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title author date output
Non-teaching rate
Erin Becker
May 18, 2016
knitr::opts_chunk$set(echo = FALSE,
                      eval = TRUE)

Instructor and trainee involvement in workshops

This analysis is meant to answer questions about the extent of the non-teaching instructor and the non-certified trainee issues.

Analysis and Data Files

The RMarkdown that generated this document: Completion_rates.Rmd
The data: In the files instructor_data_5_17_16_no_ids.csv and trainee_data_5_18_16_no_ids.csv


Questions to be answered include:

  1. What percent of fully certified instructors haven't taught?
    • Overall
    • Only those > 1 year past training
  2. What percent of one-time instructors have only taught at their home institution?
  3. What percent of trainees haven't finished checkout within 90 days of training?
    • Including online trainees
    • Excluding online trainees

Data was collected from AMY database (instructor completion) and Google sheet (trainee completion) by Greg Wilson on 5/17/16.

Note: This data can not answer Question #2.

Trainee - person who has gone through instructor training but may not have completed checkout.
Instructor - person who has gone through both instructor training and checkout.

Trainee completion rate

trainees = read.csv("", stringsAsFactors = FALSE)
trainees$End.Date = as.Date(trainees$End.Date, format = "%m/%d/%Y")
date_pulled = as.Date('2016-5-17')
trainees$Days.Since = date_pulled - trainees$End.Date # differs slightly from Greg's calculated "day's since" - mine counts from completion date
trainees$percent = round(trainees$Completed/trainees$Participants*100,2)
# looking only at trainings from at least 90 days ago
trainees_old = trainees[which(trainees$Days.Since >= 90),]
perc_co_overall = round(sum(trainees_old$Completed)/sum(trainees_old$Participants)*100,2)

# looking only at online trainings
trainees_online = trainees_old[grep("online",trainees_old$Class),]
perc_co_online = round(sum(trainees_online$Completed)/sum(trainees_online$Participants)*100,2)

# looking only at in-person trainings
trainees_in_person = trainees_old[grep("online",trainees_old$Class, invert = TRUE),]
perc_co_in_person = round(sum(trainees_in_person$Completed)/sum(trainees_in_person$Participants)*100,2)

Looking only at training sessions from at least 90 days ago:

  • overall completion r perc_co_overall%
  • in person completion r perc_co_in_person%
  • online completion r perc_co_online%. (Two events, 63% and 31%).

Most (10/14) in person events have >65% completion rate. Some (Arlington, OK, Melbourne, Florida) much lower.

Takeaway: Overall, online sessions didn't have a noticably lower completion rate than in-person sessions, but this appears to be due to a few in-person sessions have very low completion rates.

Some other events do not look on track to meeting normal completion rates (e.g. UCDavis - 84 days, 39%; UW - 68 days, 14%). More follow-up with these participants likely needed.

Wonder whether these abnormal rates are due to issues with local community, issues with how training session went, or some other factor.

Summary of completion rates per event:


Note that mean completion rate for individual events is not the same as mean overall completion rate, as the number of participants per event varies.

Instructor teaching rate

instructors = read.csv("", stringsAsFactors=FALSE)
instructors$Date = as.Date(instructors$Date)
instructors$Certified = as.Date(instructors$Certified)
date_pulled = as.Date('2016-5-17') # should be date data pulled
year_ago = date_pulled - 366 # leap year

# instructors trained over a year ago
old_inst = instructors[which(instructors$Certified < year_ago),]
names_old_inst = unique(old_inst$Person)

# non-teaching instructors
all_non_teach = instructors[which($Date)),]
names_all_non_teach = unique(all_non_teach$Person)

# non-teaching old instructors
non_teach_old = old_inst[which($Date)),]
names_non_teach_old = unique(non_teach_old$Person)

# percent of instructors trained over 1 year ago that haven't taught
perc_no_teach = round(length(names_non_teach_old)/length(names_old_inst)*100,2)

What percent of instructors trained over a year ago haven't yet taught?
Trained over one year ago: r length(names_old_inst)
Of which, haven't taught: r length(names_non_teach_old)
This is r perc_no_teach%.

What percent of total instructors haven't taught?
Total trained: r length(unique(instructors$Person))
Haven't taught: r length(names_all_non_teach)
This is r round(length(names_all_non_teach)/length(unique(instructors$Person))*100,2)%.

# instructors trained within last year
new_inst = instructors[which(instructors$Certified > year_ago),]
names_new_inst = unique(new_inst$Person)

# non-teaching new instructors
non_teach_new = new_inst[which($Date)),]
names_non_teach_new = unique(non_teach_new$Person)

# percent of instructors trained in last year who have taught
perc_new_non_teach = round(length(names_non_teach_new)/length(names_new_inst)*100,2)
perc_new_teach = 100-perc_new_non_teach

What percent of instructors trained within the past year haven't yet taught?
Trained within last year: r length(names_new_inst)
Of which, haven't taught: r length(names_non_teach_new)
This is r perc_new_non_teach%. (But many of these may have been trained very recently.)

# Test whether dates are in ascending order for each instructor
# remove instructors who haven't taught
inst_teach = instructors[which($Date) == FALSE),]
uniq_teach = unique(inst_teach$Person) # only teaching instructors
for(i in uniq_teach) {
  rows = inst_teach[which(inst_teach$Person == i),]
  test = identical(sort(rows$Date), rows$Date)
  if(test == FALSE) print(i)

# grab first line for each person (first teaching)
uniq_inst = unique(instructors$Person) # all instructors
first_teaching = data.frame()
for(i in uniq_inst) {
  rows = instructors[which(instructors$Person == i),]
  first_teaching = rbind(rows[1,], first_teaching)

# calculate time between certification and first teaching and add to df
# note some taught before certification
first_teaching$lag = as.numeric(first_teaching$Date - first_teaching$Certified)
first_teaching$num_days = as.numeric(date_pulled - first_teaching$Certified)

# count number who took longer than 1 year to teach
slow_teach = length(which(first_teaching$lag > 366))
no_teach = length(which($lag) & first_teaching$num_days > 366))
total_opt = length(which($lag) == FALSE)) + no_teach

What percent of instructors teach within their first year?
Took longer than one year to teach: r slow_teach
Haven't taught (been over a year since training): r no_teach
Total percent who didn't teach within first year: r round(sum(slow_teach + no_teach)/total_opt*100,2)%

What is the distribution of time to first teaching?

plot(density(na.exclude(first_teaching$lag)), main = "Days between training and teaching")

Note that many (r (length(which(first_teaching$lag < 0)))) instructors taught their first workshop before they were officially certified.

Excluding them:

pos_time = first_teaching[which(first_teaching$lag >0),]
plot(density(pos_time$lag), main = "Days between training and teaching (non-retroactive)")

Summary of time to teach first workshop (non-retroactive):


Are recently trained instructors on track to meet normal teaching rates?
Overall, half of instructors teach w/in r median(pos_time$lag) days.

# trainees trained between 95-120 days ago
recent_train = first_teaching[which(first_teaching$num_days < 120 & first_teaching$num_days > 95 ),]
recent_no_teach = length(which($lag)))

Trained between 95-120 days ago: r nrow(recent_train)
Of which, haven't taught: r recent_no_teach
This is r round(recent_no_teach/nrow(recent_train)*100,2)%.

Recent batch of trainees appear to be on track.


Online training sessions do not appear to have lower completion rates than in-person sessions. Completion rates are quite variable between training sessions. This may indicate greater follow-up needed. Overall completion rates ~55%.

Most instructors (~85%) teach within their first year. About half teach within 100 days. Current group of trainees is on track to meet that target.