Improving Student Engagement through Active Learning

a classroom with students standing up, one holding a slip of paper in his hand.

Students engaging in an active learning exercise in a 6.033 recitation session. (Photo by MIT OCW)

By Peter Chipman, Digital Publication Specialist and OCW Educator Assistant

Dr. Katrina LaCurts, a lecturer in MIT’s department of Electrical Engineering and Computer Science, had a problem. Her course 6.033 Computer System Engineering included twice-weekly recitation sessions in addition to the regular lectures. These recitations were meant to allow students to discuss questions raised in the lectures and readings and to work through sample problems in smaller groups. But recitation instructors reported that many students weren’t participating in discussions because they hadn’t done the assigned readings. When the instructors tried to compensate by going over key material from the readings all over again in class, not only did this take up valuable time, it also produced an undesirable secondary effect: when students came to expect that recitations would recapitulate the key points from the readings, they had even less incentive to do the readings themselves, and they came to class even less prepared to participate meaningfully.

So in redesigning the course, Dr. LaCurts decided to emphasize active learning as a key element in the recitations. What is “active learning”? It’s a general term for any and all classroom techniques that have a participatory, non-passive component, ranging from small-group discussion to skits, polls, simulations, and role playing. Dr. LaCurts describes her motivation for making this change:

“There’s some evidence that this style of learning is good for a lot of things. There’s evidence to support the effectiveness of student engagement in exam scores, failure rates, how well students remember content, student attitudes, study habits. And there’s also evidence that active learning has a disproportionate benefit for minorities, students from disadvantaged backgrounds, and female students in male dominated fields.”

An Unsuccessful First Try

But Dr. LaCurts soon found out that implementing active learning in a large, multi-section course is easier said than done. In one of the video excerpts posted on the Instructor Insights page of the OCW course site, she explains that simply telling instructors to implement active learning was ineffective:

“It turns out you can’t tell your recitation instructors to do a thing that they’ve never done before and just have them magically do it. In particular, you can’t tell your instructors to fundamentally change the way they teach and magically have that happen. I would say it’s difficult enough for us to change the way we teach, much less to get other people to change the way they teach.”

She concluded that to implement active learning effectively, she’d have to take a more active approach herself. Here are the steps she recommends for anyone trying to encourage a team of instructors to incorporate active learning in their class sessions:

1. Get Everyone on Board

The very first staff meeting, before the semester had even begun, was about active learning. Dr. LaCurts and her teaching staff, consisting of nine recitation instructors, nine teaching assistants, and thirteen communications instructors, discussed why active learning is better than lecturing, and how it could support the other learning objectives in 6.033 Computer System Engineering. Dr. LaCurts explained that there would be extensive support for the recitation instructors’ efforts, with check-ins throughout the semester to make sure active learning was really working for them and for the students. She appealed to everyone’s scientific nature, explaining that this restructuring of the course was a sort of research project, to find out whether active learning techniques would work in 6.033. She also told them that if the experiment went badly, they wouldn’t keep doing it.

Dr. LaCurts did expect some pushback. She’s in charge of a lot of educators, some of whom have been at MIT for a very long time. But she reports that talking about active learning early on and setting expectations from the beginning was surprisingly helpful. Everybody–not just the recitation instructors but also the teaching assistants and communications instructors–knew that active learning wasn’t an optional element of the course, it was their primary instructional goal for the semester.

2. Plan a Lot

Dr. LaCurts supplied her staff with an annotated version of a well-known list of several hundred active learning activities. In the second staff meeting, she and her staff went through the whole list. They knew that not all of the activities would work in the recitations, but going through the list gave everyone a better sense of what active learning can be.

Dr. LaCurts also identified specific active learning techniques for each recitation. In previous semesters, she had planned recitations strictly for technical content. She would tell instructors the technical issues they needed to hit on, but her instructors had great leeway in how they taught those topics. Now, in addition to the technical content, she began specifying two or three active learning techniques that could be employed in each recitation. For instance, she might point out places where students could break into groups to discuss a particular question, or where it would be useful to hold a debate in the class. For each recitation, the instructors had multiple options for implementing active learning in their sections, and from among these options, they could pick the ones they were the most comfortable with.

3. Support Staff as Individuals

Dr. LaCurts didn’t just plan these activities and set the staff free. She took the time to observe recitation sessions throughout the semester, making sure to stress that she wasn’t there to evaluate the instructors themselves but to see what was working and what wasn’t, so staff could implement those techniques more effectively in future sessions.

In practice, Dr. LaCurts was pleased to discover that in her observations she found far more successful activities than problematic ones. Most of her feedback to the instructors consisted of pointing out things they were doing that were really well, and encouraging them to share those techniques with the other instructors. In the end, she says, “I kind of thought of myself more as a cheerleader for them and what they were doing, than someone who was coming in and really critiquing anything.”

