Michtom School of Computer Science

Some of the examples below represent on-going innovation in data scientific applications by multiple generations of DSI Interns. Others are of work already completed by DSI Interns and designed to update automatically. Others still are of successful prototypes built by DSI Interns that are now poised to become fully integrated by full-time staff or technical consultants into existing work flows.

Campus Planning and Operations

• Improving response to daily building alarms: DSI Intern built machine-readable dataset of alarms from all campus buildings, then created analytics to pinpoint faulty alarms, missing sensors, and disproportionately high percentages associated with specific buildings. These analytics help increase the capacity of administrators to rely on data-informed decision-making to allocate scarce staff resources to response and repair.
• Enabling strategic view of utilities infrastructure: DSI Intern designed the schema necessary to create a spatial data platform for viewing all buildings, infrastructure, and utilities components on a campus map. The prototype geographical information system includes precise details of sewer pipes, manhole covers, and electric submeters, all aligned with location-specific measures of deferred maintenance, enabling administrators to more effectively evaluate potential investments (“does it make sense to install a bathroom on this spot in this field?”) and troubleshoot building failures (“which water pipe might be responsible for that manhole spewing so much steam?”)
• Measuring and predicting electricity use: DSI Intern engineered the integration of data from multiple current and historical sources to create a machine-readable dataset of electricity usage by campus building. DSI Intern then created a dashboard that allows administrators to analyze usage levels and patterns for each building across seasons (winter, spring, summer, fall) and functions (residence hall, science building, gym, etc). The DSI Intern then began designing predictive tools for better estimating energy usage by building as well as to highlight relevant anomalies deserving of strategic attention. For senior thesis in Computer Science based on this work, please read here.

Dean of Arts & Sciences

• Analyzing trends in course enrollments and instructor workloads: DSI Interns engineered and integrated datasets to make it possible to build dashboards of historical and current data on undergraduate course enrollments and instructor workloads. The dashboards now make it possible to disaggregate the data by academic department, instructor, undergraduate major, and individual course. Experimental analytics by DSI Interns based on this new data platform demonstrate the possibility of predicting enrollments in required and high traffic courses, a functionality that, once formally implemented, enables administrators to more effectively plan and allocate academic resources.

Graduate School of Arts & Sciences

• Analyzing trends in course enrollments for 100+ level courses across the university’s graduate programs: DSI Intern will leverage the data engineering and dashboard design expertise of DSI Interns working in the Dean of Arts & Sciences to build out a dashboard of course enrollment trends and analytics specific to graduate school courses. As with other DSI Intern projects that introduce dashboard functionality to existing work-flows, this will require integration of multiple data sets from various sources. Accomplishment of the goals of this project will likely bring dramatically increased efficiencies to administrators responsible for assessing and allocating academic resources at the graduate level.

Human Resources

• Launching a Human Capital Management dashboard of  Organizational metrics: DSI Interns began by assembling a prototype HR dashboard, then integrating the data streams and analytics necessary to create metrics showing turnover rates, time-to-fill for open positions, degree of diversity in recruitment pools, and completion rates for performance reviews. DSI Interns also helped collect feedback from a pilot group of users across the university to improve the design and usability of the dashboard. DSI Interns are now creating predictive analytic tools that, once formally implemented through the HR dashboard, will help senior leaders foresee and manage workforce related challenges and opportunities.

Institutional Advancement

• Building predictive models of charitable giving and of bequests that are customized to Brandeis alumni, donors, and friends: DSI Interns bring data scientific analyses to an entire corpus of Brandeis alumni and fundraising data, including current and historical data dating back to the founding of the university in 1948, enabling them to identify key characteristics of those most likely to make major gifts or to leave bequests to the university. These analyses are now making it possible for DSI Interns to design predictive tools capable of generating data-informed guidance to administrators making resource allocation decisions regarding the cultivation of potential donors.

