Predicting MOOC completion with a Naive Bayes classifier

Naive Bayes for Education and Learning

Field: Education and LearningLevel: Undergraduate thesis (S1)Method: Naive Bayes

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Classifying MOOC completion from video watching time and quizzes attempted with Naive Bayes using synthetic data for demonstration. Naive Bayes computes class probabilities with Bayes' theorem, assuming the features are independent given the class. It suits theses that need a fast, probability based classifier, for example for detection or screening tasks.

Predicting MOOC completion with a Naive Bayes classifier: result sheet

Project info

Field of studyEducation and Learning
LevelUndergraduate thesis (S1)
Method typeMachine Learning
Estimated build time± 3 working days
Toolse1071::naiveBayes, stats::glm, pROC::roc, pROC::roc.test, ggplot2
PriceUS$16 - US$106

Research goal

Classifying MOOC completion from video watching time and quizzes attempted with Naive Bayes using synthetic data for demonstration.

Variables

Role	Variable	Unit
Main	MOOC completion	class
Supporting 1	Video watching time	hours
Supporting 2	Quizzes attempted	count

Example result

0.741Accuracy (test)
0.714Precision
0.581Recall
0.641F1 score
0.82AUC Naive Bayes
0.823AUC logistic

Gaussian Naive Bayes classifies MOOC completion on the test set with 74.1% accuracy, precision of 0.714, recall of 0.581 and AUC of 0.820. The logistic regression benchmark reaches an AUC of 0.823; the DeLong test (p = 0.576) shows no significant difference in discrimination between the models.

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

About Naive Bayes

Naive Bayes computes class probabilities with Bayes' theorem, assuming the features are independent given the class. It suits theses that need a fast, probability based classifier, for example for detection or screening tasks.

Steps

1Prepare the features and code the binary class as a factor
2Split the data into stratified training and test sets
3Fit Gaussian Naive Bayes (e1071::naiveBayes) and inspect the priors and class-conditional means/SDs
4Predict class probabilities on the test set and compute accuracy, precision, recall, F1 and AUC
5Compare with logistic regression using AUC and the DeLong test
6Plot class-conditional densities, ROC curves and a calibration plot

Assumptions to check

Features are conditionally independent given the class
Continuous features are roughly normal within each class (Gaussian version)
Class proportions in the training data reflect sensible priors

Reading and reporting the results

Report test accuracy, precision, recall, F1 and AUC, then compare the AUC with logistic regression and give the DeLong p-value. The calibration plot shows whether the predicted probabilities can be read at face value.

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: Naive Bayes
suppressPackageStartupMessages({
  library(ggplot2)
  library(patchwork)
  library(e1071)
  library(pROC)
})
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: fitur kontinu berdistribusi normal bersyarat kelas ----
n <- 200
positif <- function(x, m) if (m > 0) pmax(x, 0.05 * m) else x   # cegah nilai negatif yang tidak wajar
prevalensi <- runif(1, 0.3, 0.5)
kls <- rbinom(n, 1, prevalensi)
geser1 <- sample(c(-1, 1), 1) * runif(1, 0.9, 1.5)    # beda rerata antar kelas (dalam satuan SB)
geser2 <- sample(c(-1, 1), 1) * runif(1, 0.6, 1.2)
rasio_sb <- runif(1, 1.2, 1.7)                        # kelas 1 lebih bervariasi pada X2
d <- data.frame(video_watching_time = positif(50 + 10 * (geser1 * kls + rnorm(n)), 50),
                quizzes_attempted = positif(30 + 6 * (geser2 * kls + rnorm(n) * ifelse(kls == 1, rasio_sb, 1)), 30))
d$mooc_completion <- kls
write.csv(d, "data_contoh.csv", row.names = FALSE)

# ---- Pembagian data latih/uji (70:30, bertingkat per kelas) ----
d$kelas <- factor(d$mooc_completion, levels = c(0, 1), labels = c("No", "Yes"))
idx <- unlist(lapply(split(seq_len(n), d$kelas), function(i) sample(i, round(0.7 * length(i)))))
latih <- d[idx, ]
uji <- d[-idx, ]

# ---- Analisis: Naive Bayes Gaussian (e1071::naiveBayes) ----
nb <- naiveBayes(kelas ~ video_watching_time + quizzes_attempted, data = latih)
print(nb)                                   # prior dan rerata/SB bersyarat kelas
prob_nb <- predict(nb, uji, type = "raw")[, "Yes"]
pred_nb <- factor(ifelse(prob_nb >= 0.5, "Yes", "No"), levels = c("No", "Yes"))
konf <- table(Actual = uji$kelas, Predicted = pred_nb)
print(konf)
tp <- konf["Yes", "Yes"]; fp <- konf["No", "Yes"]; fn <- konf["Yes", "No"]
akurasi <- sum(diag(konf)) / sum(konf)
presisi <- if (tp + fp > 0) tp / (tp + fp) else 0
recall <- if (tp + fn > 0) tp / (tp + fn) else 0
f1 <- if (presisi + recall > 0) 2 * presisi * recall / (presisi + recall) else 0