4. Support Staff as a Group

Dr. LaCurts’s staff had many creative ideas as to how to use active learning techniques to present the course’s technical content. So at every staff meeting, instructors would share techniques they had tried and report on how they went. Knowing what worked well in other recitation sections gave more hesitant instructors the confidence to try similar techniques with their own students.

Conversely, fostering a space for discussion at staff meetings meant that everybody was generally comfortable bringing up techniques that they had tried but that weren’t going as well. Dr. LaCurts reports that it was helpful for the staff to have this dedicated space for mutual support and nonjudgmental reflection.

What Kinds of Things Did Students Do?

Small group discussion is a very common type of active learning: students are put in small groups and asked to talk something over and then report back for a class-wide discussion. Dr. LaCurts has found that talking in these small groups beforehand makes the shyer students a lot more confident, and that asking each group to contribute to the eventual discussion means that the discussion isn’t dominated by one or two groups.

In a second technique, debating, students are asked to read two short papers that come to opposing conclusions. The recitation section is split into two teams, with each team assigned to debate in favor of one of the papers’ conclusions. Students usually enjoy this activity, Dr. LaCurts says: “They love to argue, so they’re very excited to do this.” But she admits the activity does require more monitoring on the instructors’ part, to ensure that no one team or person dominates the debate. To combat that, teams are asked to meet beforehand to prepare their arguments for the in-class debate.

A third technique is to ask students to draw pictures on the board, illustrating a particular system or component. The class then comes together to discuss what each drawing is showing, what features the various depictions share, what level of abstraction each drawing captures, and so on. This activity is especially useful because part of the communication curriculum for 6.033 Computer System Engineering involves learning how to design and draw figures. The activity provides a way for students to practice that skill while also forcing them to figure out exactly what the system is doing.

The last technique Dr. LaCurts describes in her video is one where students are asked to physically act out a computer system’s completion of a task. Students are assigned roles as parts of the system, usually with two or three students assigned to each role so shyer students will be more comfortable and no one student is in charge of something. Each part of the system is given instructions, and the system is set into operation. Afterward, the class reconvenes to discuss how the system performed (or failed to perform) its task.

Two women, one wearing a large paper hat, standing in the front of a classrom.

Dr. LaCurts (right) and a volunteer (left, in silly hat) demonstrate acting out how a master machine assigns tasks in MapReduce. (Image by MIT OCW.)

How It Turned Out

Dr. LaCurts reports that restructuring 6.033 Computer System Engineering has resulted in significant improvements in class participation. In surveys, students reported feeling comfortable in the recitations and overwhelmingly felt that these activities improved their engagement. Further, Dr. LaCurts and her staff have seen that students are understanding the details of the systems better, while developing a sense of camaraderie.

It hasn’t been only the students who have benefited from the restructuring of the recitation sessions, however. The staff has benefited as well, as Dr. LaCurts explains:

“It’s a lot of work, but this class is so much fun now. It’s fun for me to run. It’s fun for instructors to teach. I don’t know how many people would tell you that their 400-person class is fun to run. But I have a great time. And the amount of enjoyment that we get out of teaching 6.033 this way really comes through for the students.”

To Learn More

Want to know more about active learning in MIT classrooms? The following courses feature Instructor Insights that you may find of interest:

An electron micrograph of long, slender cells interacting with shorter, thicker, roughly cylindrical cells.8.591J Systems Biology

In this course, Professor Jeff Gore uses color-coded flash cards to quickly survey students’ responses to key concept questions. At the Instructor Insights page, he discusses how and why he uses these cards, and he addresses the perceived barriers to implementing active learning in large classrooms.

The body of a helicopterlike device.16.06 Principles of Automatic Control

The Instructor Insights page for this course features videos on the experience of using active learning, including a candid description of the apprehensions students may feel when asked to try unfamiliar activities in the classroom.

A graph of several curves of varying heights and widths18.05 Introduction to Probability and Statistics

In one of the Instructor Insights for this course, Dr. Jeremy Orloff and Dr. Jonathan Bloom discuss the importance of trust in their active learning classroom and their strategies for promoting it.

Students holding up a QR card5.95J Teaching College-Level Science and Engineering

Dr. Janet Rankin shares an overview of active learning and seven active learning strategies in the Instructor Insights videos for this course, which aims to prepare graduate students to teach in higher education settings.

Judge this book by its cover

Image of book cover for "Picturing Science and Engineering," featuring parts of nine scientific images.

The cover of the book Picturing Science and Engineering by Felice C. Frankel. The book serves as a guide to making scientific photographs for presentations, journal submissions, and covers.

