+1 (801) 644-8819
Brigham Young University, Provo, UT
Ph.D., Instructional Psychology and Technology, April 2018
Brigham Young University, Provo, UT
B.S., Neuroscience, April 2014
Primary writing contributor to McKay School of Education Research Grant ($32,000)
BYU Graduate Research Fellowship ($15,000 - awarded to 12% of applicants)
Data Mining Fellowship from the Digital Learning and Social Media Research Group ($1,500 - awarded to 5 applicants)
National Science Foundation (NSF) Graduate Research Fellowship – Honorable Mention
Graduate Studies and Department Travel Grants ($3,000)
BYU Library Student Research Grant ($1,500 - awarded to 10 applicants)
BYU Graduate Research Fellowship ($15,000 - awarded to 28% of applicants)
Open Educational Resources Fellowship from the Open Education Group ($5,000)
Major writing contributor to a BYU Mentored Environment Grant ($16,800)
Measuring Uncertainty in Assessments through Mouse Tracking - December 2014
Research and Industry Experience
Senior Data Scientist at Lumen Learning
February 2019 – Present
Support the learning and marketing teams with analytics insights. Build predictive models to improve teaching and learning through data-driven decision making. Conduct learning analytics research.
Data Scientist at MarketDial
November 2017 – February 2019
Develop monte carlo simulations to improve control matching methods. Increase rigor of statistical analyses conducted in data science platform to handle autocorrelated time-series data. Handle panel data using hierarchical and generalized linear models. Optimize slow SQL queries to handle hundreds of millions of rows of data.
Data Scientist at Brigham Young University
April 2017 – November 2017
Build an early alert warning system to help freshmen succeed in their first year of classes. Build a model for both interpretation and prediction. Use algorithms such as support vector machines, neural networks, random forest, XGBoost, and logistic regression for classification.
Data Scientist at Lumen Learning, LLC
January 2016 – November 2017
Use exploratory and confirmatory factor analyses and item response theory (IRT) to improve educational assessments. Use learning analytics to evaluate open educational resources. Conduct data preprocessing and statistical analysis on data from personalized learning system. Use neural networks and latent class analysis (clustering) to improve personalized learning products.
The Greaves Group, October 2016 - December 2016
Synthesize and create personalized learning materials. Analyze personalized learning plans on root cause analysis criteria.
Research with Dr. Charles Graham, April 2014 - April 2018
Design, develop, and implement real-time learner dashboards. Use data mining and machine learning methods to explore online learner system data (Canvas, BrainHoney, xAPI) and identify patterns, build predictive models, and cluster students into groups. Test the effectiveness of learning analytics dashboards using design based research, randomized control trials, focus groups, and evaluation surveys.
Research with Dr. Ross Larsen, April 2014 - August 2017
Simulate categorical and continuous correlated variables using innovative brute force approach. Conduct mean weighted propensity score analysis allowing betas in logistic regression to vary or averaging betas across all group comparisons. Use supercomputer to conduct simulation studies.
Research with Dr. Rick West, April 2014 - March 2018
Collect 20 years of educational technology journal article metadata. Code articles on methodology categories. Analyze and create visualizations of trends across time. Create Scopus API script to extract article metadata from Scopus. Create AltMetrics API script to extract altmetric data from AltMetrics.
Quantitative Research Intern
Knod Global Learning, August 2014 – April 2015
Analyze survey data. Conduct mean difference testing between groups on various outcome variables to determine impact
Bodily, R., Leary, H. & West, R. (2019). Research trends in instructional design and technology journals. British Journal of Educational Technology.
