwork)
  library(seminr)
})
set.seed(42)
# ---- Gaya grafik ----
pal <- c("#1f6feb", "#e8590c", "#2f9e44", "#ae3ec9", "#f59f00")
warna <- list(bg = "#f4f6fa", panel = "#ffffff", teks = "#1f2933", redup = "#5f6b78", grid = "#e3e8ee", bar = "#1d3557")
tata <- 1
theme_set(theme_minimal(base_size = 11) + theme(legend.position = "bottom"))

# ---- Data sintetis: skor butir Likert 1-5 dari tiga konstruk laten ----
n <- 200
a <- runif(1, 0.4, 0.7)                                         # jalur X1 -> X2 (mediator)
b <- runif(1, 0.3, 0.55)                                        # jalur X2 -> Y
cp <- runif(1, 0.1, 0.35)                                       # jalur langsung X1 -> Y
e_x1 <- rnorm(n)
e_x2 <- a * e_x1 + rnorm(n, 0, sqrt(1 - a^2))
e_y <- cp * e_x1 + b * e_x2 + rnorm(n, 0, runif(1, 0.55, 0.75))
e_y <- as.numeric(scale(e_y))
# butir Likert: muatan acak 0.65-0.9, ambang dibuat agar rata-rata butir sekitar 3.4-3.8
likert <- function(eta, k) {
  sapply(seq_len(k), function(j) {
    l <- runif(1, 0.65, 0.9)
    s <- l * eta + rnorm(length(eta), 0, sqrt(1 - l^2))
    findInterval(s, sort(c(-2, -1.1, -0.2, 0.9) + runif(1, -0.3, 0.2))) + 1
  })
}
k <- c(4, 3, 4)                                                 # jumlah butir: X1, X2, Y
d <- data.frame(likert(e_x1, k[1]), likert(e_x2, k[2]), likert(e_y, k[3]))
names(d) <- c(paste0("content_quality", "_", 1:k[1]), paste0("learning_engagement", "_", 1:k[2]), paste0("student_satisfaction", "_", 1:k[3]))
write.csv(d, "data_contoh.csv", row.names = FALSE)

# ---- Model pengukuran (reflektif, mode A) dan model struktural ----
mm <- constructs(
  composite("content_quality", multi_items("content_quality_", 1:k[1]), weights = mode_A),
  composite("learning_engagement", multi_items("learning_engagement_", 1:k[2]), weights = mode_A),
  composite("student_satisfaction", multi_items("student_satisfaction_", 1:k[3]), weights = mode_A))
sm <- relationships(
  paths(from = "content_quality", to = c("learning_engagement", "student_satisfaction")),
  paths(from = "learning_engagement", to = "student_satisfaction"))
pls <- estimate_pls(data = d, measurement_model = mm, structural_model = sm)
s <- summary(pls)
print(s$loadings); print(s$reliability); print(s$validity$htmt); print(s$paths)

# ---- Bootstrap untuk signifikansi jalur dan efek tidak langsung ----
bt <- bootstrap_model(seminr_model = pls, nboot = 400, cores = 1, seed = 123)
sb <- summary(bt)
print(sb$bootstrapped_paths)
ind <- specific_effect_significance(bt, from = "content_quality", through = "learning_engagement", to = "student_satisfaction", alpha = 0.05)
print(ind)
bp <- sb$bootstrapped_paths
p_jalur <- 2 * pnorm(-abs(bp[, "T Stat."]))                     # p dua sisi dari statistik t bootstrap
koef <- bp[, "Original Est."]
nm_j <- rownames(bp)                                            # "X1  ->  X2" dst.
j_a <- grep("^content_quality .*-> *learning_engagement$", nm_j); j_b <- grep("^learning_engagement .*-> *student_satisfaction$", nm_j); j_c <- grep("^content_quality .*-> *student_satisfaction$", nm_j)
rel <- s$reliability
ave <- rel[, "AVE"]; rhoc <- rel[, "rhoC"]
r2 <- s$paths["R^2", c("learning_engagement", "student_satisfaction")]
htmt_maks <- max(s$validity$htmt, na.rm = TRUE)
ind_est <- unname(ind["Original Est."]); ind_lo <- unname(ind["2.5% CI"]); ind_hi <- unname(ind["97.5% CI"])


