Blog
-
PeopleAdmin A PowerSchool Company
Professional development is a huge factor in retention, but getting faculty and staff to engage with professional learning can be tough.
Creating professional development learning communities (PLCs) within higher education institutions can lead to increased employee engagement, improved retention, enhanced collaboration, and personalized learning. Below, check out a few strategies to help your institution create effective PLCs that faculty and staff are eager to be a part of:
Build Strong Relationships and Encourage Reflective Practices
PLCs are designed to build stronger relationships between team members through regular meetings and a shared commitment to student learning. These communities encourage reflection on instructional practices and student progress, which can lead to a more cohesive and collaborative environment.
Establish Clear Communication and Social Presence
Effective communication is the backbone of any community. In an online setting, it’s crucial to create a plan for communication that includes real-time meetings and opportunities for information and expertise sharing. This helps in establishing a social presence and a sense of belonging among members.
Engage Through Shared Goals and Interests
Research suggests that participation in learning communities is more related to student engagement than to educational outcomes.
Therefore, focusing on shared goals and interests can encourage a sense of belonging and commitment among faculty and staff, which is essential for retention and engagement.
Emphasize Collaborative Learning and Professional Growth
Learning communities should emphasize collaborative partnerships between students, faculty, and staff. They provide opportunities for professional growth outside the classroom in a supportive and non-judgmental environment, which can lead to improved teacher satisfaction and lower turnover rates.
Utilize Collaborative Learning Techniques
Incorporate collaborative learning techniques to allow members to share ideas and work on common professional issues. This not only enhances the learning experience but also encourages members to become more invested in their work and the community.
Support Continuous Improvement
PLCs should provide continual improvement opportunities, encouraging ongoing professional development rather than one-time-learning. Continuing growth and learning is crucial for keeping faculty and staff engaged and up-to-date with the latest educational practices.
Prioritize Personalized Learning
Personalized learning within PLCs can cater to the individual needs and interests of faculty and staff, making the community more appealing. By allowing members to pursue their unique professional development paths, PLCs can enhance individual engagement and contribute to the overall success of the community.
Conclusion
Building learning communities that faculty and staff want to join requires a strategic approach that focuses on fostering strong relationships, clear communication, shared goals, collaborative learning, and continuous improvement. By creating an environment that values professional growth and personalized learning, institutions can ensure that their PLCs are engaging and beneficial for all members. If you’re interested in a tool that supports engaged professional development and learning at your institution, check out PeopleAdmin’s software or reach out to our team.
-
First-year student (freshman) migration, 2022
A new approach to freshman migration, which is always a popular post on Higher Ed Data Stories.
If you’re a regular reader, you can go right to the visualization and start interacting with it. And I can’t stress enough: You need to use the controls and click away to get the most from these visualizations.
If you’re new, this post focuses on one of the most interesting data elements in IPEDS: The geographic origins of first-year (freshman) students over time. My data set includes institutions in the 50 states and DC. It includes four-year public and four-year, private not-for-profits that participate in Title IV programs; and it includes traditional institutions using the Carnegie classification (Doctoral, Masters, Baccalaureate, and Special Focus Schools in business, engineering, and art/design.
Data from other institutions is noisy and often unreliable, or (in the case of colleges in Puerto Rico, American Samoa, and other territories, often shows close to 100% of enrollment from that territory.)
Instead of explaining how to interact with these views, I’ve put a text box on the view when appropriate. You won’t break anything by clicking; I promise.
If you use this in your business, I appreciate your support on Buy Me A Coffee to help with web hosting, software, computer, and labor costs. If you are a parent or a high school counselor, just scroll right to the views.
Yes, there are some data problems in every report using IPEDS data, so don’t make any strategic decisions based on what you see here (I corrected Harvard’s 2012 glitch of not reporting anyone from California but 220 students from Arkansas instead, and I see Kenyon 2022 is funky. I only report what’s in the data, folks.)
-
Tuition and Fees at Flagship and Land Grant Universities over time
If you believe you can extract strategy from prior activities, I have something for you to try to make sense of here. This is a long compilation of tuition and fees at America’s Flagship and Land Grant institutions. If you are not quite sure about the distinction between those two types of institutions, you might want to read this first. TLDR: Land Grants were created by an act of congress, and for this purpose, flagships are whoever I say they are. There doesn’t seem to be a clear definition.