Bodily, R., Ikahihifo, T. B., Mackley, B., & Graham, C. R. (2018). The design, development, and implementation of student-facing learning analytics dashboards. Journal of Computing in Higher Education 30(3), 572-598. http://doi.org/10.1007/s12528-018-9186-0
Bodily, R., Larsen, R., & Warne, R. T. (2018). Piecewise propensity score analysis: A new method for conducting propensity score matching with polytomous ordinal independent variables. Archives of Scientific Psychology, 6(1), 14. http://dx.doi.org/10.1037/arc0000035
Bodily, R., & Wood, S. (2017). ConfChem Conference on Select 2016 BCCE Presentations: Tracking Student Use of Web-Based Resources for Chemical Education. Journal of Chemical Education, 94(12), 2010-2012. http://doi.org/10.1021/acs.jchemed.6b00976
Bodily, R., & Verbert, K. (2017). Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies, 10(4), 405-418. http://doi.org/10.1109/TLT.2017.2740172
Henrie, C.R., Bodily, R., Larsen, R., & Graham, C. R. (2017). Exploring the potential of LMS log data as a proxy measure of student engagement. Journal of Computing in Higher Education, 1-19. http://doi.org/10.1007/s12528-017-9161-1
Bodily, R., Nyland, R., & Wiley, D. (2017). The RISE Framework: Using learning analytics to automatically identify open educational resources for continuous improvement. International Review of Research in Open and Distributed Learning, 18(2). http://doi.org/10.19173/irrodl.v18i2.2952
Davies, R.S., Nyland, R., Bodily, R., Chapman, J., Jones, B., & Young, J. (2017). Designing technology-enabled instruction to utilize learning analytics. Tech Trends 61(2), 155-161. https://doi.org/10.1007/s11528-016-0131-7
West, R., Thomas, R., Bodily, R., Wright, C., & Borup, J. (2017). An analysis of instructional design and technology departments. Educational Technology, Research, and Development 65(4). https://doi.org/10.1007/s11423-016-9490-1.
Bodily, R., Graham, C., & Bush, M. (2017). Online learner engagement: Opportunities and challenges with using data analytics. Educational Technology 57(1), 10–18. http://www.jstor.org/stable/44430535
Belikov, O., & Bodily, R. (2016). Incentives and barriers to OER adoption: A qualitative analysis of faculty perceptions. Open Praxis, 8(3). http://dx.doi.org/10.5944/openpraxis.8.3.308
Howland, S., Martin, T., Bodily, R., Faulconer, C., & West, R. (2016). Educational technology research journals: International journal of computer-supported collaborative learning, 2006 – 2014. Educational Technology, 55(6). https://eric.ed.gov/?id=EJ1079566
Porter, W. W., Graham, C. R., Bodily, R., & Sandberg, D. S. (2016). A qualitative analysis of institutional drivers and barriers to blended learning adoption in higher education. The Internet and Higher Education, 28, 17-27. https://doi.org/10.1016/j.iheduc.2015.08.003
Henrie, C. R., Bodily, R., Manwaring, K. C., & Graham, C. R. (2015). Exploring intensive longitudinal measures of student engagement in blended learning. The International Review of Research in Open and Distributed Learning, 16(3). http://dx.doi.org/10.19173/irrodl.v16i3.2015
Publications Under Review
Bodily, R., Sansom, R. L., Hansen, C. O., Leary, H. (under review). Increasing student use of a learning analytics dashboard using a design-based research approach.
Bodily, R., Ikahihifo, T., & Larsen, R. (under review). Examining the effect of two student-facing learning analytics dashboards on student behavior and achievement.
Publications in Conference Proceedings
Bodily, R., Kay, J., Aleven, V., Davis, D., Jivet, I., Xhakaj, F., & Verbert, K. (2018). Open learner models and learning analytics dashboards: A systematic review. In Proceedings of the Eight International Learning Analytics & Knowledge Conference (pp. 1-10). ACM. http://doi.org/10.1145/3170358.3170409
Bodily, R., & Verbert, K. (2017). Trends and issues in student-facing learning analytics reporting systems research. In Proceedings of the Seventh International Learning Analytics and Knowledge Conference (LAK ‘17) (pp. 309-318). ACM. https://doi.org/10.1145/3027385.3027403
Nagashima, T., Xiong, Y., & Bodily, R. (2018, October). Student engagement and learning in an OER-based course: A longitudinal study. 15th Annual Open Education Conference, Niagara Falls, NY, United States.
