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Automatic Knowledge Graphs for Assessment Items and Learning Objects

As I mentioned in this post, we’re having fun layering questions and answers with explanations on top of electronic textbook content.

The basic idea is to couple a graph structure of questions, answers, and explanations into the text using semantics.  The trick is to do that well and automatically enough that we can deliver effective adaptive learning support.  This is analogous to the knowledge graph that users of Knewton‘s API create for their content.  The difference is that we get the graph from the content, including the “assessment items” (that’s what educators call questions, among other things).  Essentially, we parse the content, including the assessment items (i.e., the questions and each of their answers and explanations).   The result of this parsing is, as we’ve described elsewhere, precise lexical, syntactic, semantic, and logic understanding of each sentence in the content.  But we don’t have to go nearly that far to exceed the state of the art here. Continue reading “Automatic Knowledge Graphs for Assessment Items and Learning Objects”

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US News & World Report: “The Education-Technology Revolution Is Coming”

This US News & World Report opinion is on the right track about the macro trend towards increasingly technology-enabled education:

But it also sounds like what I heard during the dot-com boom of the 1990s when a lot of companies—including Blackboard—began using technology to “disrupt” the education status quo. Since then we’ve made some important progress, but in many ways the classroom still looks the same as it did 100 years ago. So what’s different this time? Is all the talk just hype? Or are we really starting to see the beginnings of major change? I believe we are.

The comments about active learning are particularly on-target.  Delivering a textbook electronically or a course on-line is hardly the point. For example, textbooks and courses that understand their subject matter well enough to ask appropriate questions and that can explain the answers, assess the learner’s comprehension, guide them through the subject matter and accommodate their learning style dynamically are where the action will be soon enough.  This is not at all far-fetched or years off.  Look at Watson and some of these links to see how imminent such educational technology could be!

  1. Award-winning video of Inquire: An Intelligent Textbook
  2. Presentation  of Vulcan’s Digital Aristotle (PDF slides, streaming recording)
  3. article on Vulcan’s Digital Aristotle, Aura, Inquire, and Campbell’s Biology (PDF)

We’ve been working for several years on applications of artificial intelligence in education, as in Project Sherlock and this presentation. Please get in touch if you’re interested in advancing education along such lines.

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Neat vs. Scruffy and Watson

Recently, John Sowa has commented on LinkedIn or in correspondence with some of us at Coherent Knowledge Systems on the old adage due to Shanks concerning the Neats. vs. the Scruffies.  The Neats want nice formal logics as the basis of artificial intelligence.  This includes anyone who prefers classical logic (e.g., Common Logic, RIF-BLD, or SBVR) or standard ontologies (e.g., OWL-DL) for representing knowledge and reasoning with it.  The Scruffies may use well-defined technology, but are not constrained by it.  They’ll do whatever they think works, now, whether or not it is a good long term solution and despite its shortcomings, as long as it can obtain immediate objectives.

Watson is scruffy.  It doesn’t try to understand or formally represent knowledge.  It combines a lot of effective technologies into an evidentiary framework that allows it to effectively “guess”.

Today, in response to continued discussion in the Natural Language Processing group on LinkedIn under the topic “This is Watson”, I’m posting the following presentation on Project Sherlock and the Linguist vs. Google and IBM.

Essentially, the neat approach is more viable today than ever.  So, chalk one up for the neats, including Dr. Sowa and Menno Mofait’s comment in that discussion.

During a presentation at CMU after winning the game show,, IBM admitted that in order to get the last leg of improvement needed to win Jeopardy!, they needed to do some “neat” ontological knowledge acquisition, too!

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Pedagogical applications of proofs of answers to questions

In Vulcan’s Project Halo, we developed means of extracting the structure of logical proofs that answer advanced placement (AP) questions in biology.  For example, the following shows a proof that separation of chromatids occurs during prophase.

textual explanation of entailment using the Linguist and SILK

This explanation was generated using capabilities of SILK built on those described in A SILK Graphical UI for Defeasible Reasoning, with a Biology Causal Process Example.  That paper gives more details on how the proof structures of questions answered in Project Sherlock are available for enhancing the suggested questions of Inquire (which is described in this post, which includes further references).  SILK justifications are produced using a number of higher-order axioms expressed using Flora‘s higher-order logic syntax, HiLog.  These meta rules determine which logical axioms can or do result in a literal.  (A literal is an positive or negative atomic formula, such as a fact, which can be true, false, or unknown.  Something is unknown if it is not proven as true or false.  For more details, you can read about the well-founded semantics, which is supported by XSB. Flora is implemented in XSB.)

Now how does all this relate to pedagogy in future derivatives of electronic learning software or textbooks, such as Inquire?

Well, here’s a use case: Continue reading “Pedagogical applications of proofs of answers to questions”