Taking Control of Your Data: HTTP with Accountability (“HTTPA”)

  In the world of Big Data there is increasing concern about privacy. But the conventional wisdom, even among many “experts”, is that privacy is a relic of the past. The thinking goes: “Companies and organizations can make a best effort at securing private information but in the Internet Age a person’s data trail, once it is in external hands, is no longer subject to...

Measuring Education Quality: A First Look at US College Dropout Statistics

Turn on, Tune in, Drop Out In Introduction to College Unbound: The Future of Higher Education and What It Means for Students, Jeffrey J. Selingo opens with Samantha Dietz’s story. A top student in high school, Samantha earns a 3.9 Grade Point Average (GPA), takes Advanced Placemennt and International Baccalaureate courses, and participates in the debate club, Harvard Model Congress, and the student...

Can We Improve Retention Rates by Giving Students Chocolates?

Course Signals’ Retention Claim Course Signals is an early-warning alert system for identifying at-risk students developed at Purdue University and made available commercially by Ellucian. It has been claimed that use of Course Signals “boosts graduation rate by 21 percent”.  The claim is suspect and continues to be repeated without scrutiny.  I wrote a simulation to test the claim. My...

The Rwandan Tragedy: Data Analysis with 7 Lines of Simple Python Code

Rwandan Tragedy
There is a lot of discussion these days about Big Data, Machine Learning Algorithms, and Advanced Statistics. But with this case study I hope to illustrate that you don’t have to be a “Rocket Data Scientist” to take advantage the tools now beginning to be available for data analysis. The available of “open datasets”, when combined with tools such as Python/Pandas can empower...

Smart Education Meets Moneyball (Part I)

Wired Magazine. Innovation Insights John Baker, Desire2Learn, 04.09.13 Smart colleges and universities are beginning to use predictive analytics to transform massive amounts of data into active intelligence, using it to help their customers – i.e., students – learn their course material more effectively and boost their grades. Analytics in education is empowering the learner in every step of their...

The Learning Registry: How to Liberate Learning Resources

How can I discover valuable learning resources? This is still an unsolved problem from technical infrastructure perspective.   Funded by the US Department of Education and led by Dr. Marie Bienkowski, the Learning Registry is an exciting project for “liberating” learning resources from the web.  The Learning Registry is another Learning Object Repository (LOR). Rather, it’s an ambitious...

The Scientific Approach to Teaching

Harvard’s University’s Eric Mazur has been applying the “flipped classroom” concept for two decades. His ALT-C 2012 Keynote is must viewing for educators and anyone interested in analytics in education. I also recommend Garr Reynold’s blog for a review of Mazur’s work.

Data Analysis Comes to Python: The Pandas Library

The giant panda is renowned throughout the world.  Not as well known but fast gaining in popularity is Pandas, a Python-based data library for data analysis. Among programming languages Python has emerged as the language of choice for a broad array of applications in scientific computing. Python is easy to learn and easy to use, yet flexible enough to handle difficult computational tasks. It lends...

Reflections on Lev Gonick’s The Year Ahead in IT, 2013

Each year I look forward to reading Lev Gonick’s “The Year Ahead in IT”.  Gonick is CIO at Case Western Reserve University, a strategic thinker, and deep analyst of ICT trends in higher education. This year’s analysis doesn’t disappoint. I highlight some key themes in Gonick’s article and finish by posing a question. INSTANTANEOUS INFORMATION Gonick cites the...

What is Analytics?

Alfred Essa
Analytics can be defined in various ways. In this presentation I suggest that there are three levels of Analytics capability or maturity: Analytics I is data about the Past and, at best, data about the Present. Analytics I is the realm of traditional reporting and traditional Business Intelligence Analytics II is data about the Future. Analytics II is the realm of Predictive Analytics and Forecasting. Analytics...