Bodily, R., & Mackley, B. (2017, October). A model and platform to facilitate open assessments. 14th Annual Open Education Conference, Anaheim, CA, United States.
Bodily, R., Strader, R., & Wiley, D. (2017, October). Continuously improving content, assessments, and outcome alignment in OER courses. 14th Annual Open Education Conference, Anaheim, CA, United States.
Bodily, R., Strader, R., & Wiley, D. (2017, October). An approach to implementing nudging for teachers and students in OER courses. 14th Annual Open Education Conference, Anaheim, CA, United States.
Sansom, R., & Bodily, R. (2017, April). Design-based research to improve student perceptions of autonomy and efficiency in an online learning system. From Abstracts of Papers, 253rd ACS National Meeting & Exposition, CHED-289, San Francisco, CA, United States.
Bodily, R., & Verbert, K. (2017, March). Trends and issues in student-facing learning analytics reporting systems research. Learning Analytics and Knowledge Conference, Vancouver, Canada.
Mackley, B., & Bodily, R. (2016, November). Crowdsourcing open assessment items. 13th Annual Open Education Conference, Richmond, VA.
Bodily, R., Nyland, R., & Wiley, D. (2016, November). The RISE framework: Continuously improving OER using learning analytics. 13th Annual Open Education Conference, Richmond, VA.
Bodily, R., Mackley, B., Kerr, T., & Graham, C. R. (2016, October). Designing, developing, and evaluating a real-time student dashboard. Association for Educational Communications and Technology, Las Vegas, NV.
Bodily, R., Kerr, T., Mackley, B., & Graham, C. R. (2016, October). Examining the effect of a real-time student dashboard on student behavior and student achievement. Association for Educational Communications and Technology, Las Vegas, NV.
Davies, R.S., Nyland, R., Bodily, R., Chapman, J., Jones, B., & Young, J (2016, October). What should instructional designers know about learning analytics? Association for Educational Communications and Technology, Las Vegas, NV.
Bodily, R. & Wood, S. G. (August 2016). Tracking student use of web-based resources for chemical education. Biennial Conference on Chemical Education, Greeley, CO.
Bodily, R. & Wood, S. G. (August 2016). Using instructional videos, online quizzes, and real-time dashboards to improve student learning in online environments. Biennial Conference on Chemical Education, Greeley, CO.
Wood, S. G. & Bodily, R. (August 2016). Examining the flipped classroom design: Are students even watching the pre-lecture videos? Biennial Conference on Chemical Education, Greeley, CO.
Bodily, R. & Henrie, C. (2015, November). Examining the effect of real-time feedback on student behavior and student achievement. ICEM Graduate Panel at Association for Educational Communications and Technology, Indianapolis, IN.
Bodily, R., Howland, S., Martin, T., Faulconer, C., & West, R. (2015, November). Educational technology research journals: International journal of computer-supported collaborative learning, 2006 – 2014. Association for Educational Communications and Technology, Indianapolis, IN.
Jenkins, J. L., Larsen, R., Bodily R., Sandberg, D., Williams, P., Stokes, S., Harris, S., & Valacich, J. S. (2015, August). A multi-experimental examination of analyzing mouse cursor trajectories to gauge subject uncertainty. Americas Conference on Information Systems. Puerto Rico.
Henrie, C. R., Halverson, L. R., Bodily, R., Sandberg, D., & Graham, C. R. (2014, July). Measuring learner engagement in blended and online contexts: A review and two case studies. 11th Annual Online Learning Consortium Blended Learning Conference and Workshop. Denver, CO.
Manwaring, K., Halverson, L. R., Henrie, C. R., Bodily, R., & Graham, C. R. (2014, July). Course design from the learner’s point of view through the use of intensive longitudinal methods. 11th Annual Online Learning Consortium Blended Learning Conference and Workshop. Denver, CO.