Felice C. Frankel is an award-winning science photographer and research scientist based in MIT’s Department of Chemical Engineering. She’s also a skilled and passionate educator, whose OCW resource Making Science and Engineering Pictures: A Practical Guide to Presenting Your Work has freely shared her methods and insights with the world.

Felice describes making pictures as “an act of discovery” for both the scientist and his or her audiences. “[It] gets you, as the scientist, to look and see things that you would not ordinarily pay attention to.”

With instructional videos, hands-on tutorials, intriguing exercises and many supporting materials, one can learn a lot from this extensive OCW resource. But now there’s more: Felice has converted and extended the OCW course into a full-fledged book, Picturing Science and Engineering, now published by MIT Press.

If you’re new to making scientific imagery, or just curious to learn a little more, sample the topic and start exploring with the OCW resource. And if it grabs you (and it likely will), you’ll love the book.

Physics Is a Contact Sport

Several MIT students peering into a spherical apparatus with various wires attached.

Students perform an experiment in relativistic dynamics in MIT’s Junior Lab.
(Image by OCW)

By Peter Chipman, Digital Publication Specialist and OCW Educator Assistant

If you’re exceptionally gifted, you might be able to learn the established facts of physics by reading books and articles and by attending lectures. But if you want to contribute actively to the field, you need two other forms of expertise: skill in designing and conducting experiments, and a working knowledge of how to communicate your work to other physicists and to the world in general. MIT’s Junior Lab helps students develop firsthand expertise in both these areas.

What Is Junior Lab?

Junior Lab is a sequence of two undergraduate courses, officially designated as 8.13 Experimental Physics I and 8.14 Experimental Physics II, that most physics majors take in the fall and spring of their junior year (hence the name). As Nergis Mavalvala, Associate Head of MIT’s Physics department, explains:

Junior Lab is a keystone course of the MIT physics curriculum. This challenging and memorable course exposes students to diverse techniques in experimental physics, and develops scientific writing and oral presentation skills….Students learn to make measurements using sophisticated apparatus, analyze their data, compare their results to other empirical determinations of the same physical quantities or phenomena, write up their findings as a professional publishable paper, and communicate their results in an oral presentation — all skills with which a practicing physicist must be conversant.

Doing Hands-On Physics

During their year in Junior Lab, students perform a total of ten experiments covering a range of phenomena whose discoveries led to major advances in physics, such as Compton scattering, relativistic dynamics, cosmic-ray muons, radio astrophysics, laser spectroscopy, superconductivity, and quantum information processing. Students work in pairs to set up each experiment, to make measurements, and to analyze and interpret their data. After each experiment, each pair of lab partners participates in a one-hour oral examination and discussion with their instructors. Both students bring their lab notebooks to the oral exam session, and all oral exams are video-recorded so that students can review and refine their presentation technique.

At the end of the fall term, each student delivers a public oral presentation to peers, friends, and faculty in the style of a session at a professional conference. Near the end of the spring term, each pair of lab partners designs and conducts an original, open-ended experiment, after which they summarize their results in a scientific poster presented in an open poster session.

A Wealth of Information

The richness of the Junior Lab experience is reflected in the richness of the materials pertaining to the course on OpenCourseWare. In addition to the syllabus, the course on OCW includes the following:

  • Detailed descriptions of the standard experiments students in Junior Lab perform.
  • A set of guidelines for safety in the lab, including policies to maintain chemical hygiene, environmental safety, electrical safety, radiation safety, cryogenic safety, laser safety, and biological safety.
  • Itemized instructions on how to keep and use a lab notebook to record experimental procedures and results.

For educators and those interested in pedagogical theory, though, the most exciting aspect of Junior Lab on OCW is the wealth of interview videos, in which the course’s professors, other members of the instructional team, and several students share their insights into what’s special about the way Junior Lab is taught. A few highlights:

Junior Lab is based on the notion that the best way to learn physics is experientially, through hands-on learning. Professor Janet Conrad strongly feels that physics is “a contact sport.” In the video clip below, Professor Conrad gives a simple hands-on demonstration of electromagnetic induction that could be used to make physics real even for early elementary students:

(What’s going on in this video? Ordinarily, a dropped object falls half a meter in about a third of a second, but when Professor Conrad drops the magnet into the copper pipe, it takes almost four seconds to fall that far, because the magnet’s motion induces an electric current in the pipe, which in turn generates a magnetic field that brakes the magnet’s fall.)

The structure of the course is also designed to develop skills in collaboration and teamwork in scientific research. Students in Junior Lab don’t just conduct their experiments in teams of two; lab partners also participate in oral exams together, and work together to design their final experiment and to produce and present their poster for the presentation at the end of the spring term. This collaborative approach has clear benefits, but also brings with it some extra challenges, as Professor Gunther Roland explains.

