arXiv subjects

John M. Aiken

Publications and source records attributed to John M. Aiken.

17 recordsLinked to original sources

arXiv2023

Spatio-temporal spread of COVID-19 and its associations with socioeconomic, demographic and environmental factors in England: A Bayesian hierarchical spatio-temporal model

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aims to investigate the spatio-temporal spread of COVID-19 infection rate in England, and examine its associations with socioeconomic, demographic and environmental risk factors. Using weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England, we developed a Bayesian hierarchical spatio-temporal model to predict the COVID-19 infection rates and investigate the influencing factors. The analysis showed that our model outperformed the ordinary least squares (OLS) and geographically weighted regression (GWR) models in terms of prediction accuracy. The results showed that the spread of COVID-19 infection rates over space and time was heterogeneous. Hotspots of infection rate exhibited inconsistent clustered patterns over time. Among the selected risk factors, the annual household income, unemployment rate, population density, percentage of Caribbean population, percentage of adults aged 45-64 years old, and particulate matter concentrations were found to be positively associated with the COVID-19 infection rate. The findings assist policymakers in developing tailored public health interventions for COVID-19 prevention and control.

stat.AP↗

arXiv2021

A Framework for Evaluating Statistical Models in Physics Education Research

Across the field of education research there has been an increased focus on the development, critique, and evaluation of statistical methods and data usage due to recently created, very large data sets and machine learning techniques. In physics education research (PER), this increased focus has recently been shown through the 2019 Physical Review PER Focused Collection examining quantitative methods in PER. Quantitative PER has provided strong arguments for reforming courses by including interactive engagement, demonstrated that students often move away from scientist-like views due to science education, and has injected robust assessment into the physics classroom via concept inventories. The work presented here examines the impact that machine learning may have on physics education research, presents a framework for the entire process including data management, model evaluation, and results communication, and demonstrates the utility of this framework through the analysis of two types of survey data.

physics.ed-ph↗

arXiv2020

Predicting time to graduation at a large enrollment American university

The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto's Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).

physics.ed-ph↗

arXiv2019

Modeling student pathways in a physics bachelor's degree program

Physics education research has used quantitative modeling techniques to explore learning, affect, and other aspects of physics education. However, these studies have rarely examined the predictive output of the models, instead focusing on the inferences or causal relationships observed in various data sets. This research introduces a modern predictive modeling approach to the PER community using transcript data for students declaring physics majors at Michigan State University (MSU). Using a machine learning model, this analysis demonstrates that students who switch from a physics degree program to an engineering degree program do not take the third semester course in thermodynamics and modern physics, and may take engineering courses while registered as a physics major. Performance in introductory physics and calculus courses, measured by grade as well as a students' declared gender and ethnicity play a much smaller role relative to the other features included the model. These results are used to compare traditional statistical analysis to a more modern modeling approach.

physics.ed-ph↗

arXiv2019

Identifying features predictive of faculty integrating computation into physics courses

Computation is a central aspect of 21st century physics practice; it is used to model complicated systems, to simulate impossible experiments, and to analyze mountains of data. Physics departments and their faculty are increasingly recognizing the importance of teaching computation to their students. We recently completed a national survey of faculty in physics departments to understand the state of computational instruction and the factors that underlie that instruction. The data collected from the faculty responding to the survey included a variety of scales, binary questions, and numerical responses. We then used Random Forest, a supervised learning technique, to explore the factors that are most predictive of whether a faculty member decides to include computation in their physics courses. We find that experience using computation with students in their research, or lack thereof and various personal beliefs to be most predictive of a faculty member having experience teaching computation. Interestingly, we find demographic and departmental factors to be less useful factors in our model. The results of this study inform future efforts to promote greater integration of computation into the physics curriculum as well as comment on the current state of computational instruction across the United States.

physics.ed-ph↗

arXiv2018

A two-phase study examining perspectives and use of quantitative methods in PER

While other fields such as statistics and education have examined various issues with quantitative work, few studies in physics education research (PER) have done so. We conducted a two-phase study to identify and to understand the extent of these issues in quantitative PER . During Phase 1, we conducted a focus group of three experts in this area, followed by six interviews. Subsequent interviews refined our plan. Both the focus group and interviews revealed issues regarding the lack of details in sample descriptions, lack of institutional/course contextual information, lack of reporting on limitation, and overgeneralization or overstatement of conclusions. During Phase 2, we examined 72 manuscripts that used four conceptual or attitudinal assessments (Force Concept Inventory, Conceptual Survey of Electricity and Magnetism, Brief Electricity and Magnetism Assessment, and Colorado Learning Attitudes about Science Survey). Manuscripts were coded on whether they featured various sample descriptions, institutional/course context information, limitations, and whether they overgeneralized conclusions. We also analyzed the data to see if reporting has changed from the earlier periods to more recent times. We found that not much has changed regarding sample descriptions and institutional/course context information, but reporting and overgeneralizing conclusions has improved over time. We offer some questions for researchers, reviewers, and readers in PER to consider when conducting or using quantitative work.

