library(tidyverse)
library(readxl)
library(here)
library(janitor)
library(datapasta)
library(ggeasy)
library(plotly)

PSYC2061 self management 2019

This data is copied from the activity log page on moodle and includes how many times each resource was viewed by how many users.

read/clean data

last <- read_excel(here("research_conferences", "ausplat2019_selfManagement", "sm_2019.xlsx"))

last_clean <- last %>%
 mutate(test = str_split(engagement, pattern = "by")) %>%
  separate(test, into = c("view", "user"), sep = ",") %>%
  mutate(users = parse_number(user)) %>%
  mutate(views = parse_number(view)) %>%
  select(-user, -view, -engagement) %>%
  mutate(resource = row_number()) %>%
  relocate(resource, .before = "activity") %>%
  mutate(percent = (users/446)*100) %>%
  mutate(jit_yn = case_when(!is.na(jit_week) ~ "yes", 
                            is.na(jit_week) ~ "no"))

### fix data types

last_clean$jit_week <- fct_relevel(last_clean$jit_week, c("week0", "week1", "week7", "week 8", "week10"))

levels(last_clean$jit_week)
## [1] "week0"  "week1"  "week7"  "week 8" "week10"
last_clean$topic <- fct_relevel(last_clean$topic, c("goals", "time", "study", "procrastination",  "feedback", "stressors", "balance", "mindfulness", "emotions"))

levels(last_clean$topic)
## [1] "goals"           "time"            "study"           "procrastination"
## [5] "feedback"        "stressors"       "balance"         "mindfulness"    
## [9] "emotions"

backstory

In 2019 we were using the Moodle section, which has 62 resources, grouped into 9 categories. I chose 5 of my favourite resources to highlight in a just in time (JIT) way to students in Week 0, 1, 7, 8 and 10.

These resources were picked because they were likely to meet student need, given what was happening in the course at that time.

  • week 0 goal setting
  • week 1 assessment time management
  • week 7 stressors
  • week 8 post assessment reflection
  • week 10 study strategies

Links to each of these resources appeared in the relevant Moodle week section. The other resources were available at any time in a separate Moodle section.

plots 2019

users by topic

last_clean %>%
  ggplot(aes(x = resource, y = users, colour = topic, shape = jit_yn)) +
  geom_point(size = 2) +
  scale_shape_manual(values = c(16, 3)) +
  labs(title = "Number of students viewing each of the \n 62 self-management activities") +
  theme_classic() +
  scale_y_continuous(limits = c(0, 500)) +
  labs(y = "Number of students", x = "Resource", caption = "data from N = 446, T1 2019") +
  geom_hline(yintercept = 446)

This plot illustrates the total number of students that engaged with each of the self management resources; those that were highlighted each week are stars and those that appeared in the self management section are dots.

Resources that were highlighted JIT (stars) received more engagement than those in a separate section.

as percent

last_clean %>% 
  filter(!is.na(jit_week)) %>%
  ggplot(aes(x = jit_week, y = percent, fill = jit_week)) +
  geom_col() +
  theme_classic() +
  easy_remove_legend() +
  labs(y = "Percent of students", x = "Week") +
  scale_y_continuous(limits = c(0,100), expand = c(0,0)) +
   labs(title = "Percent of students viewing JIT \n activities each week", caption = "data from N = 446, T1 2019")

This plot illustrates engagement with just the JIT resources expressed as a percentage of the student cohort. A high proportion of students engaged with JIT resources early in the term, however this tapered off as the term went on.

PSYC2061 self management 2020

read/clean data

this <- read_excel(here("research_conferences", "ausplat2019_selfManagement", "sm_2020.xlsx"))

this_clean <- this %>%
   mutate(test = str_split(engagement, pattern = "by")) %>%
  separate(test, into = c("view", "user"), sep = ",") %>%
  mutate(users = parse_number(user)) %>%
  mutate(views = parse_number(view)) %>%
  select(-user, -view, -engagement) %>%
  mutate(resource = row_number()) %>%
  relocate(resource, .before = "activity") %>%
  mutate(percent = (users/403)*100) %>%
  mutate(jit_yn = case_when(!is.na(jit_week) ~ "yes", 
                            is.na(jit_week) ~ "no")) %>%
  filter(resource %in% 1:37) #including just core sm resources

### fix data types

this_clean$topic <- fct_relevel(this_clean$topic, c("time_goals", "study", "paralysing_ps", "feedback", "stressors"))

levels(this_clean$topic)
## [1] "time_goals"    "study"         "paralysing_ps" "feedback"     
## [5] "stressors"
this_clean$jit_week <- fct_relevel(this_clean$jit_week, c("w0", "w1", "w2", "w3", "w4", "w5", "w6", "w7", "w8", "w10"))

levels(this_clean$jit_week)
##  [1] "w0"  "w1"  "w2"  "w3"  "w4"  "w5"  "w6"  "w7"  "w8"  "w10"

backstory

In 2020, we revised the Self Management moodle section, paring down the resources to reduce repetition and grouping the resources into 5 key categories.

Following the success of JIT resources in 2019, I chose more resources to highlight, including a link to 2-3 relevant resources in each week section

  • Week 0 - goals, getting the most from lectures
  • Week 1 - assessment time management, note taking
  • Week 2 - procrastination, prioritisting to do lists
  • Week 3 - stressors, perfectionism
  • Week 4 - 168 time management, working w feedback
  • Week 5 - mindfulness, feedback, study tips
  • Week 6 - stress management
  • Week 7 - study strategies, reflecting on assessment
  • Week 8 - study tips, problem solving
  • Week 9 - procrastination
  • Week 10 - exam stress, study strategies

plots 2020

users by topic

plot2020 <- this_clean %>%
  ggplot(aes(x = resource, y = users, colour = topic, shape = jit_yn, label = activity)) +
  geom_point(size = 2) +
  scale_shape_manual(values = c(16, 3)) +
  scale_y_continuous(limits = c(0, 500)) +
  geom_hline(yintercept = 403) +
  theme_classic() +
  labs(title = "Number of students viewing each of the \n 37 self-management activities") +
  labs(y = "Number of students", x = "Resource", caption = "data from N = 403, T1 2020") 

plot2020

This plot illustrates the total number of students that engaged with each of the self management resources; those that were highlighted each week are stars and those that appeared in the self management section are dots.

Engagement with sm resources was lower in 2020 than 2019 (pandemic). The effect of JIT highlighting was also less pronounced. Some JIT resources got lots of engagement, others no more than the moodle section resources.

as percent

justjit <- this_clean %>% 
  filter(!is.na(jit_week)) %>%
  group_by(jit_week) %>%
  summarise(ave_percent = mean(percent))

justjit %>%
  ggplot(aes(x = jit_week, y = ave_percent, fill = jit_week)) +
  geom_col() +
  theme_classic() +
  labs(y = "Percent of students", x = "Week") +
  easy_remove_legend() +
  scale_y_continuous(limits = c(0,100), expand = c(0,0)) +
   labs(title = "Percent of students viewing JIT activities \n each week", caption = "data from N = 403, T1 2020")

This plot averages the percent of students engaging with JIT each week. As in 2019, the engagement declines across the term.

An aside

Which JIT resources did students engage with in 2020?

ggplotly(plot2020)

More than 200/403 students engaged with…

Between 100-200/403 students engaged with…

These JIT resources got no more engagement than non highlighted resources…

NEXT YEAR- not so many, just one per week.