Dr. Sean Robinson, Head of Junior Lab Technical Staff, discusses how the approach to teaching the course has changed in recent years, flipping the classroom to “get the students the information they need at the time when they’re most ready to learn it.” Data analysis, Dr. Robinson says, is best learned as you go along rather than by front-loading information in a lecture hall. Student Henry Shackleton agrees, emphasizing that the independent learning fostered by a flipped-classroom format meshes well with the nature of lab work, in which students are on their own much of the time.

One of the core tenets of Junior Lab is that science communication is a crucial professional competency for anyone wishing to pursue a research career. After all, progress in physics or any other scientific field requires not only that research be conducted and discoveries made, but also that experimental results and discoveries be communicated to other scientists. To help develop students’ communication skills, the instructional team for Junior Lab includes not only scientists but also a communication instructor, Senior Lecturer Atissa Banuazizi. “I think it’s somewhat of a misconception that communication can be separated from the work that scientists do,” Ms. Banuazizi says. “Because so much of what scientists do in their daily lives is communication. If you are a scientist, and you are doing really, really exciting work, that work is not going to have any kind of impact if you can’t tell people about it.”

Whether you’re a student, an independent learner, or an instructor pondering how best to teach the concepts of physics and the skills needed by working scientists, we encourage you to check out the rich collection of Junior Lab course material available to you on OCW.

The OCW course presents all the materials students use to carry out [their assigned] tasks, complemented by instructor, teaching assistant, and student perspectives on how the course is taught. It should serve as a unique guide for students and instructors on how to build and execute experiments, analyze data, and present results in effective written and oral reports.  -Nergis Mavalvala

Doctoral Students Aren’t Lone Wolves: An interview with Brian Charles Williams

By Peter Chipman, Digital Publication Specialist and OCW Educator Assistant

The Curiosity rover standing on the surface of Mars

The Curiosity Mars rover, a complex, collaboratively-built system based on cognitive robotics. (Image credit: NASA/JPL-Caltech/MSSS)

Robotics and artificial intelligence are fast-paced fields in which researchers constantly have to adapt to new technological developments. But in such fields, progress isn’t always achieved by competitive, individual effort; in many circumstances, cooperation and collaboration are more fruitful approaches. In the interview excerpt below, Brian Charles Williams, a professor at MIT’s Computer Science & Artificial Intelligence Laboratory, describes how he develops learning communities in the graduate-level course 16.412 Cognitive Robotics:

OCW: How is learning different in a course focused on an emerging field like cognitive robotics?

Brian Williams: Students are accustomed to reading chapters in textbooks—material that took decades for scientists to understand. But cognitive robotics is an active research area. It’s moving so quickly that every three years or so it reinvents itself. This course is focused on helping students close the gaps in the research. To be at the cutting edge of research, students need to read across papers and understand core ideas that are developed from a collection of publications. And then they need to be able to reduce that understanding to practice.

There’s also no better way to understand something than to teach it, implement it, and put it in a bigger context of some real-world application. That’s why we have a grand challenge at the center of the course experience.

OCW: Tell us more about the grand challenge.

Brian Williams: I like the idea of learning communities, of everybody trying to learn about a topic together. The grand challenge is a communal learning experience driven by a cutting-edge research question in cognitive robotics that allows us to focus on core reasoning algorithms. Students work in teams to present advanced lectures about different aspects of the topic.

OCW: Why teams?

Brian Williams: It’s important for students to work in teams because research is a collaborative endeavor. The notion that doctoral students are lone wolves is just not accurate. The more students can practice effective collaboration, the better.

It’s also the case that developing lectures is hard work. Just producing a first draft of a lecture can take 20 to 30 hours. And then you need to spend another 6 hours improving it. So, to develop a high-quality lecture, you really need two people working together.

Robot standing in a room.

Domo Robot, developed by Aron Edsinger and Jeff Weber, is able to adapt to novel situations. It is on display in the MIT Computer Science and Artificial Intelligence Lab. (Image by MIT OpenCourseWare.)

OCW: How do you assess student work completed collaboratively?

Brian Williams: That is an interesting problem, because when the whole class does a project collaboratively teams can become too large. When that happens, people begin to feel disenfranchised. What I do to combat that is to make clear from the beginning what elements or materials individuals are responsible for contributing to the project. I have students write down what they are contributing so that I can assess their work accurately.