physics.ed-ph↗

arXiv2018

Examining the relationship between student performance and video interactions

In this work, we attempted to predict student performance on a suite of laboratory assessments using students' interactions with associated instructional videos. The students' performance is measured by a graded presentation for each of four laboratory presentations in an introductory mechanics course. Each lab assessment was associated with between one and three videos of instructional content. Using video clickstream data, we define summary features (number of pauses, seeks) and contextual information (fraction of time played, in-semester order). These features serve as inputs to a logistic regression (LR) model that aims to predict student performance on the laboratory assessments. Our findings show that LR models are unable to predict student performance. Adding contextual information did not change the model performance. We compare our findings to findings from other studies and explore caveats to the null-result such as representation of the features, the possibility of underfitting, and the complexity of the assessment.

physics.ed-ph↗

arXiv2016

Methods for Analyzing Pathways through a Physics Major

Physics Education Research frequently investigates what students studying physics do on small time scales (e.g. single courses, observations within single courses), or post-education time scales (e.g., what jobs do physics majors get?) but there is little research into how students get from the beginning to the end of a physics degree. Our work attempts to visualize students paths through the physics major, and quantitatively describe the students who take physics courses, receive physics degrees, and change degree paths into and out of the physics program at Michigan State University.

physics.ed-ph↗

arXiv2016

Alternative model for the administration and analysis of research-based assessments

Research-based assessments represent a valuable tool for both instructors and researchers interested in improving undergraduate physics education. However, the historical model for disseminating and propagating conceptual and attitudinal assessments developed by the physics education research (PER) community has not resulted in widespread adoption of these assessments within the broader community of physics instructors. Within this historical model, assessment developers create high quality, validated assessments, make them available for a wide range of instructors to use, and provide minimal (if any) support to assist with administration or analysis of the results. Here, we present and discuss an alternative model for assessment dissemination, which is characterized by centralized data collection and analysis. This model provides a greater degree of support for both researchers and instructors in order to more explicitly support adoption of research-based assessments. Specifically, we describe our experiences developing a centralized, automated system for an attitudinal assessment we previously created to examine students' epistemologies and expectations about experimental physics. This system provides a proof-of-concept that we use to discuss the advantages associated with centralized administration and data collection for research-based assessments in PER. We also discuss the challenges that we encountered while developing, maintaining, and automating this system. Ultimately, we argue that centralized administration and data collection for standardized assessments is a viable and potentially advantageous alternative to the default model characterized by decentralized administration and analysis. Moreover, with the help of online administration and automation, this model can support the long-term sustainability of centralized assessment systems.

physics.ed-ph↗

arXiv2016

Exploring University Students' Engagement with Online Video Lectures in a Blended Introductory Mechanics Course

The advent of MOOCs has stimulated interest in using online videos to deliver content in university courses. We examined student engagement with 78 online videos that we created and were incorporated into a one-semester blended introductory mechanics course at the Georgia Institute of Technology. We found that students were more engaged with videos that supported laboratory activities than with videos that presented lecture content. In particular, the percentage of students accessing laboratory videos was consistently greater than 80 percent throughout the semester while the percentage of students accessing lecture videos dropped to less than 40 percent by the end of the term. Moreover, students were more likely to access the entirety of a laboratory video than a lecture video. Our results suggest that students may access videos based on perceived value: students appear to consider the laboratory videos as essential for successfully completing the laboratories while students appear to consider the lecture videos as something more akin to supplementary material. We found there was little correlation between student engagement with the videos and the performance in the course. In addition, an examination of the in-video content suggests that students focus more on concrete information that is explicitly required for assignment completion (e.g., actions required to complete laboratory work, or formulas/mathematical expressions needed to solve particular problems) and less on content that is considered more conceptual in nature. The results of the study suggest ways in which instructors may revise courses to better support student learning.

