Exploring Student Engagement
Temam Timite
2024-05-03
Introduction
In my project, I chose to analyze the XAPI-Edu dataset obtained from [Kaggle]. This dataset contains information on student interactions and engagement levels in an educational setting. ## Data Exploration
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("https://www.westend61.de/en/photo/CAIF02492/students-raising-hands-in-class")## [1] "https://www.westend61.de/en/photo/CAIF02492/students-raising-hands-in-class"#Load the necessary libraries
library(readr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionLoad the dataset
setwd("C:/Users/MCuser/Desktop/data")
dataset <- read_csv("xAPI-Edu-Data.csv.csv")## Rows: 480 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (13): gender, NationalITy, PlaceofBirth, StageID, GradeID, SectionID, To...
## dbl (4): raisedhands, VisITedResources, AnnouncementsView, Discussion
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View the structure of the dataset
str(dataset)## spc_tbl_ [480 × 17] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ gender : chr [1:480] "M" "M" "M" "M" ...
## $ NationalITy : chr [1:480] "KW" "KW" "KW" "KW" ...
## $ PlaceofBirth : chr [1:480] "KuwaIT" "KuwaIT" "KuwaIT" "KuwaIT" ...
## $ StageID : chr [1:480] "lowerlevel" "lowerlevel" "lowerlevel" "lowerlevel" ...
## $ GradeID : chr [1:480] "G-04" "G-04" "G-04" "G-04" ...
## $ SectionID : chr [1:480] "A" "A" "A" "A" ...
## $ Topic : chr [1:480] "IT" "IT" "IT" "IT" ...
## $ Semester : chr [1:480] "F" "F" "F" "F" ...
## $ Relation : chr [1:480] "Father" "Father" "Father" "Father" ...
## $ raisedhands : num [1:480] 15 20 10 30 40 42 35 50 12 70 ...
## $ VisITedResources : num [1:480] 16 20 7 25 50 30 12 10 21 80 ...
## $ AnnouncementsView : num [1:480] 2 3 0 5 12 13 0 15 16 25 ...
## $ Discussion : num [1:480] 20 25 30 35 50 70 17 22 50 70 ...
## $ ParentAnsweringSurvey : chr [1:480] "Yes" "Yes" "No" "No" ...
## $ ParentschoolSatisfaction: chr [1:480] "Good" "Good" "Bad" "Bad" ...
## $ StudentAbsenceDays : chr [1:480] "Under-7" "Under-7" "Above-7" "Above-7" ...
## $ Class : chr [1:480] "M" "M" "L" "L" ...
## - attr(*, "spec")=
## .. cols(
## .. gender = col_character(),
## .. NationalITy = col_character(),
## .. PlaceofBirth = col_character(),
## .. StageID = col_character(),
## .. GradeID = col_character(),
## .. SectionID = col_character(),
## .. Topic = col_character(),
## .. Semester = col_character(),
## .. Relation = col_character(),
## .. raisedhands = col_double(),
## .. VisITedResources = col_double(),
## .. AnnouncementsView = col_double(),
## .. Discussion = col_double(),
## .. ParentAnsweringSurvey = col_character(),
## .. ParentschoolSatisfaction = col_character(),
## .. StudentAbsenceDays = col_character(),
## .. Class = col_character()
## .. )
## - attr(*, "problems")=<externalptr>Summary statistics
summary(dataset)## gender NationalITy PlaceofBirth StageID
## Length:480 Length:480 Length:480 Length:480
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## GradeID SectionID Topic Semester
## Length:480 Length:480 Length:480 Length:480
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Relation raisedhands VisITedResources AnnouncementsView
## Length:480 Min. : 0.00 Min. : 0.0 Min. : 0.00
## Class :character 1st Qu.: 15.75 1st Qu.:20.0 1st Qu.:14.00
## Mode :character Median : 50.00 Median :65.0 Median :33.00
## Mean : 46.77 Mean :54.8 Mean :37.92
## 3rd Qu.: 75.00 3rd Qu.:84.0 3rd Qu.:58.00
## Max. :100.00 Max. :99.0 Max. :98.00
## Discussion ParentAnsweringSurvey ParentschoolSatisfaction
## Min. : 1.00 Length:480 Length:480
## 1st Qu.:20.00 Class :character Class :character
## Median :39.00 Mode :character Mode :character
## Mean :43.28
## 3rd Qu.:70.00
## Max. :99.00
## StudentAbsenceDays Class
## Length:480 Length:480
## Class :character Class :character
## Mode :character Mode :character
##
##
## Distribution of variables
Load necessary libraries
library(plotly)## Warning: package 'plotly' was built under R version 4.3.3## Loading required package: ggplot2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutCreate an interactive histogram with color
histogram <- plot_ly(x = ~dataset$raisedhands, type = "histogram", marker = list(color = "skyblue"))Customize the layout
layout(histogram, title = "Interactive Histogram of Raised Hands", xaxis = list(title = "Number of Raised Hands"))Show the plot
histogramCheck for missing values
sum(is.na(dataset))## [1] 0Handle missing values (if necessary)
dataset <- dataset %>% na.omit()Select specific variables
dataset_selected <- dataset %>% select(raisedhands, VisITedResources)Filter rows based on conditions
dataset_filtered <- dataset %>% filter(raisedhands > 50)Summarize data
dataset_summary <- dataset %>% group_by(raisedhands) %>% summarize(mean(VisITedResources))Load ggplot2 library
library(ggplot2)Create a scatter plotwith color
scatter_plot <- ggplot(dataset, aes(x = raisedhands, y = VisITedResources, color = Class)) +
geom_point() +
labs(x = "Raised Hands", y = "Visited Resources", color = "Class") +
theme_minimal()Show the scatter plot
scatter_plot
In this scatter plot, we’re visualizing the relationship between the
number of raised hands and the number of visited resources for students,
with each point colored by the student’s class (L, M, H). The scatter
plot visualizes the relationship between the number of raised hands and
the number of visited resources for students in the dataset. Each point
represents a student, with the x-axis indicating the number of raised
hands and the y-axis indicating the number of visited resources. The
points are colored based on the student’s class (L = Low, M = Medium, H
= High). From the scatter plot, we can observe that students who have a
higher number of raised hands also tend to have a higher number of
visited resources, indicating a potential positive correlation between
classroom participation (raised hands) and engagement with course
materials (visited resources). Additionally, we can see variations in
this relationship across different class levels, with some classes
showing stronger correlations than others.