Another piece of the assessment puzzle is providing good feedback. The place where feedback matters the most is during the dry run for the students’ advanced lectures. A week before the students give their lecture to the class, they do a dry run for the teaching team and receive feedback. The process takes about two and a half hours. We teach them how to capture students’ interest at the beginning of the lecture and how to clarify the main points they want students to learn. We also help them convey the synergies between the main points and encourage them to consider the role of examples in their presentations.

OCW: Are there other components of the grand challenge, in addition to the advanced lectures?

Brian Williams: Yes. As I mentioned, the field of cognitive robotics is moving really fast. What normally happens is that members of the research community will generate tutorials on emerging themes. These tutorials encapsulate core ideas that everybody should know. The problem is that there’s just so much we need to know—but not enough time to write all the tutorials. So some of the students in the class are assigned to write tutorials related to the topic of the grand challenge. And a few others will write corresponding Jupiter or Python notebook problem sets. Along with the lectures, students end up producing materials that are enormously helpful to researchers in the field. This is important because I want them to learn that as scientists, their role is to consolidate ideas and to teach the community.

Man sitting at desk. Bookshelves with books to his left.

Aeronautics and Astronautics Professor Brian Williams in his office on the MIT campus. (Image by MIT OpenCourseWare.)

OCW: It’s interesting that you have the goal of figuring out cognitive robotics as a field, but also how to teach it to others.

Brian Williams: And how to catalyze community. An engaged, collaborative community is absolutely key.

***

You can read more of Professor Williams’s thoughts about teaching 16.412 on the Instructor Insights page of this course.

Keep learning! The following courses and Instructor Insights may be of interest to you:

Another OCW Course Offered by Professor Williams

Image combining data taken by an autonomous vehicle with the views from its windows.16.410 Principles of Autonomy and Decision Making

This course surveys a variety of reasoning, optimization, and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research.

Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.

More about Robotics and Artificial Intelligence

Wheeled robot carrying doll in its arm.2.12 Introduction to Robotics

This course provides an overview of robot mechanisms, dynamics, and intelligent controls. Topics include planar and spatial kinematics, and motion planning; mechanism design for manipulators and mobile robots, multi-rigid-body dynamics, 3D graphic simulation; control design, actuators, and sensors; wireless networking, task modeling, human-machine interface, and embedded software. Weekly laboratories provide experience with servo drives, real-time control, and embedded software. Students design and fabricate working robotic systems in a group-based term project.

Graphic of three figures in an evolutionary arc, starting with a figure standing upright on the left, ending with a person hunched over at a computer on the right.6.034 Artificial Intelligence

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

More on Learning Communities

Illustration of a brain with colors indicating regions involved in social processes, plus three example faces used in social testing and a photo of a large group sitting in a circle.9.70 Social Psychology

In the rather idiosyncratic syllabus for this course, which goes into much more philosophical depth than such documents usually attain, Professor Stephan L. Chorover lays out the principles of the collaborative learning system that formed the basis of his approach to teaching.

A woman on the monitor of a video camera, facing the viewer20.219 Becoming the Next Bill Nye: Writing and Hosting the Educational Show

Elizabeth Choe and Jaime Goldstein discuss the importance of cultivating a sense of community in the classroom, and explain how situating themselves as facilitators-of-learning, rather than omniscient givers-of-knowledge, communicated to students their respect for them as learners.

The faces of the four members of the Beatles in a 4 by 4 grid.21M.299 The Beatles

The Beatles lived an insulated life in the 1960s. They couldn’t go out without being mobbed. As a result, the four of them spent much of their time together, listening to and playing music. In that process, they were constantly learning from each other. Lecturer Teresa Neff discusses the centrality of group learning in her Instructor Insights for this course.

Find insights like these on many other teaching approaches at our Educator Portal.

College search support from your friends at OCW

Photo of three students sitting on a bench, in conversation.

Photo by Jake Belcher.

To all students who are now deep into the autumn ritual of college applications, along with all the other demands of your year: we feel you!

While we can’t join you on college tours or write those application essays, OCW can hopefully support you in a few other ways during this exciting hectic time, as OCW is always free and open for you anytime and anywhere you need it.

Screenshot of the OCW course homepage for 6.0001 Introduction to Computer Science and Programming in Python.Many incoming students use OCW to preview what college studies are like. For instance, 6.0001 Introduction to Computer Science and Programming in Python is one of the most popular courses at MIT and also on OCW. Freely browse through the teaching materials used in every MIT department and major, and go well beyond the short descriptions in most course catalogs: check out OCW lecture notes, readings, assignments and more from introductory core classes as well as advanced electives.

Screenshot of OCW Find by Topic browser.

Our Find Courses by Topic and Find Courses by Department pages make it easy to explore OCW’s collection of over 2500 courses and supplemental resources from 36 MIT departments and programs.

