Brandon Davis, PhD’s Post

DATA                                              DATA                                                DATA The most important variable in overseeing a corporate structure is the collection and management of data. As important are the many other corporate variables, e.g. marketing, all such variables depend on the most significant factor of all—data. In developing my global education network (RAPT GLOBAL MOBILE), formulating the data interface is the first priority.  By comparison, of all the corporate structures in the world, educational institutions provide for their consuming students the least effective interface with the metrics of data. At least 3 major student-consumer demands can be met with solid data analysis: 1) Achievement in Course Studies; 2) Vocational Search/Post Secondary Education; & 3) Emotional Guidance. While schools have traditionally made attempts to provide the latter 3 student-consumer demands, it is felt that the rendering of such learning opportunities can be offered in a more technologically dynamic and compelling student-consumer format.

PSA: Be a critical reader of reports on the ROI or value of higher education and earnings and employment of graduates. These reports are coming out faster than many academics/experts' ability to evaluate them. Some are coming from organizations with considerable resources and communications capacity. But that doesn't mean the methodologies have been vetted or authors have extensive experience in higher education. In some cases, the data sources and methodologies are deep in an appendix and hard to parse out, even for experts. It's not the case that I think these organizations are nefarious, though some may have a vested interested in the results. My point is that when you come across a report, don't simply accept the results as Truth. When I see an eye-popping statistic, my first response is to try to understand where it came from. Statistics are not naturally occurring phenomena--they reflect a set of choices by researchers. How are they measuring ROI or conceptualizing underemployment? What assumptions are being made? What are the data sources and data limitations?

"Statistics are not naturally occurring phenomena--they reflect a set of choices by researchers." Well said! So important for everyone to understand this! No matter what kind of data you're looking at - don't ever just read the numbers and move on. You need to know where the numbers came from. Who was asked the questions? What were the questions? Who was left out? What statistical models were used and were they appropriate for the study? I am not a statistical data analyst expert, but I am surprised whenever they are NOT consulted when a group of non-researchers is looking over data and trying to make decisions from it. It's important to get expert interpretation from someone who can speak to the nuances of how the data was collected and how it was cleaned and analyzed. #research #dataquality #treatment #outcomes #quantitativeresearch #stats

In June Gallup performed their annual US Confidence in Institutions survey and released the results two weeks ago. I've posted a link in the Comments to their summary. One disturbing data point is the confidence in Higher Education. In 2015 57% of respondents responded a "Great Deal" or "Quite a Lot" when asked about the level of confidence they had in Higher Education Institutions. In 2019 this number had dropped to 48%, and in 2023 and 2024 now rests at 36%. While 36% may not be as low as some of the institutions in the data set, the rapid drop is troubling. I believe in higher education, but also recognize this as a pivotal moment where action is necessary to reverse this trend. A few ideas are below: 1. Align credentialing with independently verifiable instruments and assessments. (ie: Certification Tests) 2. Ensure that advisory and industry groups influence and are tightly integrated with workforce programming, not as an after thought. (BILT Model) 3. Develop pathways where there are multiple entry and exit points during the educational journey, not all paths need to result in an Associates / Bachelors. 4. Look for ways to ensure application of learning exists across the journey, including general education courses. 5. Remove the things that do not have impact, be real about difficult decisions, and transparent about data. I don't know what the crystal ball holds for next years report, but in it's current state, there isn't much room to wiggle.

Excited to be working on a project as a consultant for a government agency! It's a great opportunity to bridge the gap between university academia and real-world applications. However, there are some challenges we're facing such as data collection from various ministries, departments, and education institutions. Plus, we need upfront capital to run the project since payment is not received at the beginning. And let's not forget the challenge of gathering all parties involved within the scope of the project. But we're up for the challenge and ready to make a positive impact! #consulting #government #university #challenges