# ---- Grafik ----
bintang <- function(p) ifelse(p < 0.001, "***", ifelse(p < 0.01, "**", ifelse(p < 0.05, "*", " ns")))
bungkus <- function(s) paste(head(strwrap(s, 17), 2), collapse = "\n")   # nama konstruk maks. 2 baris
nd <- data.frame(x = c(0, 1, 2), y = c(0, 1, 0), lab = c(bungkus("content_quality"), bungkus("learning_engagement"), bungkus("student_satisfaction")),
                 r2 = c("", sprintf("R2 = %.2f", r2[1]), sprintf("R2 = %.2f", r2[2])))
pan <- data.frame(x = c(0.2, 1.2, 0.36), y = c(0.2, 0.8, 0), xend = c(0.8, 1.8, 1.64), yend = c(0.8, 0.2, 0),
                  lab = sprintf("%.2f%s", koef[c(j_a, j_b, j_c)], bintang(p_jalur[c(j_a, j_b, j_c)])),
                  lx = c(0.33, 1.67, 1), ly = c(0.6, 0.6, -0.1))
p1 <- ggplot() +
  geom_segment(data = pan, aes(x, y, xend = xend, yend = yend), colour = warna$redup, linewidth = 0.9,
               arrow = arrow(length = unit(0.14, "inches"), type = "closed")) +
  geom_text(data = pan, aes(lx, ly, label = lab), colour = warna$teks, size = 3.4, fontface = "bold") +
  geom_tile(data = nd, aes(x, y), width = 0.72, height = 0.38, fill = pal[1:3]) +
  geom_text(data = nd, aes(x, y + 0.04, label = lab), colour = warna$panel, fontface = "bold", size = 3.1, lineheight = 0.9) +
  geom_text(data = nd, aes(x, y - 0.13, label = r2), colour = warna$panel, size = 2.8) +
  annotate("text", x = 1, y = -0.34, label = sprintf("Indirect effect = %.3f [%.3f, %.3f]", ind_est, ind_lo, ind_hi),
           colour = warna$teks, size = 3.2) +
  coord_cartesian(xlim = c(-0.4, 2.4), ylim = c(-0.42, 1.22)) +
  theme_void() + theme(plot.background = element_rect(fill = warna$panel, colour = NA),
                       plot.title = element_text(face = "bold", colour = warna$teks, margin = margin(4, 0, 4, 6))) +
  labs(title = "PLS path model (bootstrap, 400 resamples)")
ld <- s$loadings
dl <- data.frame(butir = rownames(ld), muatan = apply(ld, 1, function(v) v[v != 0][1]))
# nama konstruk pendek (<= 18 huruf): kata sambung dibuang dan kata panjang disingkat bila perlu
singkat <- function(s, w = 18) {
  k <- strsplit(s, " ")[[1]]
  if (nchar(s) > w) k <- k[!tolower(k) %in% c("of", "to", "the", "and", "for", "in", "on", "with")]
  i <- length(k)
  while (nchar(paste(k, collapse = " ")) > w && i >= 1) {
    if (nchar(k[i]) > 5) k[i] <- paste0(substr(k[i], 1, 4), ".")
    i <- i - 1
  }
  substr(paste(k, collapse = " "), 1, w)
}
nm_k <- make.unique(unname(sapply(c("content_quality", "learning_engagement", "student_satisfaction"), singkat)), sep = " ")
dl$konstruk <- rep(nm_k, k)
dl$label <- paste0(dl$konstruk, " #", sub(".*_", "", dl$butir))
dl$label <- factor(dl$label, levels = rev(unique(dl$label)))
dl$konstruk <- factor(dl$konstruk, levels = nm_k)
p2 <- ggplot(dl, aes(muatan, label, fill = konstruk)) +
  geom_col(width = 0.7) +
  geom_vline(xintercept = 0.7, linetype = "dashed", colour = warna$teks) +
  geom_text(aes(label = sprintf("%.2f", muatan)), hjust = -0.15, colour = warna$teks, size = 2.7) +
  scale_fill_manual(values = pal[1:3], name = NULL) +
  scale_x_continuous(limits = c(0, 1.1), breaks = seq(0, 1, 0.2)) +
  labs(title = "Outer loadings (threshold 0.7)", x = "Loading", y = NULL)
dq <- data.frame(konstruk = factor(rep(nm_k, 2), levels = nm_k),
                 ukuran = rep(c("AVE", "rhoC"), each = 3), nilai = c(ave[c("content_quality", "learning_engagement", "student_satisfaction")], rhoc[c("content_quality", "learning_engagement", "student_satisfaction")]))
p3 <- ggplot(dq, aes(konstruk, nilai, fill = ukuran)) +
  geom_col(position = position_dodge(0.75), width = 0.7) +
  geom_hline(yintercept = c(0.5, 0.7), linetype = "dashed", colour = warna$redup) +
  scale_fill_manual(values = pal[4:5], name = NULL) +
  scale_y_continuous(limits = c(0, 1)) +
  labs(title = "Convergent validity and reliability", x = NULL, y = "Value")
p_hasil <- if (tata == 1) p1 / (p2 | p3) + plot_layout(heights = c(1.1, 1)) else (p1 | p2) / p3 + plot_layout(heights = c(1.4, 1))

