Teaching Prospects Data on iTalki

Posted on Jun 28, 2020

Recently the online English teaching industry has garnered additional attention as major players like VIPKid and DaDaABC have earned accolades for their work environments and employee treatment. While these companies offer an excellent opportunity for new and seasoned teachers to earn income teaching remotely from home, their rigid schedules and curricula may not be appropriate for all teachers and can be seen from the data.

iTalki is an online platform that connects language learners with teachers and allows teachers to teach classes in any language on their own rate and schedule. Founded as a "Facebook for language learners", iTalki now boasts more than 5 million users and 10,000 teachers world-wide. 

While iTalki offers flexibility and the ability of teachers to dictate their own prices, because of its marketplace nature iTalki teaching income is less obvious than its counterparts. In this project, we hope to shed some light on what prospective teachers may expect to encounter as they begin a journey teaching on iTalki. All data and statistics represent teachers located in the United States who teach English language on the iTalki website.

Volume

Over the next four weeks English teachers are scheduled to teach an average of 65 30-minute lessons. The mean booking rate of the data is 28.6%, meaning that iTalki students signed up for an average of 28% of time made available by teachers for language lessons over the next four weeks. 

Pay

iTalki teachers select their own pay and curricula and so it was prohibitively difficult to substantively evaluate pay during this project. Lowest hourly rate per teacher started at $18.53 and had a standard deviation of $7.71, but because iTalki does not publish which lessons students take and when, it is impossible to infer actual teacher income from scraped data.

Trends

This data shows no obvious relationship between average rating, career lessons taught, career students taught, and four week bookings. Nearly all teachers boasted a 4.9 or 5 star average rating and so this metric does not appear to be a useful distinguisher of teacher effectiveness. The strongest predictor of four week bookings identified so far is the number of lessons taught in the month of May, suggesting that recent teaching momentum is more important than cumulative teaching experience.

Teacher Descriptions

Teachers most commonly used words in the word cloud below to describe themselves in their introductory profiles. Of note, the frequency of words like "Mexico", "Japan", and "French" suggest that many teachers may themselves be language learners or proficient in multiple languages.

Take Aways

  1. iTalki teachers are scheduled for an average of 65 hours over the next four weeks, suggesting either that teachers only teach an average of 15-20 hours per week, or that most lessons are not booked until within a month of the lesson. It appears that for many, teaching for iTalki may then represent a realistic alternative to sites like VIPKid which typically boast around 15-20 hours per week per employee. 
  2. The relative un-importance of career lessons taught and career students taught vis-a-vis the number of lessons taught in the most recent term suggests that it may be fairly easy for new teachers to enter the iTalki marketplace.

The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

About Author

Nathan

Data scientist in training with a background in education and a passion for solving challenges using data-driven decision-making. Methodological, tenacious self-starter. Building skills with Python, R, SQL, statistical analysis, and machine learning models. Ask me where I am...
View all posts by Nathan >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI