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Five Questions with Henry Zheng

Vice Provost for Institutional Effectiveness and Planning at Carnegie Mellon University, USA

Connect with Henry

Henry chose to be interviewed in chair #3.

Photo of Henry Zheng wearing a suit and tie, glasses, and standing in front of a redbrick wall.

1. What is your job title?

Vice Provost for Institutional Effectiveness and Planning at Carnegie Mellon University in Pittsburgh, Pennsylvania, USA.

2. Briefly, how would you describe your role in your institution?

As the Vice Provost for Institutional Effectiveness and Planning, I am the senior-most institutional research leader for Carnegie Mellon University (CMU), and report to the provost of the university. I lead a team of researchers and analysts who provide both internal and external reporting and data analysis expertise to the university. In this capacity, I play an active role in leading, championing, and facilitating data-based decision making at CMU. I serve as the primary liaison between the office of institutional effectiveness and planning and other Carnegie Mellon offices, providing both evidence to university leaders in support of strategic priorities and decision making, and analysis capabilities to the broader university community.

3. From your perspective, what will be the key skills, capabilities, and knowledge required for institutional research moving forward?

Institutional researchers nowadays utilise a wide range of skills, capabilities, and knowledge to do their jobs effectively, depending on their institution’s size, mission, sector, and organisational structure. In my opinion, Patrick Terenzini provided a very elegant framework [1] (originally proposed in 1993 and revised in 2013) that described such skills, capabilities, and knowledge requirements.

According to Terenzini, institutional research work requires three tiers of ‘intelligence’ to be effective. Tier-1 is technical or analytical intelligence. The first is the body of technical knowledge and information required to be an IR practitioner. Tier 1 intelligence can include a working command of statistics, research methods, and computer applications that help organise, manage, and analyse data resources (e.g., SPSS, SAS, R, STATA, Microsoft Access, Microsoft Excel, and SQL). With the rapid advances of analytical applications, this also means that you need to keep your analytical toolset current. Think Tableau, PowerBI, Python, and other newer tools available.

Tier-2 is issue intelligence. This refers to knowledge and understanding of the major operational domains and issues for colleges and universities. For example, IR practitioners need to know:

  • what enrolment management is about
  • how it works
  • what data resources are generated from the enrolment management process
  • how data can be used to improve that process.

To be effective, a good IR practitioner needs to develop familiarity, and preferably expertise, in the operational areas they do most of their analysis. You simply cannot be a good IR researcher if you do not understand the content areas where your analysis is applied to.

Tier-3 is contextual intelligence. According to Terenzini, contextual intelligence is about understanding the campus operating environment, the relationship dynamics, and the strategic imperatives of a university. Contextual intelligence is about the understanding of your institution’s position in relation to state/province, regional, national, and international contexts and issues. To be able to become highly effective in institutional research, it is essential to develop contextual intelligence. For example, when Coursera went public with their IPO, or when 2U purchased edX from Harvard and MIT. You need to know what these transactions are about and how they might relate to your institution’s operations.

Communication skills are not specifically addressed in the three-tier framework, but it is critically important for institutional research. If you want to be truly effective in institutional research and want to play an effective role in supporting decisions on campus, you must learn to present your ideas and data insights clearly and effectively to your audiences. Your leaders have a lot of competing priorities. When you have their attention, don’t just give them a data dump. Focus on the key data messages, tell the data stories, and explain your data stories in a way that will impress upon them the key actionable points. I suggest that anyone interested in polishing up their data storytelling skills should check out these books:

Cover of a book with white cover and blue charts and diagrams on it.
Cover of a book with blue cover and white charts and diagrams on it.
Cover of a book with a frog wearing a crown and leaning on a golden egg.
Cover of a book with white cover and blue charts and diagrams on it.
Cover of a book with blue cover and white charts and diagrams on it.
Cover of a book with a frog wearing a crown and leaning on a golden egg.

4. What do you believe will be the future priorities or emerging areas of interest for institutional research, in particular, since COVID-19?

COVID-19 accelerates the pivot to online education. Universities large and small were forced to take part in a mass experiment of online learning when the pandemic ravaged the world. With this massive exposure, it is expected that online learning models (fully online, blended, hybrid, and hyflex (hybrid-flexible)) will continue to grow. For institutional researchers, this means that we need to evaluate the student success assessment process and predictive algorithms with different learning models in mind. What works in the in-person environment may not work in the online environment, and vice versa. We need to have a better understanding of data generated from the learning management system and differentiate learning behaviours in different learning models or platforms.

Another area of change to come is the increasing use of different pathways to educational achievements. Competency-based learning, micro-credentials, stackable credentials, micro-masters degrees, and experiential transcripts, will add more complexity and much-needed changes to the traditional credit-hour-oriented education model. For institutional researchers, this means that counting degree completions and measuring student outcomes suddenly gets more complicated. We need to welcome such changes and develop the right data capturing and management system to reflect such changes.

Lastly, we are going to see more and more institutions moving to cloud-based enterprise resource planning systems, customer relationship management systems, and data lakes. The cloud platform provides easier expandability and faster response time to organisational needs. For institutional researchers, this means at least two things:

  1. How will cloud-based solutions disrupt your data access, reporting, and quality control practices?
  2. Do you have the right skills to keep up with such changes? For example, do you have a working knowledge of programming languages such as Java, JavaScript, R, and Python. These are the common languages for cloud-based data resources.

5. Complete this statement: In my role, I can’t operate effectively without …

… building a network of collaborators on campus. Without trusting and relying on the participation and professionalism of our colleagues on campus, and without strong support from the senior leadership, the road to analytics maturity will be a steep uphill climb with little hope of success. People are the most important element in the data analytics equation.

 

[1] Terenzini, P (February 1993) ‘On the Nature of Institutional Research and the Knowledge and Skills It Requires’, Research in Higher Education, 34(1), accessed 14 July 2021.

Henry Zheng

August 2021

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