Recent Educational Experiments at ESG
Experimentation with how to teach better and learn better is core to the ESG ethos. Many pilot projects have been designed, executed, and funded in collaboration with other MIT organizations that are experimenting with curriculum development inside and outside ESG. The ESG community continues to undertake new pilot projects, functioning as a source of innovation in the first–year experience at MIT.
Hack Yourself: Data–Driven Wellbeing and Learning
This advising seminar incorporates elements of computation, data science, and positive psychology to teach students how to take charge of their learning and wellbeing. Learning objectives include an understanding of the importance of trustworthy data, the need to question the results from machine learning algorithms, the ability to use statistical techniques to confirm results, and the development of a growth mindset.
In collaboration with Electrical Engineering and Computer Science (EECS) and Writing, Rhetoric, and Professional Communication (WRAP)
Supported by ESG, EECS, MIT Stephen A. Schwarzman College of Computing, Open Learning, and WRAP
Specification Grading Pilot
This pilot explores the use of specific, clearly articulated requirements for what constitutes a passing grade in the physics curriculum. Students are able to revise and resubmit assignments and tests to improve outcomes, which allows them to reflect on their work and assess their problem–solving process. Final grades are based on the total number of satisfactory assignments, ensuring that students who pass the class meet the learning objectives for the physics General Institute Requirements (GIRs).
In collaboration with Teaching + Learning Lab (TLL)
Supported by ESG and Alumni Class Funds
ESG Physics and Computing Pilot
A self–selected subset of students took ES.8012 (Physics I, Classical Mechanics) coupled with 6.100A (Introduction to Python). This pairing of subjects enabled students to enrich their understanding of physics by adding computation to their study of the subject, while learning Python “in context.” Problem sets for both classes were developed to connect Python learning objectives with physics. The Jupyter Notebook was introduced in ES.8022 (Physics II, Classical Electromagnetism) to help with the visualization of physical concepts and to introduce the elements of computational thinking. A portion of the material developed in this pilot is being piloted in mainstream physics.
In collaboration with EECS, Physics, Office of the Vice Chancellor (OVC), and TLL
Supported by ESG, d’Arbeloff Fund for Excellence in Education, EECS, MIT Stephen A. Schwarzman College of Computing, and OVC
Unifying the Teaching of Core Chemistry Concepts Across MIT
ESG developed video recitations for 5.111 and ES.5111 (Principles of Chemical Science) to coordinate how core concepts are taught in the chemistry GIRs. The content for each video addresses a specific problem set and provides additional material beyond what was presented in the classroom. Appearing on the MITx platform, the videos are used by graduate teaching assistants (TAs) in mainstream 5.111 and in ESG as supplemental material for problem-solving sessions in the ES.5111 classroom.
In collaboration with Chemistry
Supported by ESG and Alumni Class Funds
Bringing the Lab into the Chemistry GIRs
Time constraints do not allow the MIT chemistry GIRs to contain a lab component. To introduce students to laboratory experiments, staff from ESG and the chemistry department developed Guided Learning Demonstrations for 5.111 and ES.5111 (Principles of Chemical Science). Each Guided Learning Demonstration pairs a videotaped demonstration of a chemistry experiment with a set of concept questions, which are designed to help students set up and analyze the experiment. These Guided Learning Demonstrations are currently used across the chemistry GIRs.
In collaboration with Chemistry
Supported by ESG, Chemistry, and Alumni Class Funds
5.01x and 5.02x Development
In collaboration with the MIT chemistry department, ESG developed content for online versions of 5.01x (General Chemistry I) and 5.02x (General Chemistry II). ESG also created video recitations and reviewed problem sets, exams, and other content to be included in the online course.
In collaboration with Chemistry
Supported by ESG and Chemistry
ESG Project–Based Learning Pilot
ESG replaced final exams with hands–on final projects, allowing students to apply theoretical learning to real–world projects of their own interest and design. Through this process, students discovered how their GIR learning could be applied to a range of topics. This pilot was so successful that ESG no longer has final exams in nearly all GIR classes.
In collaboration with TLL
Supported by ESG and OVC
Teaching GIR Material in Context
ESG created a series of learning modules that integrate climate and environmental science concepts into GIR course material. These modules were developed for required courses across the core curriculum in chemistry, biology, physics, and mathematics. Learning the material in context enables students to see the connection between what they are learning in class and how it might be applied in the real world. Examples of topics covered include greenhouse gases, the kinetics of ozone depletion, and photosynthesis and energy flow.
In collaboration with Environmental Solutions Initiative (ESI)
Supported by ESG, ESI, and Alumni Class Funds
Leveraging MITx to Enable Success in MIT’s Chemistry GIR
ESG developed a “boot camp” learning module that covered the fundamentals of chemistry to help incoming students prepare for the chemistry GIR at MIT. Prepared in collaboration with the chemistry department, this module evolved into 5.111x (Fundamentals in Chemistry) on MITx.
In collaboration with Chemistry
Supported by ESG, Chemistry, and d’Arbeloff Fund for Excellence in Education
Taking ESG Teaching to Scale in MIT’s Mathematics Department
ESG math teaching combines targeted pre–readings, small classes with highly interactive lectures mixed with problem solving and discussion, projects, and ample mentorship from TAs. Staff from ESG and the mathematics department worked together to scale these methods to a class with many more students by redesigning 18.05 (Introduction to Probability and Statistics) as a flipped, active learning course in a TA–filled Technology Enabled Active Learning (TEAL) classroom. They wrote comprehensive materials for a new, unified introduction to probability, Bayesian inference, and frequentist statistics. In this class, students also develop computational skills and statistical thinking by using R to simulate, analyze, and visualize data and by exploring privacy, fairness, and causality in contemporary media and research. 18.05 is among the most popular math courses on OpenCourseWare and student evaluations, and it now satisfies a requirement for many majors, including Course 6 (Electrical Engineering and Computer Science). Currently, ESG is working to develop and scale 18.05 to multiple sections (100 to 200 students) in collaboration with the mathematics department.
In collaboration with Mathematics
Supported by ESG, Mathematics, and Davis Educational Foundation