Serendipity35

As I write this post, Prezi is bragging they have over 50 million users and lots of higher education presentations. I'm still not a Prezi user and I've seen presentations using it that made me dizzy with all the movement. But I understand why people are looking to get out of PowerPoint which is so often criticized.



Honestly, I think the criticism of almost all bad PowerPoint presentations should be directed at its creator and/or the presenter rather than the software. "Death by PowerPoint" doesn't occur because of the program.



Web apps like Haiku Deck and Canva are getting some attention now and some people say this is the beginning of the end of PowerPoint as the main tool for slidedecks. Apple has always tried to make their Keynote program the other choice, but it was initially limited to Mac users. They have introduced other app versions and an iCloud version for the web.

Microsoft has also recently launched its Prezi competitor called Sway.

Do you remember the early days of PowerPoint? Did you know that it was originally designed for the Macintosh computer? The initial release was called "Presenter", but in 1987 it was renamed to "PowerPoint" due to problems with trademarks.

The idea of slides comes from what the program was designed to replace - 35 mm photo slides.

Back then, and still today, many of the best presentations using slidedecks focus on images rather than slides full of text.

When I started working at NJIT in 2000, professors were still bringing 35 mm slides to media services to be converted to .jpgs so that they could use them in PowerPoint. As you might imagine, the College of Architecture and Design had many tray of beautiful slides that they used in lectures.

There are plenty of online articles, tutorials and posts about how to make a good presentation, but I don't think that PowerPoint (or some web or app version of it) is going away.

That old phrase GIGO (Garbage in, garbage out) that came from computer science applies to presentations too. Input bad data ("garbage in") and produce bad  output ("garbage out"). Just add the presenter to the GIGO mix.

 

Less than 30% of tech jobs are held by women, and that number is even smaller for leadership positions.

6 takeaways from smartblogs.com/education/

1. Many tech companies are working hard to improve the industry’s gender gap, releasing diversity numbers to the public and launching hiring initiatives geared specifically toward women. But for real change to happen, it needs to start earlier – specifically, in STEM education.


2. Current trends suggest that more women are studying STEM now than ever before – in fact, in 2010, women represented 50.3% of all science and engineering bachelor’s degrees. But there’s still a long way to go when it comes to getting – and keeping – women interested in tech.


3. A culture that is far friendlier to men than it is to women and a glass ceiling that’s worse than in almost any other industry. According to a recent study by Penn, Schoen and Berland, nearly two-thirds of teens have never considered a career in engineering. Another study by the Girl Scouts of America revealed that only 13% of female teens say that a career in STEM would be their first choice. The reason? They’re not as interested by technology as their male counterparts, and they don’t see the benefits of getting involved.


4. Historical efforts to get women and girls more involved with technology have been focused on making it easier. But going forward, improving gender diversity in technology won’t only be about making STEM more accessible for girls and women. It will need to be about making it more interesting, too.


5. Experts agree that one of the most important factors to getting girls interested in STEM is doing it at a young age. From the toys and games they play with to the guidance they receive in grade school, early actions and choices have a surprising effect on girls’ educational and career paths later in life. 


6. In grades K-12, girls take high-level math and science courses at similar rates as their male peers – and they perform well in them. However, those numbers drop off dramatically the undergraduate level – particularly in the fields of math, computer science and engineering. Colleges and universities are going to have to work hard to get and retain more women in STEM classes. One way Harvey Mudd is trying to improve its numbers? Offering more introductory computer science courses and hosting events and conferences for women in tech. And it’s worked – 40% of Harvey Mudd’s computer science majors are women, far more than at any other co-ed school.

Tracy, Hepburn and EMERAC in DESK SET, 1957

I watched the 1959 film Desk Set over the holiday break. It is set within the TV network FBN, Federal Broadcasting Network (the exterior shots were done at Rockefeller Center, headquarters of NBC). Bunny Watson (Katharine Hepburn) is in charge of its reference library, which is responsible for researching and answering questions on almost any topic. With a secret merger pending, and anticipating a lot more demand for the department, the network boss has ordered two new computers.



Of course, this being 1959, the computers are called "electronic brains" in the film and they are huge. Richard Sumner (Spencer Tracy) is the inventor of them and they are called EMERAC. That name is some wordplay from ENIAC - the Electronic Numerical Integrator And Computer that was the first electronic general-purpose computer.