physics.ed-ph↗

arXiv2014

Peer Evaluation of Video Lab Reports in an Introductory Physics MOOC

Assessing student performance becomes challenging when course enrollment becomes very large (~10^4 students). As part of a Massive Open Online Course (MOOC) in introductory physics offered by Georgia Tech in 2013, students submitted video reports on mechanics labs. Peer evaluation of these reports provided the primary method for evaluating student laboratory work. This paper describes the methods developed and used to guide students in evaluating each other's video lab reports. We also discuss how students' peer evaluation behavior changed with different interventions provided in the course.

physics.ed-ph↗

arXiv2014

Peer Evaluation of Video Lab Reports in a Blended Introductory Physics Course

The Georgia Tech blended introductory calculus-based mechanics course emphasizes scientific communication as one of its learning goals, and to that end, we gave our students a series of four peer-evaluation assignments intended to develop their abilities to present and evaluate scientific arguments. Within these assignments, we also assessed students' evaluation abilities by comparing their evaluations to a set of expert evaluations. We summarize our development efforts and describe the changes we observed in student evaluation behavior.

physics.ed-ph↗

arXiv2014

Student Use of a Single Lecture Video in a Flipped Introductory Mechanics Course

In the Fall of 2013, Georgia Tech offered a 'flipped' calculus-based introductory mechanics class as an alternative to the traditional large-enrollment lecture class. This class flipped instruction by introducing new material outside of the classroom through pre-recorded, lecture videos. Video lectures constituted students' initial introduction to course material. We analyze how students engaged with online lecture videos via 'clickstream' data, consisting of time-stamped interactions (plays, pauses, seeks, etc.) with the online video player. Analysis of these events has shown that students may be focusing on elements of the video that facilitate a 'correct' solution.

physics.ed-ph↗

arXiv2013

Transforming High School Physics with Modeling and Computation

The Engage to Excel (PCAST) report, the National Research Council's Framework for K-12 Science Education, and the Next Generation Science Standards all call for transforming the physics classroom into an environment that teaches students real scientific practices. This work describes the early stages of one such attempt to transform a high school physics classroom. Specifically, a series of model-building and computational modeling exercises were piloted in a ninth grade Physics First classroom. Student use of computation was assessed using a proctored programming assignment, where the students produced and discussed a computational model of a baseball in motion via a high-level programming environment (VPython). Student views on computation and its link to mechanics was assessed with a written essay and a series of think-aloud interviews. This pilot study shows computation's ability for connecting scientific practice to the high school science classroom.

physics.ed-ph↗

arXiv2013

Integrating Numerical Computation into the Modeling Instruction Curriculum

We describe a way to introduce physics high school students with no background in programming to computational problem-solving experiences. Our approach builds on the great strides made by the Modeling Instruction reform curriculum. This approach emphasizes the practices of "Developing and using models" and "Computational thinking" highlighted by the NRC K-12 science standards framework. We taught 9th-grade students in a Modeling-Instruction-based physics course to construct computational models using the VPython programming environment. Numerical computation within the Modeling Instruction curriculum provides coherence among the curriculum's different force and motion models, links the various representations which the curriculum employs, and extends the curriculum to include real-world problems that are inaccessible to a purely analytic approach.

physics.ed-ph↗

arXiv2013

The Initial State of Students Taking an Introductory Physics MOOC

As part of a larger research project into massively open online courses (MOOCs), we have investigated student background, as well as student participation in a physics MOOC with a laboratory component. Students completed a demographic survey and the Force and Motion Conceptual Evaluation at the beginning of the course. While the course is still actively running, we have tracked student participation over the first five weeks of the eleven-week course.

physics.ed-ph↗

arXiv2012

Understanding Student Computational Thinking with Computational Modeling

Recently, the National Research Council's framework for next generation science standards highlighted "computational thinking" as one of its "fundamental practices". 9th Grade students taking a physics course that employed the Modeling Instruction curriculum were taught to construct computational models of physical systems. Student computational thinking was assessed using a proctored programming assignment, written essay, and a series of think-aloud interviews, where the students produced and discussed a computational model of a baseball in motion via a high-level programming environment (VPython). Roughly a third of the students in the study were successful in completing the programming assignment. Student success on this assessment was tied to how students synthesized their knowledge of physics and computation. On the essay and interview assessments, students displayed unique views of the relationship between force and motion; those who spoke of this relationship in causal (rather than observational) terms tended to have more success in the programming exercise.

physics.ed-ph↗