##Introduction: This purpose of exploring this data set is to determine if time spent per week on the LMS by students can be helpful when attempting to redesign a course. This is data that would be helpful to professors and learning designers so that redesign can be based around real student data and show how students are interacting with the course. The question that this analysis seeks to answer is: how can LMS time data guide course redesign?
This data set shows how many hours were spent on the LMS each week, by each individual student. There are 3 main variables of this data set: The student, the week, and the hours spent per week. The students in this data set are provided with a number to keep student data both private and distinct. The hours are logged by the LMS to show how many hours are spent on the platform each week. The week allows the data to be sorted and displayed in a way that allows the user to see the hours spent on the LMS for each distinct week.
##Summary Statistics
summary_data <-summary(data[,])summary_data
Student_ID Week_1 Week_2 Week_3
Length:40 Min. : 6.00 Min. : 6.00 Min. : 6.00
Class :character 1st Qu.: 9.00 1st Qu.: 8.00 1st Qu.: 9.75
Mode :character Median :13.00 Median :10.50 Median :12.50
Mean :12.32 Mean :10.75 Mean :12.53
3rd Qu.:15.00 3rd Qu.:13.00 3rd Qu.:16.00
Max. :20.00 Max. :20.00 Max. :19.00
Week_4 Week_5 Week_6 Week_7
Min. : 6.00 Min. : 6.00 Min. : 6.00 Min. : 6.00
1st Qu.:12.00 1st Qu.: 8.50 1st Qu.: 9.00 1st Qu.: 8.75
Median :14.50 Median :13.50 Median :15.00 Median :14.00
Mean :14.28 Mean :13.18 Mean :13.22 Mean :13.60
3rd Qu.:18.00 3rd Qu.:17.00 3rd Qu.:17.00 3rd Qu.:18.25
Max. :20.00 Max. :20.00 Max. :20.00 Max. :20.00
Week_8 Week_9 Week_10 Week_11
Min. : 6.00 Min. : 6.00 Min. : 6.00 Min. : 6.00
1st Qu.:10.75 1st Qu.:10.00 1st Qu.: 9.00 1st Qu.:10.75
Median :13.50 Median :14.00 Median :13.00 Median :14.00
Mean :13.05 Mean :13.05 Mean :12.75 Mean :13.47
3rd Qu.:16.00 3rd Qu.:16.00 3rd Qu.:16.00 3rd Qu.:17.00
Max. :20.00 Max. :20.00 Max. :20.00 Max. :20.00
Week_12 Week_13 Week_14 Week_15
Min. : 6.00 Min. : 6.00 Min. : 6.00 Min. : 6.00
1st Qu.: 8.00 1st Qu.: 9.75 1st Qu.:10.75 1st Qu.:10.75
Median :12.00 Median :14.00 Median :14.00 Median :15.00
Mean :12.12 Mean :12.90 Mean :13.70 Mean :13.75
3rd Qu.:15.00 3rd Qu.:16.00 3rd Qu.:17.25 3rd Qu.:17.00
Max. :19.00 Max. :20.00 Max. :20.00 Max. :20.00
Week_16
Min. : 6.00
1st Qu.:10.00
Median :15.00
Mean :13.65
3rd Qu.:17.00
Max. :20.00
The purpose of using summary statistics is to take a deeper look at the spread of student time data. Being able to see both the average time and the quartiles allows a better understanding of which weeks are seeing a spike or decrease in overall time spent on the LMS platform. This shows if the spike was caused by an outlier of if the numbers were higher or lower across the board.
##Calculating mean of hours spent each week
week_cols <-grep("^Week_", names(data), value =TRUE)average_hours <-colMeans(data[, week_cols])average_hours
ggplot(average_hours_table, aes(x = Week, y = Average_Hours_Spent)) +geom_bar(stat ="identity", fill ="Blue", color ="white") +labs(title ="Average Hours Spent per Week ",x ="Semester Week",y ="Average Hours" ) +theme_minimal() +theme(plot.title =element_text(size =12, face ="bold", hjust =0.5),axis.text.x =element_text(angle =45, hjust =1) )
This graph shows that most weeks students spent around 13 hours on the LMS platform. The weeks that show the largest variance in time are weeks 2, 4 and 12. Weeks 2 and 12 are below that 15 hours average which suggests that content may be less engaging those weeks.This may suggest that the designer or professor may need to revise this week to include more interactive components within that week. Week 4 shows that students spent more time on the LMS by an average of 2 hours. This suggests that this week may need to be revised to ensure that directions and activities are clear and that there is an appropriate amount of material for a single week. This graph allows for the user to get a general idea of the time students are spending per week. This is not the most precise way to analyze the average time spent.
Trend Line Graph Set up
ggplot(average_hours_table, aes(x = Week, y = Average_Hours_Spent, group =1)) +geom_line(color ="Black", linewidth =1.0) +geom_point(color ="Blue", size =3) +labs(title ="Trend of Average Hours Spent per Week",x ="Semester Week",y ="Average Hours" ) +theme_minimal() +theme(plot.title =element_text(size =16, face ="bold", hjust =0.5),axis.text.x =element_text(angle =45, hjust =1) )
This trend line is in my opinion the most helpful visualization within this analysis. This graph allows the instructor or designer to see a more precise picture of exactly what the average time spent per week is. This allows a clear comparison between week two, which is almost 2 full hours less than week one and three. This also shows that students on average spent less time on week 12. Students spent just over 12 hours on this week while spending 13.5 hours on week 11 and just under 13 hours on week 13. This shows that these weeks may need to be redesigned to be more engaging and include more interactive components. This graph also shows that students spent on average around 14.25 hours on week 4 which is between 1 and 2 more hours than other weeks. This suggests that this week may need to be redesigned to be less interactive and may need some clarification in directions.
Findings summary
To answer the question of can student time spent on LMS guide redesign, this data set shows that a few key weeks may need to be redesigned based on the average time spent by students on that week. In this analysis, summary statistics were taken from each week looking at the mean, median,1st and 3rd quartiles,minimums, and maximum. Visualizations were also completed to look at each week’s average in the form of line graph and a bar graph. The analysis reveals that weeks 2 and 4 may need to be redesigned based on their deviation from the average time spent on the LMS, with week 2 needing more engagement and week 4 possibly needing less or clarification on instructions or less material.
