Mentoring
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I have had the opportunity to serve as a W-STEM mentor for a brilliant undergraduate student majoring in Cognitive Science, as well as to mentor six dedicated undergraduate research assistants on projects in the Sensorimotor Neuroscience lab. Research Assistants who work with me gain experience in close reading and communication of scientific articles, experimental methods and design, data collection with human subjects, principles of data management, and introductory experience with running behavioral and perceptual experiments, computational and physical motion capture techniques, electroencephalography, and transcranial magnetic stimulation.
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My Teaching Philosophy
My enthusiasm for teaching draws partly on the inherent reward in guiding students through learning and engaging with new material and experiences, and partly from my extensive training and preparation to do so. I mention pedagogical training explicitly, because this seems to be the training that many colleagues and professors have lamented as most lacking from many graduate programs. However, since most graduate students and college and university professors will find themselves in a teaching role in some capacity, I actively sought out opportunities to strengthen my pedagogy. Pedagogical training for professors is shown to improve teaching skills as well as student learning (Gibbs and Coffey, 2004), and aids professors and graduate student teachers in adopting a student-centered practice, focused on developing students' conceptual and technical skills, as opposed to a teaching-centered practice focused on disseminating information (Postareff, Ylänne, and Nevgi, 2007; Lee, 2019).
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Pedagogical Training
I have formal pedagogical training in Evidence-Based Teaching Practices from the Center for Engaged Teaching and Learning at UC Merced, as well as a certificate in Teaching English to Speakers of Other Languages (TESOL) from Texas A&M University-Corpus Christi. This is the foundational training upon which all of my teaching in cognitive science, neuroscience, logic, and philosophy of cognitive science is built. A central strategy in second language acquisition has been communicative language teaching (Duff, 2014). Communicative teaching practices emphasise the pragmatic and social context of language use -- the goal is not for students to memorize the mechanisms of English grammar or to make perfect translations to English from their native language, but rather to build the skills and strategies that students need to effectively navigate a variety of real world social situations. Similarly, in a science classroom, my goal is not for students to merely memorize e.g. neural pathways associated with specific cognitive processes, but to develop pragmatic skills for discussing the relationship between the brain and cognition, to reason in a scientific context, and to confidently engage in scientific inquiry. I have implemented tools from this comprehensive pedagogical training in the college classroom as a Teaching Assistant and Instructor of Record for upper division undergraduate cognitive science courses. These same skills have allowed me to facilitate strong undergraduate research experience in my role as a mentor for undergraduate research assistants in the Sensorimotor Neuroscience Lab. |
Sample Syllabus
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Teaching Strategies
I adopt communicative teaching strategies in my science classrooms. Students in my classes don't merely memorize brain regions and associated cognitive functions, they don't list off major frameworks in the history of cognitive science. Rather, they learn to communicate in the language of neuroscience, and in the language of behaviorist vs computational frameworks of cognition. Through task-based instruction, students learn to link the scientific concepts I teach in class to their experience with e.g. artificial intelligence or predictive technology outside the classroom. Central to task-based teaching practice is considering what a student needs to do with the content that they learn (Nunan, 2014). I often assign collaborative learning activities such as modified think-pair-share assignments. As an example, I taught my Philosophy of Cognitive Science students about different roles of artificial neural networks: for use in Engineering and Machine Learning (where the goal is just to make something that works), Scientific (cognitive) Modeling (where the goal is to create a network that works the way humans or animals do), Computational Neuroscience (modeling biological data), or traditional Connectionist networks (which model psychological or behavioral data). To ensure students both learned, and could apply, the conceptual differences between these roles in practice, I assigned abstracts of recently published scientific articles. Students would read these abstracts and consider their own answer, discuss with their small group why the neural networks in use in that article were likely serving the role of Engineering, or Computational Neuroscience etc, and then students individually reported their answers via an online quiz app (footnote1). The modified portion of this activity is of course in the `share' aspect -- encouraging students to share via an online poll provides low-stakes, immediate feedback for each student, and avoids unnoticed inequities that might emerge (if e.g. the same few students chose to share in every class meeting (Cooper, Schinske, & Tanner, 2021)).
Inclusive Practices
As Instructor of Record for Philosophy of Cognitive Science this summer, I implemented evidence-based teaching practices taught by UC Merced's Center for Engaged Teaching and Learning. I chose course content knowing that what content is taught, and whose work is taught, communicates a message about who belongs and is valued in a particular field. Philosophy, generally, is known for being a male-dominated field, particularly of white western philosophical traditions. In my teaching, I emphasized the role of Ada Lovelace as the first computer programmer and contextualized the legal persecution of Alan Turing for being gay, despite his foundational work in computer science, AI, and in ending WWII. But beyond these well known historic figures, I referenced the Diversity Reading List to introduce my students to less famously known women philosophers of cognitive science, including Susan Sterrett and her significant response to Turing's Imitation Game (Sterrett, 2003), as well as work from Susan Hurley and Zoë Drayson on action oriented approaches to cognition (Hurley, 2001; Drayson, 2017).
In addition to the active learning strategies mentioned above, I intentionally scaffolded the final essay assignment over a span of multiple weeks. Students wrote a project proposal which had to be approved by me(footnote2), outlined an argument structure and draft which needed to be reviewed by their peers, and turned in a completed project at the end of the course. This allowed for frequent, goal-directed feedback, as well as providing students with observable progress in their learning and work. Due to the COVID19 pandemic, I also implemented a flexible deadline schedule for weekly assignments and uploaded all of my course lectures to proactively accommodate students during difficult situations, if any arose , providing them a chance to prioritize their mental and physical well-being or family responsibilities. And although attendance was not required in my course, a consistent ninety percent of students logged on and were active participants in our zoom course meetings. To the extent allowable in future faculty positions, I aim to implement similar policies to provide students with agency and opportunities for self-efficacy in navigating their educational path. I believe that these measures of flexibility improved student experience and learning by creating an intentionally supportive, equitable, and inclusive environment.