I also saw a new film over the break - The Imitation Game based on the book Alan Turing: The Enigma. The ENIAC computer was considered to be "Turing-complete" - a term from the work of Alan Turing. In the book and film, set during WWII, Turing is trying to crack the German Enigma code and in the course of doing that, saves the Allies from the Nazis, and sort of invents the computer and artificial intelligence.



The Spencer Tracy character in the 1959 film was also trying to create a digital way of solving problems. They describe him as being an "efficiency expert" which was a new and big concern in the 1950s.



Today, predictive analytics has become a big topic in educational technology and I have written a number of posts about its use in education. It is a way of using statistics, modeling and data mining to analyze current and historical facts in order to make predictions about future events. An example of one of the desired educational uses is to monitor at-risk students and allow interventions at the proper times.

 

Data analytics in higher education is still in its early years and the terms have changed over the past few years as the use of the term "big data" has replaced "data mining" in popular conversations. Where I was once reading articles about using "descriptive analytics" - the analysis of historical data to understand what has happened in the past - now I'm more likely to find articles on "predictive analytics" - using historical data to develop models for helping to predict the future.



Prescriptive analytics takes those predictions and goes to the next step of prescribing recommendations or actions to influence what happens in the future.



Confused? As an example, using big data and descriptive analytics about students and any particular student, we might predict the student's performance and problems in the current semester and then using a prescriptive analytics-driven learning management system we could recommend additional material, resources online or even notify on-campus people and departments to interact with the student early on.



Prescriptive analytics seems best-suited for educational problems like student retention, enrollment management, prospect analysis, improving learning outcomes and curricular planning. These are all problems that can be addressed with data analytics because there is adequate high-quality data to analyze those problem.



Did you read Moneyball or see the film version of Michael Lewis' popular book? It can be viewed as the story of the power of predictive analytics as he describes baseball's Oakland A’s team manager working with the lowest team budget in Major League Baseball and using predictive analytics techniques to turn around his team’s performance.



What schools need to do is similar to the "business rules" that companies formulate with input from various stakeholders in the organization.



Sometimes the data may produce results that are open to interpretation and campus experts need to be involved. In an article, "Prescriptive Analytics for Student Success," it is pointed out that student data now includes that generated by mobile device usage, campus cards, social media and sensor technologies. It presents an interesting case of an on-campus student who is not using dining services as often as before. Does that mean the student isn't as active socially on campus? Is she depressed? Can we add to the data class attendance or even clicker use? Is she more likely to drop out?



These "alternative data sources" are still emerging and may cross over into FERPA and privacy concerns about what is permissible in data collection. 



Learning analytics is another term used and seems to apply more to using learner-produced data and analysis models to discover information and social connections for predicting and advising people's learning. This area sounds like it would appeal more to teachers than the earlier examples, but in some ways there is a lot of crossover in studying individual learners. The data might allow the learner to reflect on their achievements and patterns of behavior in relation to their peers. It can warn them of topics or courses requiring extra support and attention. It can help teachers and support staff plan interventions with individuals and groups.



For departments and institutions, it can help improve current courses, help develop new offerings and develop marketing and recruitment strategies.



Predictive analytics is a big field and one that seems to strike fear into teachers much like those computers in Desk Set did more than 50 years ago. It encompasses statistical techniques from modeling, machine learning, and data mining. Do we trust the predictions about future, or otherwise unknown, events?



This is being done in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields, but many educators (and I confess to still being one) are hesitant about moving business models into education.



You may be able to accurately do credit scoring (as in the FICO score) in order to rank-order individuals by their likelihood of making future credit payments on time, but can we really predict how a student will do next semester in Biology 102?



Nate Silver gained a lot of attention with his blog and books, especially during the last presidential election, showing that predictive analytics pays big dividends in politics, sports and business.



Companies such as Google, Twitter and Netflix are hiring predictive analytics professionals to mine consumer behavior and they are in the front of the office rather than crunching numbers behind the scenes.



In higher education, student retention is such a big concern that colleges find success using data analytics, it will quickly find its way from administrative tasks and into classrooms.



 

A few other sites have posted this graph of the 20 most popular web sites every year since 1996, but I think it's interesting enough to pass along.

1996-2000  This section of the graph is the original dot-com boom era.

AOL really dominated at the start of the century. 

Looking at 2009-2013, the data (from comScore) shows the top five continues to be Google, Yahoo, Microsoft, Facebook and AOL, with Apple, LinkedIn and other mixed in with some of the "old media" companies.

see the full chart