Do you need online teaching ideas and materials? Dialogica was supposed to be a text book, but instead we are adapting it for use in online learning and self-study. It is shared here under a CC BY 4.0 license so you can adapt as needed.
Dialogica (http://dialogi.ca) plays with the idea of learning through a dialogue. A dialogue with the text; a dialogue mediated by the tool; and a dialogue with instructors like us.
Dialogica is made up of a set of tutorials that students should be able to alone or with minimal support. These are Word documents that you (instructors) can edit to suit your teaching and we are adding to them. We have added a gloss of teaching notes. Later we plan to add Spyral notebooks that go into greater detail on technical subjects, including how to program in Spyral.
Dialogica is made available with a CC BY 4.0 license so you can do what you want with it as long as you give us some sort of credit.
Michael Sinatra invited me to a “show and tell” workshop at the new Université de Montréal campus where they have a long data wall. Sinatra is the Director of CRIHN (Centre de recherche interuniversitaire sur les humanitiés numériques) and kindly invited me to show what I am doing with Stéfan Sinclair and to see what others at CRIHN and in France are doing.
I knew the end of Agile was coming when we started using hockey sticks.
From Slashdot I found my way to a good essay on The End of Agile by Kurt Cagle in Forbes.
The Agile Manifesto, like most such screeds, started out as a really good idea. The core principle was simple – you didn’t really need large groups of people working on software projects to get them done. If anything, beyond a certain point extra people just added to the communication impedance and slowed a project down. Many open source projects that did really cool things were done by small development teams of between a couple and twelve people, with the ideal size being about seven.
Cagle points out that certain types of enterprise projects don’t lend themselves to agile development. In a follow up article he provides links to rebuttals and supporting articles including one on Agile and Toxic Masculinity (it turns out there are a lot of sporting/speed talk in agile.) He proposes the Studio model as an alternative and this model is based on how creative works like movies and games get made. There is an emphasis on creative direction and vision.
I wonder how this critique of agile could be adapted to critique agile-inspired management techniques?
Generative Adversarial Networks (GANs) analyze tens of thousands of images, learn from their features, and are trained with the aim to create new images that are undistinguishable from the original data source.
They also point out that many of the same concerns people have about AI art today were voiced about photography in the 19th century. Photography automated the image making business much as AIs are automating other tasks.
Can we use these GANs for other generative scholarship?
The death of a woman hit by a self-driving car highlights an unfolding technological crisis, as code piled on code creates ‘a universe no one fully understands’
The Guardian has a good essay by Andrew Smith about Franken-algorithms: the deadly consequences of unpredictable code. The essay starts with the obvious problems of biased algorithms like those documented by Cathy O’Neil in Weapons of Math Destruction. It then goes further to talk about cases where algorithms are learning on the fly or are so complex that their behaviour becomes unpredictable. An example is high-frequency trading algorithms that trade on the stock market. These algorithmic traders try to outwit each other and learn which leads to unpredictable “flash crashes” when they go rogue.
The problem, he (George Dyson) tells me, is that we’re building systems that are beyond our intellectual means to control. We believe that if a system is deterministic (acting according to fixed rules, this being the definition of an algorithm) it is predictable – and that what is predictable can be controlled. Both assumptions turn out to be wrong.
The good news is that, according to one of the experts consulted this could lead to “a golden age for philosophy” as we try to sort out the ethics of these autonomous systems.
Emulation as a strategy for digital preservation is about to become an accepted technology for memory institutions as a method for coping a large variety of complex digital objects. Hence, the demand for ready-made and especially easy-to-use emulation services will grow. In order to provide user-friendly emulation services a scalable, distributed system model is required to be run on heterogeneous Grid or Cluster infrastructure.
The Emulation-as-a-Service architecture simplifies access to preserved digital assets allowing end users to interact with the original environments running on different emulators. Ready-made emulation components provide a flexible web service API allowing for development of individual and tailored digital preservation workflows.
Having just finished teaching a course on Big Data and Text Analysis where I taught students Python I can appreciate a well written tutorial on Python. Python Programming for the Humanities by Folgert Karsdorpis a great tutorial for humanists new to programming that takes the form of a series of Jupyter notebooks that students can download. As the tutorials are notebooks, if students have set up Python on their computers then they can use the tutorials interactively. Karsdorp has done a nice job of weaving in cells where the student has to code and Quizes which reinforce the materials which strikes me as an excellent use of the IPython notebook model.
Text Analysis with Topic Models for the Humanities and Social Sciences (TAToM) consists of a series of tutorials covering basic procedures in quantitative text analysis. The tutorials cover the preparation of a text corpus for analysis and the exploration of a collection of texts using topic models and machine learning.
Stéfan Sinclair and I (mostly Stéfan) have also produced a textbook for teaching programming to humanists called The Art of Literary Text Analysis. These tutorials are also written as Jupyter notebooks so you can download them and play with them.
I’ve been playing with DataCamp‘s Python lessons and they are quite good. Python is taught in the context of data analysis rather than the turtle drawing of How to Think Like a Computer Scientist. They have a nice mix of video tutorials and then exercises where you get a tripartite screen (see above.) You have an explanation and instructions on the left, a short script to fill in on the upper-right and interactive python shell where you can try stuff below.
An article about authorship attribution led me to this nice site on Common Errors in English Usage. The site is for a book with that title, but the author Paul Brians has organized all the errors into a hypertext here. For example, here is the entry on why you shouldn’t use enjoy to.