Teaching and mentoring philosophy and course objectives
My goal is to maximize student learning by setting high expectations and challenging students to meet or even exceed them. In my experience, students are willing to perform at such a level if they are sufficiently engaged and inspired. Thus, my primary role in the teaching process is to create a class environment – including exercises, examples and experiences – that maximally captures student interest and imagination.
In other words, the way for classes to have a transformational effect on students is to hold myself to a suitably high standard of conduct in order to make the classes as engaging and challenging as they can be.
This approach demonstrably works in eliciting high levels of student commitment. For instance, in my “Scientific Programming and Computing” class, students write an average of 700 lines of code per week to complete the assignments, whereas they complete 2 assignments each week (intensive online assessments, plus educational “games”) in addition to papers and exams in my “Advanced Psychological Statistics” class.
This kind of immersive experience is instrumental to achieve the course objectives, which are clearly defined, and specific to each course. For instance, in my “Scientific Programming and Computing” course, the specific objective is to impart the skills, perspective and confidence that allows students to achieve “computational freedom”, which means the ability to employ computational and coding methods in their own research. In my statistics class, I want students to gain “data literacy” – the ability to make sense of data – as this ability will likely be as essential for a productive life in the 21st century, as being able to read was following the invention of the printing press. In my “Cognitive Neuroscience” class, students gain a deep appreciation of how the brain brings about the mind and the implications that this perspective has on our understanding of the human condition with regards to topics such as emotion or free will.
Support of independent research and honors theses
I was fortunate to meet many talented undergraduate students at NYU and involve them at all levels of my research, including study design, data analysis and manuscript preparation. I currently work with nine undergraduate students in my lab and have supervised 22 undergraduates to date, mostly working on DURF projects, FAST grants or honors theses. These included a large range of topics ranging from individual differences in musical cognition to psychopath detection as well as to personality correlates of movie preferences. These yielded – so far – a total of 16 presentations by undergraduates at major national and international conferences as well as three publications with undergraduate co-authors, most of them as first author. Finally, I consider mentoring to be absolutely essential to student success, and several of my undergraduate students have now gone on to pursue academic careers in PhD programs at Stanford, UT Austin and the University of Pennsylvania, among others.
Statement On The Use Technology In Teaching
While teaching is an inherently social activity, technology can augment and facilitate the transformational effects of effective teaching, which is what I call “teachnology”.
When teaching, I use technology in a wide variety of ways, each to address a specific problem one encounters in teaching. For instance, I use clickers to solve what I term the “feedback” problem – not all students are equally likely to speak out, particularly in large classes. Thus, the sample of students willing to talk in such a setting might not be representative of all students. Using clickers allows me to assess where the entire class is, in real time. It also gives each student immediate feedback how they are doing relative to the rest of the class, throughout the lecture.
However, in this document, I would like to focus on a new approach that I started to implement relatively recently, since Spring 2019. When teaching my “Cognitive Neuroscience” class prior to that date, I encountered a problem that is analogous to one which philosophers call the “Mary Problem”. In the philosopher’s version, a world expert on color vision – Mary – is reared in an entirely monochrome environment until she steps out into a rich world of color one day. Philosophers argue – to this day – whether Mary would learn something new by having these experiences, or whether Mary would be completely unsurprised, as she could have anticipated all these experiences by her book knowledge on colors.
We face a similar problem when teaching highly technical classes like “Cognitive Neuroscience”. The course objective is to understand how the brain implements and brings about human cognition, but neuroscience is an extremely methods driven field. All empirical findings discussed in the class can only be understood in the context of the methods – importantly, their assumptions and limitations – by which they were obtained. Yet, students are often entirely unfamiliar with the relatively abstract concepts that are often involved in these methods, such as electricity and voltages in the case of EEG. In my experience, a verbal description can only go so far when describing methods such as EEG that don’t readily lend themselves to the everyday experience of the students and that are not easy to visualize either.
However, a tenuous grasp on these methods puts the achievement of the course objective in jeopardy, as students will not be able to understand the implications of the empirical findings discussed in the course without a solid grasp on these methods.
That is where my technology augmented solution that addresses this “Mary problem” in teaching comes in. The idea is to Experience a Data recording Method in action, which I call EDM. Specifically, I added a component to the course in which students work in small groups to address a specific research question. We then secured a number of portable EEG headsets and created the software that interfaces with these headsets to record the data that the students need to answer their research question empirically.
The portable EEG headsets we used
I also created the software that the students need to create the stimuli and to analyze their data.
For instance, in this case – see the figure below – the students were instructed to create a series of stimuli, some of which elicit illusory starburst rays and some of which do not.
Stimulus creation software with a stimulus that induces strong illusory starburst rays in most observers
Stimulus creation software with a stimulus that does not induce illusory starburst rays in most observers
They then show these stimuli to participants (each other), while recording the EEG. After that, they analyze their data and come to some kind of theoretical conclusion linking the stimulus parameters, the EEG data and the phenomenal experience when viewing the stimulus (the software I created also allows to record behavior in each trial).
Thus, the students learn how to pursue a research project from research question to theoretical conclusion, and they do so while experiencing personally how an EEG actually works.
Importantly, this approach is inherently experiential, and yields significant increases in terms of increased engagement and understanding. Students filled in a survey on scientific research self-efficacy before and after this intervention, see table below.
As you can see, there are significant gains in terms of a standardized instrument that measures student self-efficacy before and after the implementation of the EDM intervention. The gains were particularly strong in terms of an appreciation of laboratory techniques, tolerance for obstacles faced in the research process, an understanding of how scientists think and self-confidence in scientific abilities.
Importantly, no such gains were observed in a different year when this survey was deployed at the same time in the semester, but without the EDM intervention.
To summarize, we were able to address the Mary Problem in teaching with the EDM approach, and we are happy to report that the students indeed learn something important when approaching the subject matter experientially.