print(p_hasil)

The project file you can download

The original project file is available. It is not just the picture above: you get the working R project. It can be downloaded after payment, and we send it by WhatsApp or email.

ZIP: analisis.R + data_contoh.csv + BACA-DULU.txt

What is inside the ZIP

The complete analysis script (analisis.R), commented step by step, ready to run in RStudio.
A sample dataset (data_contoh.csv) so the script runs straight away.
A short guide (BACA-DULU.txt): which packages to install, how to run it and how to swap in your own data.

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

Package 1

Code and Explanation

US$31

excl. 11% VAT

Duration: 7 working days

Output: R files + result figures

Deliverables

Fully commented R code (.R) for PLS-SEM (seminr)
Example data and step-by-step run instructions
Ready-to-use result figures and metrics table
Short explanation of the method, its assumptions and how to read the results

Choose Package 1

Package 2 · most chosen

Your Data Analysis + Interpretation

US$91

excl. 11% VAT

Duration: 13 working days

Output: Code + analysis + interpretation notes

Includes all of Package 1, plus

Adapting the code to your own research dataset (CSV or Excel)
Assumption checks, result validation and data cleaning
Tables and figures formatted for your manuscript
Interpretation notes for the methods and discussion sections, for you to write up in your own words

Choose Package 2

Package 3

Full Guidance

US$200

excl. 11% VAT

Duration: 18-21 working days

Output: Code + analysis + guidance until ready to present

Includes all of Packages 1 & 2, plus

Comparison against one alternative method with the reasoning for the choice
Two 60-minute online coaching sessions so you fully understand the code and results
Revisions following your supervisor feedback (up to 3 rounds)
Practice Q&A to prepare for your seminar and defence
Full documentation (README, R package list (sessionInfo)) so the results can be reproduced

Choose Package 3

Payment: 50% down payment before work starts, balance after the deliverables are handed over
Tax: Prices exclude 11% VAT
Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions

Do I get the real project file?

Yes. The original project file is a ZIP containing the complete analisis.R script, a data_contoh.csv sample dataset and a BACA-DULU.txt guide. It can be downloaded after payment, and we send it by WhatsApp or email. For your own data (Package 2 and 3) the script is adapted to your dataset.

Is this suitable for a Doctoral dissertation (S3)?

Yes. PLS-SEM (seminr) is commonly used at this level in Education and Learning. We adapt the analysis to your research question, your own data and your supervisor's requirements.

Is the result sheet real data?

No. The sheet is computed from illustrative synthetic data so you can see the kind of output R Studio gives. For your order we run the same method on your own data.

Will I understand the code?

That is the point of the packages. Every script is commented, and Package 3 includes two coaching sessions plus practice questions so you can explain the method and the results at your seminar and defence.

How is the price decided?

Each project has three packages: Package 1 (code and explanation) US$31, Package 2 (your data analysis and interpretation) US$91 and Package 3 (full guidance) US$200, excluding 11% VAT. Larger datasets, extra methods or tight deadlines adjust the quote after a short consultation.

Similar projects

See all

PreviousLinks Between Unemployment, Inflation and Minimum Wage: VAR and Granger Causality

NextSegmenting primary schools by achievement, teacher ratio and facilities using cluster analysis

The result sheet uses illustrative synthetic data. R is a programming language of the R Foundation project; RStudio and Posit are trademarks of Posit PBC.

Back to R Studio portfolio

Memuat