United States

Machine Learning (ML) is becoming ubiquitous in the realms of science, business, law enforcement, and even the arts. I have developed a three-week general education course on machine learning for non-scientists. Each day in the first half of the course, students engage with demonstrations and tutorials. They learn how to implement simple examples of text and image classification as well as generative algorithms and adversarial networks. In the second half of the course, the students carry out a project of their own design. I do not assume students have previous experience with programming, calculus, or ML algorithms. They learn by experimenting with existing code to achieve goals of increasing complexity. I will report on the successes and challenges of this course and share what I have learned about how students can effectively grapple with the opportunities and dangers that ML represents.

AAPT SM 22

Teaching Machine Learning to Non-Scientists

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

<iframe style="width:700px;height:400px" src="https://online.fliphtml5.com/ebtyf/ceby/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe>

Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

technical paper

AAPT meetings are a great way to reflect on your teaching, recommit to best practices and re-energize your career with new research, products, and techniques. Listen to internationally known plenary speakers, attend sessions, workshops, and special activities. This meeting is an excellent opportunity to share your expertise and learn from and network with leaders in the field.



In an earlier study we showed that small amounts of extra credit offered for early progress on online homework assignments can reduce cramming behavior in introductory physics students. This work expands on the prior study by implementing a planning prompt intervention. In the prompt we asked students to what degree they intended to earn extra credit offered for early work on the module sequence, and what their plan was to realize their intentions. The survey was due several days before the first extra credit deadline. We found that students who completed the prompt earned on average 0.6 more extra credit points and completed the modules an average of 1.1 days earlier compared to a previous semester. We detect the impact of the survey by creating a multilinear model based on data from students exposed to the intervention as well as students in a previous semester.

Planning Prompt Surveys to Encourage Early Completion of Homework Assignments

Heileman et al. developed a methodology for quantifying the complexity of program curricula. Using that framework, they showed that Engineering programs that were deemed higher quality had curricula and degree plans that were simpler than engineering programs that were ranked to be lower quality. They also propose that curricula that is simpler is more agreeable to students, claiming that a simpler curriculum would be beneficial to student retention. The purpose of this presentation is to determine whether or not this trend holds for physics programs in the U.S. Physics curricula from different universities were analyzed using the methods developed by Heileman et al. to see if there is a correlation between program complexity and program quality.

Curricular Analytics in Physics

Research-validated clicker questions comprise an easy-to-implement instructional tool that serves to scaffold and formatively assess student learning. We present findings from the implementation, in consecutive years, of a research-validated Clicker Question Sequence (CQS) on student understanding of the uncertainty principle as it applies to two-state quantum systems. This study was conducted in an advanced undergraduate quantum mechanics course, in both the virtual learning environment of 2020 and the in-person classroom setting of 2021. Students were assessed after receiving traditional lecture-based instruction, and their performance was compared with that on a similar assessment given after engaging with the CQS. We examine and remark on the constant and differing trends observed in the two modes of instruction.

Clicker Question Sequence on Uncertainty Principle: Virtual and In-Person Implementation

The field of quantum computing is presently gaining tremendous attention from governments, researchers, engineers, academics and investors. There is an ongoing push for the development of quantum computers, advances in information technology, and the development of a quantum workforce. This needs to be accompanied by the development of quantum computing courses and curricula and the development and adoption of evidence-based materials and pedagogies to support the education of the next generation of quantum information scientists At Texas Tech University (TTU), we have introduced a course in Quantum Computing and have begun to research student understanding of topics in this field. Our goal is to develop evidence-based materials for the course. We did a series of interviews to identify students’ strengths and difficulties in these topics. We report on the results of these interivews and our initial work on the development of supplementary materials for the course.

Investigating Students’ Strengths and Difficulties in Quantum Computing

This talk will focus on ways to assist high school physics teachers in incorporating 21st century physics side by side with classical topics. The talk will compare the QuarkNet approach with the seven characteristics of effective professional development developed by Linda Darling-Hammond et.al.