Create a bar plot with color
bar_plot <- ggplot(dataset, aes(x = gender, fill = gender)) +
geom_bar() +
labs(x = "Gender", y = "Count", fill = "Gender") +
theme_minimal()Show the bar plot
bar_plot
This bar plot displays the distribution of genders among the students,
with bars colored by gender.
The bar plot illustrates the distribution of genders among the students in the dataset. Each bar represents the count of students for each gender category (Male or Female), with bars colored accordingly. From the bar plot, we can observe that the dataset contains a relatively balanced distribution of genders, with a slightly higher count of male students compared to female students. This visualization provides insights into the gender composition of the student population, which can be useful for understanding demographic characteristics and potentially exploring gender-specific trends or patterns in student performance.
Check for non-numeric values in the “Class” variable
unique(dataset$Class)## [1] "M" "L" "H"Check for non-numeric values in other variables
sapply(dataset[, c("raisedhands", "VisITedResources", "AnnouncementsView", "Discussion")], function(x) any(!is.numeric(x)))## raisedhands VisITedResources AnnouncementsView Discussion
## FALSE FALSE FALSE FALSEConvert “Class” to a factor
dataset$Class <- factor(dataset$Class, levels = c("L", "M", "H"))Encode “Class” as numeric
dataset$Class_numeric <- as.numeric(dataset$Class)model <- lm(Class_numeric ~ raisedhands + VisITedResources + AnnouncementsView + Discussion, data = dataset)Check the summary of the regression model
summary(model)##
## Call:
## lm(formula = Class_numeric ~ raisedhands + VisITedResources +
## AnnouncementsView + Discussion, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.44733 -0.39587 -0.09817 0.41153 1.67803
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0192914 0.0539684 18.887 < 2e-16 ***
## raisedhands 0.0070514 0.0011664 6.045 3.02e-09 ***
## VisITedResources 0.0096084 0.0010273 9.353 < 2e-16 ***
## AnnouncementsView 0.0014954 0.0012543 1.192 0.2338
## Discussion 0.0022850 0.0009459 2.416 0.0161 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5161 on 475 degrees of freedom
## Multiple R-squared: 0.5288, Adjusted R-squared: 0.5248
## F-statistic: 133.3 on 4 and 475 DF, p-value: < 2.2e-16Load the plotly library
library(plotly)Create an interactive scatter plot
interactive_plot <- plot_ly(data = dataset, x = ~raisedhands, y = ~Discussion, color = ~Class, type = "scatter", mode = "markers",
marker = list(size = 10)) %>%
layout(title = "Interactive Scatter Plot",
xaxis = list(title = "Raised Hands"),
yaxis = list(title = "Discussion"),
legend = list(title = "Class"))Show the interactive plot
interactive_ploteach point represents a student. You can hover over the points to see additional information, and the plot is colored by class (Low, Medium, High).
Visualization Analysis: The interactive scatter plot visualizes the relationship between the number of raised hands and discussion participation among students. It reveals a positive correlation between the two variables, with students who actively participate in discussions also showing a higher tendency to raise their hands in class. The bar plot representing the gender distribution provides insights into the gender composition of the student population, showing a relatively balanced distribution with a slightly higher count of male students. Lastly, the box plot illustrates variations in the distribution of raised hands among different classes, with higher medians observed in the High class, indicating potentially higher levels of engagement.
In researching the topic of student engagement, I came across several studies highlighting its significance in academic achievement and overall student success. According to a study by Fredricks et al. (2011) [https://www.sciencedirect.com/science/article/abs/pii/S0360131515300427], engaged students are more likely to demonstrate higher levels of academic performance and retention. Additionally, research by Skinner and Belmont (1993) [https://www.researchgate.net/publication/232483889_Motivation_in_the_classroom_Reciprocal_effect_of_teacher_behavior_and_student_engagement_across_the_school_year] suggests that student engagement positively impacts classroom dynamics and fosters a conducive learning environment. These findings underscore the importance of understanding and promoting student engagement in educational practices.