 

Photo of a group of students celebrating in a lab, with one student being held up in the air by the others.The OCW Highlights for High School has collected resources of particular interest to high school students, and their teachers and parents. Check out our exam prep material, lists of introductory OCW courses to guide and inspire your college search, and cool stuff like the ChemLab Boot Camp reality video series.

Screenshot of First Year STEM courses webpage, highlighting Biology courses.If you’re looking to get ahead in your studies of STEM subjects like Math, Physics, and Computer Science check out the First Year STEM Classes from MIT collection, which includes both MITx on edX and OCW courses. Learn from the same material used by first year MIT students to advance your knowledge, and help you prepare for incoming student placement tests.

Screenshot of webpage "Best of the Blogs."Finally, while it’s not actually part of OCW, we’re big fans of the MIT Admissions student blogs for their direct, honest, diverse and personal account of college life. Whether or not you’re applying to MIT, they’re well worth a read.

 

We wish you all the best in your quest for a great college match!

How Would You Like Your Grade? An Interview with George Verghese

By Peter Chipman, Digital Publication Specialist and OCW Educator Assistant

black lines, indicating elements of heart beats, on red and white graph paper.

Electrocardiogram data, an example of measured signals. (Image courtesy of kenfagerdotcom on flickr. License: CC BY-NC-SA.)

The syllabus for a typical MIT course spells out a familiar grading scheme that assigns fixed percentage weights to the different elements of the course: so many points for attendance and participation, so many for the quizzes or written assignments, and so many for the final exam or final project. Such a system is straightforward to implement and easy for students to understand, but there are times when both students and instructors want a little more flexibility. After all, not all students are the same, and they don’t all have the same needs.

In Spring 2018, Professors George Verghese, Alan V. Oppenheim, and Peter Hagelstein co-taught 6.011 Signals, Systems and Inference, an undergraduate course that covers a broad range of topics pertaining to communication, control, and signal processing. The material is complex, and the instructors support student learning in unique ways. We approached Professor Verghese for his insights on the course’s unusual grading system, and also on how the teaching team uses tutorials and an informal collaborative learning space called the Common Room to help MIT students succeed.

OCW: You offer three grading schemes in the course: regular, lower-friction, and project. This is so interesting! Tell us about your decision to offer students this kind of choice.

George Verghese: Ideally we’d like all students to attend all lectures and recitations, and for most students this is essential to their learning the material well and succeeding in the class. However, student lives can be complicated, their backgrounds and motivations are varied, and they optimize their trajectories through MIT in different ways. I know there is always a handful of students who can master the material with much less interaction, and I am fine with allowing them to do that, without getting in their way—hence the “lower-friction option,” in which the only components of the grade are their scores on the homework, the two quizzes during the term, and on the final exam. Students who opt for this have access to all the material in the class, but not to the tutorial sessions, as we don’t want them using the teaching assistants to help them make up for lecture and recitation material they may have missed. Unfortunately, there are always a couple of students who opt for the lower-friction version who really should not have, and their grade ends up suffering for it. But there are others—and these are the ones for whom this option is intended—who end up near, or even at, the top of the class. More power to them! My only regret is that we lose the benefit of whatever they may have contributed to class discussions if they had attended lectures and recitations.

Those students who do not elect the lower-friction option are, in effect, signing on to attending most lectures and recitations, and 15% of their course grade is allotted to attendance. I don’t actually take attendance directly, but every few lectures I will have them pair up in class to work out some problem related to the lecture material, then turn in their answer sheets at the end of lecture (with their neighbor’s name on their sheet, so they know I’m not looking to grade them on their answers!). Any student who misses a couple of these gets a note from me to urge better attendance. And recitation instructors have a good sense of who is attending and who isn’t, even if they don’t take attendance formally.

Lecture 22 image

An example of binary hypothesis testing from Lecture 22 (PDF).

There are also a few students each semester who feel they’d do better if they had a project to anchor their learning, and also to spread the course grade over (10% of the course grade is assigned to the project for students who choose this option, and the contributions of quizzes and the final exam are correspondingly reduced). Since there are typically only a few such students, I work quite closely with them over the semester, meeting at least every couple of weeks, to ensure the projects are related to course material and are moving along well. Some of these projects turn out to be good demonstrations for lectures in succeeding terms.

OCW: Please describe the tutorials offered to students and tell us about their role in the course.