Information and Technology Services

• Expanding data analytic client services capacity: DSI Interns develop trustworthy and reliable expertise in report generation through Workday (the university’s new cloud-based enterprise management system) which helps administrators broaden data scientific support across the university. For example, DSI Interns in ITS were instrumental in helping to roll out the Human Capital Management dashboard.
• Experimenting with potential improvements to Workday implementation: DSI Interns in ITS help specify potential solutions to data-management challenges that emerge in the Workday implementation process. This has included identifying opportunities for improvement in the Workday Student module, and providing technical description of functionalities that need to be activated in order to better serve the Brandeis student community.
• Speeding up deployment of data management innovations: DSI Intern was able to create an automated solution to the NSF’s required annual Higher Education Research and Development (HERD) survey thanks to application of innovative data management tool Prism. Specifically aimed at enabling the Workday ERP to effectively integrate non-native data, the Prism tool had promised to be valuable to Brandeis data-related workflow, but full-time staff resources had not yet been available to acquire expertise to deploy the tool.

Office of the Provost

• Enabling strategic understanding of Leaves of Absence (LOA) taken by undergraduates: Intern engineered and integrated multiple sources of unstructured data into a machine-readable dataset relevant to LOA students, including reasons for the leaves, length, graduation rates, academic majors, and demographic characteristics of the LOA students. Creating a dashboard based on this dataset has now made it possible for administrators to begin analyzing patterns and implications of LOA trends for potential interventions to help improve student outcomes. The DSI Intern also automated the process for regularly integrating data and updating analytics based on information from new generations of students.
• Analyzing bias in student course evaluations: DSI Intern created a machine-readable data analytic platform capable of identifying demographic bias driving student responses in course evaluations. This required exploration and application of appropriate statistical techniques for the complex social science analysis of biased behaviors. Preliminary results show surprisingly low to no discrimination from Brandeis students based on the gender of instructors. Results for potential race and ethnicity bias are inconclusive, so far, due to insufficient data. 

Office of Research Administration

• Visualizing trends in sponsored research and predicting likelihood that any one particular grant proposal will be funded: thanks to the dashboard built by DSI Interns, experts in research funding at Brandeis are now able to more easily share trends with senior academic leaders about the percentage of grant proposals that are successfully funded, with break-outs by department, principal investigator (PI), faculty rank of PI, and individual granting agency. DSI Interns are also working on predictive tools that will help make it possible for administrators to identify not only the likelihood of a proposal’s success, but also the specific characteristics of the proposal that might increase or decrease its likelihood of success.

Student Affairs

• Creating interactive access to outcomes data and to analytics that answer administrators’ Brandeis-relevant questions from the National College Health Assessment (NCHA) survey: DSI Intern transformed NCHA data into format capable of supporting user friendly dashboard. This now makes it possible for administrators to better understand risks associated with student wellness and to design programs and interventions targeted to address these data-informed insights. DSI Intern automated the process for updating the dashboard with new results from the NCHA. For more detail on this DSI Intern project, see section below on “Measurable Impact.”

We are beginning to explore various ways to measure the impact of the DSI Internship concept. Our sense is that each DSI Intern-Admin partnership will suggest somewhat different ways of measuring impact.

For example, in the case from Student Affairs, below, the administrator reported a 99% reduction in time needed to access relevant analytics, thanks to the dashboard built with the DSI Intern. Measuring the amount of time an administrator is dedicating to getting questions answered seems like a meaningful measure of impact.

Another example we see from the Student Affairs case: counting the numbers of program modifications, or new programs and solutions that administrators are able to implement as a result of the data scientific tools and techniques introduced by the DSI Intern, also seems like a path worth pursuing. The case study below describes one such program modification resulting from analytics that would not otherwise have been evident to Student Affairs staff had it not been for the dashboard built through the DSI Intern project.