Footnotes:
I adopt communicative teaching strategies in my science classrooms. Students in my classes don't merely memorize brain regions and associated cognitive functions, they don't list off major frameworks in the history of cognitive science. Rather, they learn to communicate in the language of neuroscience, and in the language of behaviorist vs computational frameworks of cognition. Through task-based instruction, students learn to link the scientific concepts I teach in class to their experience with e.g. artificial intelligence or predictive technology outside the classroom. Central to task-based teaching practice is considering what a student needs to do with the content that they learn (Nunan, 2014). I often assign collaborative learning activities such as modified think-pair-share assignments. As an example, I taught my Philosophy of Cognitive Science students about different roles of artificial neural networks: for use in Engineering and Machine Learning (where the goal is just to make something that works), Scientific (cognitive) Modeling (where the goal is to create a network that works the way humans or animals do), Computational Neuroscience (modeling biological data), or traditional Connectionist networks (which model psychological or behavioral data). To ensure students both learned, and could apply, the conceptual differences between these roles in practice, I assigned abstracts of recently published scientific articles. Students would read these abstracts and consider their own answer, discuss with their small group why the neural networks in use in that article were likely serving the role of Engineering, or Computational Neuroscience etc, and then students individually reported their answers via an online quiz app (footnote1). The modified portion of this activity is of course in the `share' aspect -- encouraging students to share via an online poll provides low-stakes, immediate feedback for each student, and avoids unnoticed inequities that might emerge (if e.g. the same few students chose to share in every class meeting (Cooper, Schinske, & Tanner, 2021)).
Inclusive Practices
As Instructor of Record for Philosophy of Cognitive Science this summer, I implemented evidence-based teaching practices taught by UC Merced's Center for Engaged Teaching and Learning. I chose course content knowing that what content is taught, and whose work is taught, communicates a message about who belongs and is valued in a particular field. Philosophy, generally, is known for being a male-dominated field, particularly of white western philosophical traditions. In my teaching, I emphasized the role of Ada Lovelace as the first computer programmer and contextualized the legal persecution of Alan Turing for being gay, despite his foundational work in computer science, AI, and in ending WWII. But beyond these well known historic figures, I referenced the Diversity Reading List to introduce my students to less famously known women philosophers of cognitive science, including Susan Sterrett and her significant response to Turing's Imitation Game (Sterrett, 2003), as well as work from Susan Hurley and Zoë Drayson on action oriented approaches to cognition (Hurley, 2001; Drayson, 2017).
In addition to the active learning strategies mentioned above, I intentionally scaffolded the final essay assignment over a span of multiple weeks. Students wrote a project proposal which had to be approved by me(footnote2), outlined an argument structure and draft which needed to be reviewed by their peers, and turned in a completed project at the end of the course. This allowed for frequent, goal-directed feedback, as well as providing students with observable progress in their learning and work. Due to the COVID19 pandemic, I also implemented a flexible deadline schedule for weekly assignments and uploaded all of my course lectures to proactively accommodate students during difficult situations, if any arose , providing them a chance to prioritize their mental and physical well-being or family responsibilities. And although attendance was not required in my course, a consistent ninety percent of students logged on and were active participants in our zoom course meetings. To the extent allowable in future faculty positions, I aim to implement similar policies to provide students with agency and opportunities for self-efficacy in navigating their educational path. I believe that these measures of flexibility improved student experience and learning by creating an intentionally supportive, equitable, and inclusive environment.
Footnotes:
- Research articles were Examples of Engineering, Cognitive Modeling, and Computational Neuroscience roles for neural networks, respectively:
a) Chiu and Ko, 2017, Develop a personalized intelligent music selection system based on heart rate variability and machine learning;
b) Krumhansl et al, 2000, Cross-cultural music cognition: cognitive methodology applied to North Sami yoiks;
c)Tal, Large, et al 2017, Neural entrainment to the beat: the "missing pulse" phenomenon - Project proposals are also great examples of task-based communicative learning activities, with practical relevance to future work in graduate school, grant applications, or project development in various industries
- Graham Gibbs and Martin Coffey. The impact of training of university teachers on their teaching skills, their approach to teaching
and the approach to learning of their students. Active learning in higher education, 5(1):87–100, 2004. - Liisa Postareff, Sari Lindblom-Ylänne, and Anne Nevgi. The effect of pedagogical training on teaching in higher education. Teaching
and teacher education, 23(5):557–571, 2007. - Star W Lee. The impact of a pedagogy course on the teaching beliefs of inexperienced graduate teaching assistants. CBE—Life
Sciences Education, 18(1):ar5, 2019. - Patricia A Duff. Communicative language teaching. In M. Delce-Murcia, D.M. Brinton, M. A. Snow (Eds.), Teaching English as a second or foreign language, 4:15–30, 2014.
- David Nunan. Task-based teaching and learning. In M. Delce-Murcia, D.M. Brinton, M. A. Snow (Eds.), Teaching English as a second or foreign language, 4:455–470, 2014.
- Katelyn M Cooper, Jeffrey N Schinske, and Kimberly D Tanner. Reconsidering the share of a think–pair–share: Emerging
limitations, alternatives, and opportunities for research. CBE—Life Sciences Education, 20(1):fe1, 2021. - Susan G Sterrett. Turing’s two tests for intelligence. In The Turing test, pages 79–97. Springer, 2003.
- Susan Hurley. Perception and action: Alternative views. Synthese, 129(1):3–40, 2001.
- Zoe Drayson. What is action-oriented perception? 2017.