Bodily, R., McCormick, A., Nielson, J., & Graham, C. R. (2018, April). Examining the effect of question creation on student exam performance. Won first place in graduate student division of McKay School Mentored Research Conference Brigham Young University.
Hansen, C., Bodily, R., & Sansom, R. (2017, April). Design elements of online learning systems that increase student use and perceptions of optional online study materials. From Abstracts of Papers, 253rd ACS National Meeting & Exposition, CHED-767, San Francisco, CA, United States.
Bodily, R. (2016, July). Adding predictive elements to student and instructor dashboards. Pittsburgh Summer LearnLab. Pittsburgh, PE.
Bodily, R., Kerr, T., Mackley, B., & Graham, C.R. (2016, March). Designing and evaluating a student dashboard. McKay School Mentored Research Conference Brigham Young University.
Henrie, C., Bodily, R., Graham, C. R., & Larsen, R. (2015, March). Using log data as a proxy for student engagement survey scores. McKay School Mentored Research Conference Brigham Young University.
Sandberg, D., Bodily, R., Harris, S., Larsen, R., & Jenkins, J. (2015, March). You can see it in their hands: Exploring correlates between user’s uncertainty and mouse cursor movements. McKay School Mentored Research Conference Brigham Young University.
Henrie, C.R., Bodily, R., & Graham, C.R. (2014, March). Measuring online and blended learner engagement. McKay School Mentored Research Conference Brigham Young University.
Bodily, R., & Mackley, B. (2017, October). Entrepreneurship in Education. Seminar for the Instructional Psychology and Technology department at BYU.
Angerhoffer, P., Whitchurch, M., Wisco, J., Bodily, R. (2017, November). Open Educational Resources. Brigham Young University Copyright Symposium 2017.
Barrus, A., Chapman, J., Bodily, R., Rich, P. (2016). Using educational technologies to scaffold high school and college students’ skill & will to plan, practice, and produce in Lin, L. & Atkinson, R. K. (Eds). Educational Technologies: Challenges, Applications and Learning Outcomes. New York: Nova Science Publishers.
Instructor of Graduate Courses
Brigham Young University
IPT 760R: Advanced E-Learning Development (Python Programming) (instructor)
IPT 692R: Statistical Learning in R (Machine Learning) (co-instructor)
Reviewer (updated 10/5/2018)
Computers and Education (3 papers)
Learning Analytics and Knowledge Conference (4 papers)
IEEE Transactions in Learning Technologies (2 papers)
Journal of Computing in Higher Education (5 papers)
International Review of Research in Open and Distance Learning (5 papers)
PLoS ONE (1 paper)
Journal of Sustainability in Higher Education (1 paper)
Journal of Chemical Education (1 paper)
Graduate Student Panel at the Association of Educational Communications and Technology conference (5 papers)
Chair of Society of Learning Analytics Graduate Student Special Interest Group
April 2017 – Present
Create a special interest group for graduate students in learning analytics. Manage and delegate efforts to help graduate students become integrated into the learning analytics community. Provide opportunities for graduate students to receive mentoring and networking from top learning analytics scholars. Build a community of graduate students interested in learning analytics.
President of BYU IPT Graduate Student Organization
August 2016 – August 2017
Lead the selection process for student organization officers for the upcoming year, plan service, leadership, and professional development activities throughout the year
Vice President of BYU IPT Graduate Student Organization
August 2015 – August 2016
Plan professional development activities, organize and promote social events, increase networking within the department and with other professionals in the instructional technology field
AECT 3-Minute Thesis Committee
November 2015 – Present
Plan 3-Minute Thesis competition at AECT. Recruit students to participate in the competition. MC the competition.
2017-2018 IP&T Outstanding Student Researcher Award
Awarded to one graduate student each year for their research accomplishments
Sept 2018 - Present
Co-Founder of Wadayano.com
May 2018 - Present
A knowledge monitoring awareness quizzing system designed to help students increase their metacognitive skills by providing visualizations and reports showing gaps between what students know and what they think they know.
