Integrated Statistics Learning Environment

The ISLE project combines educational platform development with behavioral data science research. It is part of the Data Science Initiative research group at Carnegie Mellon Statistics & Data Science.

What is ISLE?

A browser-based interactive statistics & data analysis platform.

Easy-to-use Data Explorer

Students can perform the entire data analysis workflow without any coding and will only see the methods covered in class


Comes with tools for use in class and out of class


Instructor can pose surveys during class/lab

Action Logs

Storage of reproducible analysis steps for students/instructors

Sketchpad Functionality

Instructors and students can take notes on lecture slides and upload them with a single mouse click

Response Visualizers

Instructors can summarize/visualize student responses for mediation, discussion, etc. in real-time

Real-Time Communication

Student-driven case study data analyses with peer review and real-time communication functionality

Data Analysis Reports

Students can create data analysis reports and presentations (oral/posters) directly in ISLE


ISLE contains pre-built widgets on topics such as hypothesis testing, confidence intervals, CLT, statistical distributions to text mining and image analysis.

Data Explorers

Allows students to explore data sets via visualizations and summary statistics and comes with functionality to run hypothesis tests and fit basic statistical models. An integrated Markdown editor allows students to practice writing data analysis reports.


A drawing sketchpad for note taking on lecture slides or an empty canvas. Drawings can be automatically synced between the instructor and the students or among groups. All other ISLE components can be overlaid onto the sketchpad for interactive lectures.

Real-Time Communication

Facilitation of communication between students through chats, peer review, and group work tools.

ISLE Dashboard

An online dashboard for students and instructors. Instructors can deploy, organize and monitor ISLE lessons, whereas students can keep track of their progress as they advance through a course.

ISLE Editor

A desktop-application that can be used to author ISLE lessons and deploy them online. Comes with a point-and-click interface to reuse existing components but also allows full customizability via HTML, CSS, and JS.

Frequently Asked Questions

Should you have questions, please get in touch. Answers to common questions are collected in our FAQ.


Everyone is a Data Scientist

Rebecca Nugent on ISLE and democratizing data science at the Carnegie Mellon Future Summit:

Upcoming Events

Please feel free to contact us to learn more about the ISLE system or to join forces on behavioral data science research. You can also run into us at the below places:

Upcoming Workshops

Past Events

Recordings of past ISLE presentations

Selected Presentations / Publications

  • P Burckhardt, C Genovese, R Nugent, R Yurko. Incorporating Real-Time Clustering of Student Responses into an E-Learning System. Joint Statistical Meetings, July 2019. View poster.
  • R Nugent, R Yurko, P Burckhardt & F Kovacs. "Many Students, One Dataset": Using ISLE to Teach Reproducibility and the Impact of Data Analysis Decisions on Conclusions. Breakout session. US Conference on Teaching Statistics (USCOTS), May 2019. View abstract.
  • Burckhardt P, Nugent R, Genovese C. How students make sense of data on an e-learning platform. Joint Statistical Meetings, Vancouver, July-August 2018. 2018 SPEED Session Award.
  • Burckhardt P, Chouldechova A, Nugent R. TeachIT: Turning the Classroom into a Research Laboratory via Interactive E-Learning Tools. Invited paper. Proceedings of the Tenth International Conference on Teaching Statistics (ICOTS10, July, 2018), Kyoto, Japan. View paper.
  • Yurko R, Nugent R, P Burckhardt. Detecting Data Analysis Patterns in Text and Graphs to Characterize Student Learning in an Introductory Statistics & Data Science Course. Classification Society Annual Meeting, June 2018.
  • Yurko R, Nugent R. Using text analysis to characterize student learning in an introductory statistics & data science course. Electronic Conference On Teaching Statistics (eCOTS), May 2018. View video poster.
  • Burckhardt P, Nugent R, Genovese C. Learning Data Science with the Help of a Data Exploration Tool. Electronic Conference On Teaching Statistics (eCOTS), May 2018. View video poster.

Building the Data Science Laboratory - A Web-Based Framework for Modern Statistics

PhD Thesis by Philipp Burckhardt

This thesis analyzes the impact of the digital revolution on pedagogy and distills opportunities of a blended-learning approach. It then discusses the principles of an interactive e-learning environment geared towards a contemporary statistics curriculum focused on the understanding of concepts and data analysis rather than mere number crunching.

The Integrated Statistics Learning Environment is a practical open-source implementation of the distilled principles. ISLE is a framework for e-learning instruction both inside and outside of the classroom.

Open PDF


Faculty, students, and staff are welcome to join and work on projects when available; the below includes current, regular members doing research with ISLE and/or using it in their classes.

  • Rebecca Nugent, Associate Dept Head and Director of Undergraduate Studies, Statistics & Data Science
  • Christopher Genovese, Dept Head, Statistics & Data Science
  • Philipp Burckhardt, Postdoctoral Researcher, Statistics & Data Science
  • Ciaran Evans, PhD student, Statistics & Data Science
  • Ron Yurko, PhD student, Statistics & Data Science
  • Francis R. Kovacs, Masters in Statistical Practice Student, Statistics & Data Science
  • Gordon Weinberg, Instructor, Statistics & Data Science
  • David West Brown, Associate Teaching Professor of English, Associate Director of First-Year Writing for Research and Assessment

The following faculty at other institutions have used ISLE in their classes or are working together with us on projects involving it:

  • Jerzy A. Wieczorek, Assistant Professor of Mathematics and Statistics, Colby College
  • Marian Frazier, Assistant Professor - Statistical & Data Sciences, The College of Wooster