# ---- Pembanding: regresi logistik dengan prediktor yang sama ----
lr <- glm(kelas ~ video_watching_time + quizzes_attempted, data = latih, family = binomial)
print(summary(lr))
prob_lr <- predict(lr, uji, type = "response")
akurasi_lr <- mean(ifelse(prob_lr >= 0.5, "Yes", "No") == uji$kelas)
roc_nb <- pROC::roc(uji$kelas, prob_nb, levels = c("No", "Yes"), direction = "<", quiet = TRUE)
roc_lr <- pROC::roc(uji$kelas, prob_lr, levels = c("No", "Yes"), direction = "<", quiet = TRUE)
auc_nb <- as.numeric(pROC::auc(roc_nb))
auc_lr <- as.numeric(pROC::auc(roc_lr))
uji_delong <- pROC::roc.test(roc_nb, roc_lr, method = "delong")   # beda AUC (sampel berpasangan)
print(uji_delong)
p_delong <- if (is.finite(uji_delong$p.value)) uji_delong$p.value else 1


# ---- Grafik ----
kurva_nb <- function(kolom) {
  # kurva normal yang diestimasi Naive Bayes untuk tiap kelas
  tb <- nb$tables[[kolom]]
  xs <- seq(min(d[[kolom]]), max(d[[kolom]]), length.out = 200)
  do.call(rbind, lapply(rownames(tb), function(k) data.frame(x = xs, dens = dnorm(xs, tb[k, 1], tb[k, 2]), kelas = k)))
}
k1 <- kurva_nb("video_watching_time")
k2 <- kurva_nb("quizzes_attempted")
warna_kelas <- c(No = pal[1], Yes = pal[2])
p1 <- ggplot(latih, aes(video_watching_time, fill = kelas)) +
  geom_density(alpha = 0.35, colour = NA) +
  geom_line(data = k1, aes(x, dens, colour = kelas), linewidth = 0.9, linetype = "dashed", inherit.aes = FALSE) +
  scale_fill_manual(values = warna_kelas, name = "mooc_completion") +
  scale_colour_manual(values = warna_kelas, guide = "none") +
  labs(title = "Class-conditional density", subtitle = "Dashed = Gaussian fitted by Naive Bayes", x = "video_watching_time", y = "Density") +
  theme(legend.position = "top")
p2 <- ggplot(latih, aes(quizzes_attempted, fill = kelas)) +
  geom_density(alpha = 0.35, colour = NA) +
  geom_line(data = k2, aes(x, dens, colour = kelas), linewidth = 0.9, linetype = "dashed", inherit.aes = FALSE) +
  scale_fill_manual(values = warna_kelas, guide = "none") +
  scale_colour_manual(values = warna_kelas, guide = "none") +
  labs(title = "Class-conditional density", x = "quizzes_attempted", y = "Density")
rd <- rbind(data.frame(fpr = 1 - roc_nb$specificities, tpr = roc_nb$sensitivities, model = sprintf("Naive Bayes (AUC %.3f)", auc_nb)),
            data.frame(fpr = 1 - roc_lr$specificities, tpr = roc_lr$sensitivities, model = sprintf("Logistic (AUC %.3f)", auc_lr)))
rd <- rd[order(rd$model, rd$fpr, rd$tpr), ]
p3 <- ggplot(rd, aes(fpr, tpr, colour = model)) +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = warna$redup) +
  geom_line(linewidth = 1) +
  scale_colour_manual(values = c(pal[4], pal[3]), name = NULL) +
  labs(title = "ROC curve (test data)", x = "False positive rate", y = "True positive rate") +
  theme(legend.position = "inside", legend.position.inside = c(0.97, 0.03), legend.justification = c(1, 0),
        legend.background = element_rect(fill = warna$panel, colour = warna$grid))
kal <- rbind(data.frame(prob = prob_nb, model = "Naive Bayes"), data.frame(prob = prob_lr, model = "Logistic"))
kal$aktual <- rep(as.numeric(uji$kelas == "Yes"), 2)
kal$bin <- cut(kal$prob, breaks = seq(0, 1, by = 0.2), include.lowest = TRUE)
kb <- aggregate(cbind(prob, aktual) ~ bin + model, data = kal, FUN = mean)
kb$jumlah <- aggregate(prob ~ bin + model, data = kal, FUN = length)$prob
p4 <- ggplot(kb, aes(prob, aktual, colour = model)) +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = warna$redup) +
  geom_line(linewidth = 0.9) +
  geom_point(aes(size = jumlah), alpha = 0.85) +
  scale_colour_manual(values = c(Logistic = pal[3], `Naive Bayes` = pal[4]), guide = "none") +
  scale_size_area(max_size = 4.5, guide = "none") +
  coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) +
  labs(title = "Calibration (test data)", subtitle = "Point size = cases per probability bin",
       x = "Mean predicted P(Yes)", y = "Observed share Yes")
p_hasil <- if (tata == 1) (p1 / p2) | (p3 / p4) else (p1 | p2) / (p3 | p4)

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$16

excl. 11% VAT

Duration: 3 working days

Output: R files + result figures

Deliverables

Fully commented R code (.R) for Naive Bayes
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$47

excl. 11% VAT

Duration: 7 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$106

excl. 11% VAT

Duration: 12-15 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 Undergraduate thesis (S1)?

Yes. Naive Bayes 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$16, Package 2 (your data analysis and interpretation) US$47 and Package 3 (full guidance) US$106, excluding 11% VAT. Larger datasets, extra methods or tight deadlines adjust the quote after a short consultation.

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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.

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