21st Century Physics Integrated into the High School Physics Curriculum

Models like string theory inspire exciting questions about physics in higher dimensions. Fundamental concepts like energy and momentum generalize to extra dimensions easily, but angular momentum as traditionally taught depends on the cross product: an intrinsically three-dimensional concept. Instead, even introductory rotational physics can be taught just as easily in terms of "bivectors," which can be visualized as "tiles" with area and orientation whose components form an antisymmetric matrix. Bivectors have historically been discussed mostly in specialized contexts like spacetime classification or geometric algebra, but in most ways they are just as easy to understand as vectors, and they generalize naturally to extra dimensions. Imagine never again watching a student struggle to master yet another right hand rule, never again worrying about the subtle trap of a left-handed coordinate system. Teaching rotational physics in this language opens the door to exploration of higher dimensions, and it's more fundamental as well.

Lessons from Extra Dimensions: Rotations Were Bivectors All Along

The von Neumann theory of quantum measurement involves uniquely entangling the eigenstates of a self-adjoint operator with the pointer states of the experimental apparatus. Then, either wavefunction collapse, or decoherence (due to the macroscopic pointer states) fixes the measurement of the apparatus. Unfortunately, most, if not all, quantum experiments don't operate that way. Instead they are counting experiments, where properties are often inferred from the geometrical set-up of the experiment. In this talk, I will focus on how momentum is actually measured, which usually follows a time-of-flight strategy, that infers the momentum by measuring the position. I will provide examples for how this works and explain why we need to teach a proper measurement theory within undergraduate quantum mechanics classes. Only in this fashion, can we properly prepare our students for future careers in the quantum-enabled workforce, particularly in the area of quantum sensing.

Teaching Measurement to Prepare for Quantum Sensing

Physical science standards on electromagnetic and gravitational interactions are sometimes relegated to a chapter in a textbook and may feel to middle and high school students like topics that are old and stale, in other words, settled science. Approaching these standards from a different direction - using current searches for dark matter as a storyline - can spark students' curiosity and engage them in seeing science as a process that is always evolving. Now that we are face-to-face again, we will take the opportunity to explore hands-on activities developed or adapted by the Education and Outreach Department at the Sanford Underground Research Facility around the topics of evidence for dark matter and current dark matter searches.

Dark Matter Activities as a Phenomenon for MS/HS Standards

Hypersonic flight is a domain that is generally considered to include anything traveling five times or more the speed of sound. At these speeds, unique challenges arise that influence vehicle design. Hypersonic flight is also of national defense interest for most international actors. However, there is little in the way of curriculum to support hypersonic instruction at the K-12 level. I will report on the initial curriculum development results for teaching concepts about hypersonic flight. This curriculum development is being completed through a partnership of the Indiana Univeristy-Bloomington School of Education and the education outreach office at Crane Naval Surface Warfare Center.

The Development of a Hypersonic Curriculum: Initial Results

Inequitable outcomes in physics courses point to systemic inequities in higher education for students with historically disadvantaged backgrounds and investigations focusing on these can be useful to provide support and create an equitable and inclusive learning environment. Specifically, student grades and motivational outcomes in introductory physics courses can influence their retention in science, technology, engineering, and math (STEM) disciplines and future career aspirations. This study uses data at a large public research institution to investigate the grades earned by students categorized by four demographic characteristics: gender, race/ethnicity, low income status, and first-generation college student status. We find that on average across all years of study, underrepresented minority (URM) students experience a larger penalty to their STEM GPA than even the most disadvantaged non-URM students. In order to further understand inequitable outcomes in introductory physics courses, we discuss an investigation of students' motivational beliefs that show a gender gap.

Premium content

Teaching Machine Learning to Non-Scientists

Downloads

Next from AAPT SM 22

Planning Prompt Surveys to Encourage Early Completion of Homework Assignments

Similar lecture

Editing Factual Knowledge in Language Models