George Verghese: Our tutorials are run by the teaching assistants on an optional, sign-up basis, limited to 5 students per session. Some students—perhaps a third of the class—attend them very regularly each week, others occasionally or not at all. The idea here is to actively engage the students, have them go the board to work things out, rather than having the teaching assistant give a summary of lecture at the board and then work out problems for the students. The teaching assistants go prepared with a small set of basic problems, simpler than those on homework, and illustrating points that have come up in lecture. However, the tutorials are also teaching assistant office hours, and students are encouraged to come with questions they may have. Any general guidance that the lecturer or the recitation instructors may have for the teaching assistants usually comes at our weekly staff meeting, held on Monday to set plans and directions for the week and beyond, but we typically leave the teaching assistants to come up with specific problems for their tutorials, perhaps in coordination with each other. The teaching assistants also take turns helping the lecturer generate the problem sets and solutions.

OCW: Please tell us about the role of the Common Room in the course. What was the impact of having a space where students could informally ask questions and work alongside each other on course assignments?

George Verghese: I think the evening Common Room is one of the best elements of the class, for those students—around a third to half of the class—who take advantage of it. I got the idea for it many years ago when visiting another university campus center after dinner, and found clusters of students sitting at desks and working collaboratively on homework and projects, though with no instructors in sight. For 6.011 Signals, Systems and Inference, we reserve a classroom for the three or four evenings that precede the day homework is due, and guarantee that at least one of the staff will be present there for 1.5-2 hours; usually we have the lecturer or a recitation instructor, as well as a teaching assistant.

“Our staff invariably finds the Common Room to be the most rewarding of the various settings in which they interact with students.”—GEORGE VERGHESE

We find students working individually as well as collaboratively, and periodically interacting with the staff, either at the board or at their desk—very immersed and engaged in the homework problems, and in sorting out ideas and misconceptions related to these. The staff will typically respond to student questions with other (well chosen!) questions or hints that guide them along, rather than with answers—and that makes for a very fruitful dynamic. We have never found the Common Room misused as a place to come and get fellow students to feed one solutions to the homework. I would absolutely recommend this to other faculty, if they have the staff resources and time. Our staff invariably finds the Common Room to be the most rewarding of the various settings in which they interact with students, and it is where they get to know their students best.

***

You can read more of Professor Verghese’s thoughts about teaching 6.011 on the Instructor Insights page of this course.

Keep learning! The following courses and Instructor Insights may be of interest to you:

More OCW Courses Offered by Professors Verghese and Oppenheim

Artist's depiction of the Cassini spacecraft, with Saturn in the foreground and a dark blue, starry background.Introduction to EECS II: Digital Communication Systems

An introduction to several fundamental ideas in electrical engineering and computer science, using digital communication systems as the vehicle. The three parts of the course—bits, signals, and packets—cover three corresponding layers of abstraction that form the basis of communication systems like the Internet.

The course teaches ideas that are useful in other parts of EECS: abstraction, probabilistic analysis, superposition, time and frequency-domain representations, system design principles and trade-offs, and centralized and distributed algorithms. The course emphasizes connections between theoretical concepts and practice using programming tasks and some experiments with real-world communication channels.

An audio compact disc.Discrete-Time Signal Processing

This class addresses the representation, analysis, and design of discrete time signals and systems. The major concepts covered include: Discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flowgraph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; multirate techniques; Hilbert transforms; Cepstral analysis and various applications.

More about Communication, Control, and Signal Processing

6-003f11-th.jpgSignals and Systems

This course, a prerequisite for course 6.011, covers the fundamentals of signal and system analysis, focusing on representations of discrete-time and continuous-time signals (singularity functions, complex exponentials and geometrics, Fourier representations, Laplace and Z transforms, sampling) and representations of linear, time-invariant systems (difference and differential equations, block diagrams, system functions, poles and zeros, convolution, impulse and step responses, frequency responses). Applications are drawn broadly from engineering and physics, including feedback and control, communications, and signal processing.

2-14s14-thAnalysis and Design of Feedback Control Systems

This course develops the fundamentals of feedback control using linear transfer function system models. Topics covered include analysis in time and frequency domains; design in the s-plane (root locus) and in the frequency domain (loop shaping); describing functions for stability of certain non-linear systems; extension to state variable systems and multivariable control with observers; discrete and digital hybrid systems and use of z-plane design. Students will complete an extended design case study.

2-161f08-thSignal Processing: Continuous and Discrete

This course provides a solid theoretical foundation for the analysis and processing of experimental data, and real-time experimental control methods. Topics covered include spectral analysis, filter design, system identification, and simulation in continuous and discrete-time domains. The emphasis is on practical problems with laboratory exercises.

More on Assessment and Grading

6-01scs11-thIntroduction to Electrical Engineering and Computer Science I

Professor Dennis Freeman reflects on the advantages and limitations of using oral exams to assess student learning, and considers how online exams might help instructors offer scalable assessments that are personalized and productive.