Our explorations of impact measurement will also include the perspective of the DSI Intern’s educational experience. We know that in the Student Affairs case, the DSI Intern faced the responsibility of quickly and effectively teaching themselves “plotly” and “dash” (python-based app design tool that is open source) to build the requisite dashboard. The Intern acquired these skills while learning to support an intensely iterative process with their administrative mentor, acquiring domain knowledge and testing software development progress along the way. So, a combination of mastering new technical skills, self-directed learning, and organizational leadership was required to get the job done in this case. We will be designing a survey to help capture and measure this type of educational experience.

Case Study of Impact: Student Affairs

As part of its responsibility for supporting and improving student well-being on campus, the Division of Student Affairs conducts the National College Health Assessment (NCHA) of the American College Health Association (ACHA) on the Brandeis campus. The value of participating in the survey is that Brandeis Student Affairs is able to receive and analyze completed data to better inform program design and implementation plans regarding student wellness. Unfortunately, student affairs professionals across the country have been constrained in their ability to leverage the NCHA data in support of wellness objectives because of the format of the data: the ACHA manages and delivers the survey through SPSS, a statistical software package introduced 55 years ago which lacks the capability to support quick access to key insights with the greatest relevance to decision-making.

The DSI Intern converted the SPSS data into formats that are machine-readable by modern programming languages like python. This made it possible for the DSI Intern to build a dashboard for easy interactivity with the data. The result is a dramatic reduction in the time needed to access key findings in the data, which in turn dramatically raises the capacity of Brandeis Student Affairs professionals to pull practical relevance out of NCHA data. The application of data scientific techniques to NCHA data at Brandeis has also made it possible to conduct analyses that would not otherwise be possible: these include most importantly the disaggregation of data across multiple groupings, such as race, gender, academic program, family background, and year of graduation.

Take alcohol consumption as a student wellness issue of obvious importance that needs measuring, monitoring, and guidance on a college campus: student affairs professionals are naturally concerned about risky behaviors involving alcohol among first-year students, since the inexperience of first-years suggests that they stand to benefit most from extra guidance and education. Yet, the data scientific analyses of the NCHA data at Brandeis, which could be disaggregated by class year, demonstrated that it is juniors and seniors who are most engaging in dangerous behaviors involving alcohol. As a result, Brandeis professionals are now redesigning student wellness materials to incorporate the extra support needed by upper-class students.

DSI Intern automated the process by which new rounds of NCHA survey data will be updated into the interactive dashboard. The creation of the NCHA dashboard promises to generate many examples of positive impact.

Details of research we are launching based on DSI Internship project on “reverse mentoring” and on “scrum and agile methodologies” are forthcoming.

• Tim Hickey, Professor, Undergraduate Advising Head, Computer Science 
• Jessica Liebowitz, Research Scholar, Computer Science
• Olga Papaemmanouil, Professor, Computer Science, and Senior Associate Dean of Academic Affairs
• Pito Salas, Professor of Practice, Computer Science
• Niawen Xue, Professor and Chair of Computer Science, ex officio
• Antonella Di Lillo, Associate Professor, Computer Science

Co-Directors

Jessica Liebowitz is a Research Scientist in Computer Science at Brandeis, where she has helped launch the Data Science Internal (DSI) Internship. This project creates partnerships between undergraduates in data science and administrative offices across the university. It oversees the experiential learning of participating students, and tracks the impact of bringing data science, machine learning, and artificial intelligence into higher ed operations. It grew out of Dr. Liebowitz's and Professor Tim Hickey's data analytic research on the higher ed workforce.

Dr. Liebowitz holds a PhD from Harvard University, BA from Yale University, and Honorary Doctorate from Middlebury College.

Timothy J. Hickey is a Professor of Computer Science whose current work focuses on educational technology, brain-computer interfaces and game-based learning. His specialties include analysis of algorithms, logic programming and parallel processing, symbolic manipulation, and groupware.

For information on applying to be a DSI Intern, or becoming an administrative partner, please contact Sandra Iriskic, Executive Administrator.