College Students Data Cleansing Email: [email protected] https://lnkd.in/d2JmYv6H Optimize your educational databases with our College Students Data Cleansing services. We specialize in rectifying errors and inconsistencies to ensure you have accurate and reliable data. Our Student Information Scrubbing Services transform your databases into clean, actionable sources of information. By utilizing our services, you can enhance communication with students, improve administrative processes, and make informed decisions based on reliable data. Don’t let inaccurate data hold you back; trust us to keep your college student databases in top shape. Contact us: Datacleaningservices.com #collegestudentsdatacleansing #studentinformationscrubbing #datacleaningservices #dataquality #educationaldata #administrativeefficiency #accuratedata #datadriven #databaseoptimization #cleandata

College Students Data Cleansing Email: [email protected] https://buff.ly/4gX7LlZ Optimize your educational databases with our College Students Data Cleansing services. We specialize in rectifying errors and inconsistencies to ensure you have accurate and reliable data. Our Student Information Scrubbing Services transform your databases into clean, actionable sources of information. By utilizing our services, you can enhance communication with students, improve administrative processes, and make informed decisions based on reliable data. Don’t let inaccurate data hold you back; trust us to keep your college student databases in top shape. Contact us: https://buff.ly/3OpCydP #collegestudentsdatacleansing #studentinformationscrubbing #datacleaningservices #dataquality #educationaldata #administrativeefficiency #accuratedata #datadriven #databaseoptimization #cleandata

Thank you to Higher Education Quality Council of Ontario (HEQCO) for their insightful blog post about Ontario's WIL landscape! We at WxL agree that more robust data infrastructure is key to enhancing decision-making around work-integrated learning (WIL). It is crucial to first establish consistent definitions around WIL. Variations in defining WIL experiences can lead to discrepancies in data collection and interpretation. This makes it challenging to draw meaningful conclusions. Referring to standardized definitions, such as those from CEWIL/ECAIT Canada, can provide a stronger foundation for data analysis, enabling informed decisions and effective strategies for enhancing WIL experiences. Clear benchmarks and metrics are also vital for evaluating WIL programs. By identifying key performance indicators and standardizing measurement practices, we can create a more robust framework for assessing program outcomes. This will help identify best practices and set realistic goals for continuous improvement, enhancing the overall quality and impact of WIL experiences. By focusing on the above, we can better guide stakeholders towards more effective and impactful #WIL programs.

1. Case Study #1 To improve the effectiveness of its teaching staff, the administration of a high school offered the opportunity for all teachers to participate in a workshop. They were not required to attend; instead, the administration encouraged teachers to sign up. Of the 43 teachers on staff, 19 chose to take the workshop. At the end of the academic year, the administration collected data on teacher performance for all teachers on staff. The data was collected via student survey. In the survey, students were asked to rank each teacher's effectiveness on a scale of 1 (very poor) to 6 (very good).  The administration compared data on teachers who attended the workshop to data on teachers who did not. The comparison revealed that teachers who attended the workshop had an average score of 4.95, while teachers who did not attend had an average score of 4.22. The administration concluded that the workshop was a success. Consider this scenario:  What are the examples of fair or unfair practices? How could a data analyst correct the unfair practices? Answer: Examples of fair or unfair practices: The analysis might be unfair because it doesn't account for potential differences between teachers who chose to attend the workshop and those who didn't. For example, teachers who were already more motivated or interested in improving their skills may have been more likely to attend, which could have influenced their higher scores. Data Analyst correction of unfair practices: A data analyst could correct this by considering other factors that might have influenced the results, such as the teachers' prior performance, years of experience, or subject taught. They could also conduct a more controlled study where a random selection of teachers is asked to attend the workshop, ensuring a more unbiased comparison of the workshop’s impact. Lateef Fatai Ghuzanfar Abbas Khan #problemsolving #dataanalyst #work #dataanalysis #understanding

Many higher education institutions are grappling with challenges across the entire student lifecycle. With the current slide in enrollment being noted by the U.S. Bureau of Labor Statistics as the steepest on record, operating models are under pressure. This is when Master Data Management can step in and revitalize how higher education operates and ensure better decision making. Learn how: https://hubs.ly/Q02ZmnDG0 #mdm #data #dataplatform #datamanagement #highereducation #studentdata