6-034f10-thArtificial Intelligence

Teaching assistants Jessica Noss and Dylan Holmes describe the unusual grading system for this course, in which the final exam is optional, with each of its sections serving as a make-up exam for one of the course’s regular quizzes.

18-821s13-thProject Laboratory in Mathematics

Project Laboratory in Mathematics is designed to give students a sense of what it’s like to do mathematical research. In the Grading section of this course, Professor Haynes Miller and Susan Ruff describe their approach to grading and their experiences in developing (and revising!) grading rubrics.

Find insights like these on many other teaching approaches at our Educator Portal.

OCW’s Greatest Hits: Architecture and Urban Studies and Planning

It’s time for a new post in our Greatest Hits series, highlighting individual MIT departments through a handpicked selection from their most-visited OCW courses. This month we feature the departments of Architecture and Urban Studies and Planning.

Photo of interlocking wooden forms.

This model from a student’s final project in 4.111 Introduction to Architecture & Environmental Design demonstrates the relationship between object and void. (Courtesy of Johanna Greenspan-Johnston. Used with permission.)

Architecture

  • 4.111 Introduction to Architecture & Environmental Design, taught by Lorena Bello Gomez
    This course provides a foundation to the design of the environment from the scale of the object, to the building to the larger territory. The design disciplines of architecture as well as urbanism and landscape are examined in context of the larger influence of the arts and sciences.
  • 4.125 Architecture Studio: Building in Landscapes, taught by Professor Jan Wampler
    This undergraduate design studio “introduces skills needed to build within a landscape establishing continuities between the built and natural world. Students learn to build appropriately through analysis of landscape and climate for a chosen site, and to conceptualize design decisions through drawings and models.”
  • 4.241J Theory of City Form, taught by Professor Julian Beinart
    This course covers theories about the form that settlements should take and attempts a distinction between descriptive and normative theory by examining examples of various theories of city form over time. Case studies will highlight the origins of the modern city and theories about its emerging form, including the transformation of the nineteenth-century city and its organization.
  • 4.341 Introduction to Photography and Related Media, taught by Andrea Frank et al
    This course provides practical instruction in the fundamentals of analog and digital SLR and medium/large format camera operation, film exposure and development, black and white darkroom techniques, digital imaging, and studio lighting.”
  • 4.401 Introduction to Building Technology, taught by Professor Marilyne Andersen
    This course provides a fundamental understanding of the physics related to buildings and an overview of the various issues that have to be adequately combined to offer the occupants a physical, functional and psychological well-being. Students are guided through the different components, constraints and systems of a work of architecture. These are examined both independently and in the manner in which they interact and affect one another.

Photo of feet along a brick-paved path.

The Post Office Square in Boston served as the site of a student’s project in 11.309J Sensing Place: Photography as Inquiry. (Image courtesy of Francisca Rojas. Used with permission.)

Urban Studies and Planning

  • 11.001J Introduction to Urban Design and Development, taught by Professor Susan Silberberg
    This course examines the evolving structure of cities and the way that cities, suburbs, and metropolitan areas can be designed and developed. Boston and other American cities are studied to see how physical, social, political and economic forces interact to shape and reshape cities over time.
  • 11.011 The Art and Science of Negotiation, taught by David Laws
    This course provides an introduction to bargaining and negotiation in public, business, and legal settings. It combines a “hands-on” skill-building orientation with a look at pertinent social theory. Strategy, communications, ethics, and institutional influences are examined as they influence the ability of actors to analyze problems, negotiate agreements, and resolve disputes in social, organizational, and political circumstances characterized by interdependent interests.
  • 11.126J Economics of Education, taught by Professor Frank Levy
    This class discusses the economic aspects of current issues in education, using both economic theory and econometric and institutional readings. Topics include discussion of basic human capital theory, the growing impact of education on earnings and earnings inequality, statistical issues in determining the true rate of return to education, the labor market for teachers, implications of the impact of computers on the demand for worker skills, the effectiveness of mid-career training for adult workers, the roles of school choice, charter schools, state standards and educational technology in improving K-12 education, and the issue of college financial aid.
  • 11.309J Sensing Place: Photography as Inquiry, taught by Professor Anne Whiston Spirn
    This course explores photography as a disciplined way of seeing or investigating urban landscapes, and expressing ideas. Readings, observations, and photographs form the basis of discussions on light, detail, place, poetics, narrative, and how photography can inform design and planning.
  • 11.431J Real Estate Finance and Investment, taught by Professors David Geltner and Tod McGrath
    This course is an introduction to the most fundamental concepts, principles, analytical methods and tools useful for making investment and finance decisions regarding commercial real estate assets. As the first of a two-course sequence, this course will focus on the basic building blocks and the “micro” level, which pertains to individual properties and deals.