Co-Founder of Prendus, LLC
April 2016 - April 2018
Prendus is an online learning and assessment platform. Students create questions, review student-generated questions, and practice on the item bank they have created. Teachers get real-time analytics on question quality and student content mastery. Students learn better by engaging in higher-level thinking activities and have unlimited practice on questions in class repositories.
Project Manager for Learning Analytics System Dashboard Project
April 2015 - April 2018
Quiz Application (Open Assessments): Managed the implementation of Open Assessments on Amazon Web Services, the implementation of additional question types (short answer and essay), and the implementation of xAPI to track student activity
Video Application (Ayamel): Managed implementation of xAPI to track student activity
Dashboards: Managed 1) implementation of LTI as a tool consumer and tool provider, 2) xAPI to track student activity, and 3) real-time visualizations of student activity
Learning Record Store: Managed the implementation of Learning Locker (an open source LRS) to store student activity data from multiple sources
Programming and Statistical Expertise
– Python (pandas, scipy, numpy, statsmodel, matplotlib, sklearn, beautiful soup, NLTK)
– R (lme4, ggplot2, lavaan, eRm)
– SQL and NoSQL databases (mySQL, postgresql, MongoDB, GraphQL)
– JSON, XML, CSV
Statistical Methods Expertise
– t-tests and ANOVA
– Linear regression
– Logistic regression
– Structural equation modeling
– Latent class analysis/latent profile analysis
– Factor analysis (exploratory, confirmatory)
– Item response theory
Data Mining Methods Expertise
– Support vector machines
– Neural networks in keras (Python)
– Decision trees
– XGBoost (boosted decision trees)
– Random forest
– Linear/logistic regression
– Clustering (k-means and hierarchical)
– Natural language processing/text mining
Technical Project Experience
Google Scholar Profile Analyzer
Use Python to analyze a Google Scholar profile. Provide a Google Scholar profile link, and the script will provide most frequent collaborators, most frequent venues, most frequent topics from titles, publications over time, citations over time, and a word cloud of words used in titles.
Web-based game (The Professor)
Android/iOS Cross-Platform Mobile Application
Use python (back-end), Corona SDK and Lua (front-end), and Material UI (front-end framework) to build a functional mobile application to measure creativity.
SCOPUS API Integration
Retrieve article metadata for 65 journals in the educational technology field. Process the data and create data visualizations to succinctly summarize reports.
Ebscohost Web Scraper
Article metadata web scraper (python) to obtain authors, abstracts, titles, keywords, and author keywords for more than 50,000 articles using BeautifulSoup, Mechanize, and Requests libraries in python
BrainHoney DLAP API Analytics
Python script to extract online student interaction data from the BrainHoney DLAP API and conduct data preprocessing for analysis
Canvas API Analytics
Python script to extract online student interaction data from the Canvas API and conduct data preprocessing for analysis
SPSS & MPlus automated analysis
Python scripts to run a regression, check collinearity diagnostics, output model statistics, graph a histogram of the residuals, graph a residual plot, and conduct analysis in MPlus for FIML and clustering using the following python libraries: pandas, spss, spssaux, matplotlib, numpy, and subprocess
Amazon Mechanical Turk/Qualtrics Integration
Python scripts to create qualification tests in MTurk, automatically pull results from qualtrics and MTurk, and automatically approve HITs based on survey codes.
Family History Opportunity Finder
Python script to pull family history information using the familysearch API to identify promising family research opportunities.
Learning Analytics and Knowledge (LAK) Member
Association for Educational Communications and Technology (AECT) Member
Certificate of accomplishment
Offered by Hastie and Tibshirani on https://lagunita.stanford.edu/ (March 2016)
Big Data in Education
Certificate with distinction
Offered by Dr. Ryan Baker on EdX.org (August 2015)
An Introduction to Interactive Programming in Python
Certificate with distinction
Offered by Rice University on Coursera.org (June 2014)