Textbooks in the Fabric

With the iPad already available in pre-order we are probably all wondering how the battle between Amazon and Apple will shape up in the eBook segment . eTextbooks are an important part of this market due to a captive audience and estimated annual textbook sales of $9B. Last year Amazon put a large-screen version of the Kindle through an eTextbook pilot program at several schools while more recently ScrollMotion entered into a deal with major textbook publishers to develop eTextbooks for the iPad. It seems like we are all pretty much aware that the concept of textbooks as shrink-wrapped, neatly packaged compendiums of knowledge printed on bundles of paper is on its way out.

I personally don’t have a dog in the fight, but I do think that we are due for major changes in the textbook arena. There are many reasons to complain about traditional textbooks (cost, need for a physical supply chain, etc.), but what has always bothered me about them is that the content inside textbooks is physically isolated. We can’t directly connect the words and concepts in a paper textbook to the ecosystem of related information on the web and on our computer. A traditional textbook remains static after it’s been printed, in complete ignorance of all of the opinions and ideas of the multitude of readers and the swirling, ever-changing vortex of knowledge and information around it.

Fortunately, a significant amount of work has been ongoing in the arena of biomedical publishing that could inspire the evolution of textbooks. This field is notorious for information overload, with large numbers of research papers published every year. Large volumes of data from high throughput experiments, frequent term ambiguity (many proteins have been given multiple names), and multiple formats for results (images, 3D protein structures, chemical structures, systems models and more) only complicate the picture. This blog post by Abhishek Tiwari describes some of the halting progress in scientific publishing that seeks to address this issue. It also led me to this paper in PLoS Computational Biology which describes a prototype scientific paper of the future, and Elsevier’s Article 2.0 Contest. I was inspired to summarize some of the concepts I read that could address my chief complaint with traditional textbooks:

  • Connections to the data –We want raw data so that we can do new things with it – analyze it, visualize it or mash it up with other data (echoing Tim Berners-Lee call for Raw Data Now). The next generation of eTextbooks could make the data behind charts and graphs available so that we can feed it into a platform like ManyEyes, Tableau Public or Swivel or do our own thing with it (The PLoS paper includes a nice demo of this, where author provided data was fed into the Google Maps API to create a geographic visualization). We also want to be able to get different or more recent versions of the data and visualize it alongside the author’s analysis. The issue of authors providing raw data in scholarly publications is currently a hot topic (discussed here, here and here) – perhaps a new generation of students who expect data to be made available as the norm will create the demand that spurs change.
  • Collaborative features – A project to make textbooks editable has actually been around since 2003 – the Wikibooks project – but a quick perusal shows a rather limited selection of textbooks and uneven quality. So it is safe to say that at this point community-written textbooks are not going to play a big role in higher education. But the community can still contribute. Let us tag, rate, vote and comment on chapters, paragraphs and even sentences. Readers can choose to view user contributed content or ignore it, and authors could use user contributed content to improve their textbooks in real time. The open source sBook project includes these features, though at this point it appears a bit clunky and doesn’t seem tailored for handheld devices.
  • Embedded structured content – This could be anything from semantic markup (such as identifiers that link back to community databases) or XML (like MathML) that could be loaded directly into specific programs. This feature can makes searching, getting auxiliary information and summarization of content much easier.
  • Connections to the sources – Images, quotes, statements and data pulled from other sources should link directly to the sources they were pulled from. Referenced content can be shown in a preview to avoid breaking up the reading experience. A winning entry in Elsevier’s Grand Challenge applied NLP  to identify the most relevant passage in a reference.

Textbooks that are made available as assemblies of independent, re-purposable units of information will hopefully emerge in the near future, and through individuals applying their own creativity will transform the process of learning.

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