Further, for this visualization, I’ve only selected the first group of Land Grants, funded by the Morrill Act of 1862. They tend to be the arch rival of the Flagship, unless, of course, they’re the same institution.
Anyway, today I’m looking at tuition, something you’d think would be pretty simple. But there are at least four ways to measure this: Tuition, of course, but also tuition and required fees, and both are different for residents and nonresidents. Additionally, you can use those variables to create all sorts of interesting variables, like the gap between residents and nonresidents, the ratio of that gap to resident tuition, or even several ways to look at the role “required fees” change the tuition equation. All would be–in a perfect world–driven by strategy. I’m not sure I’d agree that such is the case.
Take a look and see if you agree.
There are five views here, each getting a little more complex. I know people are afraid to interact with these visualizations, but I promise you can’t break anything. So click away.
The first view (using the tabs across the top) compares state resident full-time, first-time, undergraduate tuition and required fees (yellow) to those for nonresidents (red bar). The black line shows the gap ratio. For instance, if resident tuition is $10,000 and nonresident tuition is $30,000, the gap is $20,000, and that is 2x the resident rate. The view defaults to the University of Michigan, but don’t cheat yourself: Us the filter at top left to pick any other school. If you’ve read this blog before, you know why Penn State is showing strange data. It’s not you, it’s IPEDS, so don’t ask.)
The second tab shows four data points explicitly, and more implicitly. This view starts with the University of Montana, but the control lets you change that. On top is resident tuition (purple) and resident tuition and fees (yellow). Notice how the gap between the two varies, suggesting the role of fees in the total cost of attendance. The bottom shows those figures for nonresidents.
The third view looks a little crazy. Choose a value to display at top left, and the visualization will rank all 77 institutions from highest to lowest. Use the control at top right to highlight an institution to put it in a national context. Hover over the dots for details in a popup box. If you want to look at a smaller set of institutions, you can do that, too, using the filters right above the chart. The fourth view is the exact same, but shows the actual values, rather than the rank. As always, hover for details.
Finally, the fifth view is a custom scatter plot: Choose the variable you want on the x-axis and the variable to plot it against on the y-axis. Then use the filters to limit the included institutions. As always, let me know what you find that’s interesting.
-
PeopleAdmin A PowerSchool Company
It’s time to start leveraging data to improve your recruitment strategy.
In today’s competitive higher education landscape, attracting and retaining top talent is more challenging than ever. Fortunately, data can be a powerful tool to inform and improve your recruitment strategy. By leveraging data analytics, HR professionals in higher education can make more informed decisions, target the right candidates, and ultimately, enhance the quality of their hires. In this blog post, we will explore how data can be used to refine recruitment strategies in the higher education sector.
The Power of Data in Recruitment
Data analytics can provide valuable insights into the effectiveness of your recruitment efforts. By analyzing metrics such as candidate sources, application-to-hire ratios, and time-to-fill, HR professionals can identify which channels and methods are most successful in attracting qualified candidates. This information can help in allocating resources more effectively and focusing on the most productive recruitment strategies.
Utilizing Predictive Analytics
Predictive analytics can be particularly valuable in higher education recruitment. By analyzing historical data on successful hires, predictive models can be used to identify the characteristics and qualifications that are most likely to lead to a successful hire. This can help in creating more targeted job descriptions, screening criteria, and interview questions, leading to a more efficient and effective recruitment process.
Enhancing Diversity and Inclusion
Data can also play a crucial role in promoting diversity and inclusion in recruitment. By tracking and analyzing demographic data throughout the recruitment process, HR professionals can identify potential biases and disparities. This information can be used to implement targeted strategies to attract a more diverse pool of candidates and ensure a fair and inclusive recruitment process.
Leveraging Technology
In the digital age, there is no shortage of tools and technologies to help HR professionals collect and analyze recruitment data. Applicant tracking systems, job board analytics, and candidate relationship management platforms are just a few examples of the technologies that can provide valuable data insights. By leveraging these tools, HR professionals can make more data-driven decisions and continuously refine their recruitment strategies.
Final Thoughts
In conclusion, data can be a game-changer in the higher education recruitment landscape. By harnessing the power of data analytics and predictive models, HR professionals can make more informed, efficient, and inclusive recruitment decisions. As the competition for top talent continues to intensify, those who embrace data-driven recruitment strategies will be best positioned to attract and retain the best and brightest in their fields.