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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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Higher Education on a Flatter Earth

We’re collaborating on some educational work and came across this sentence in a textbook on finance and accounting:

  • All of these are potentially good economic decisions.

We use statistical NLP but assist with the ambiguities.  In doing this, we relate questions and answers and explanations to the text.

We also extract the terminology and produce a rich lexicalized ontology of the subject matter for pedagogical uses, assessment, and adaptive learning.

Here’s one that just struck me as interesting.  This is a case where the choice looks like it won’t matter much either way, but …

Continue reading “Higher Education on a Flatter Earth”

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Artificially Intelligent Educational Technology

Over the last two years, machines have demonstrated their ability to read, listen, and understand English well enough to beat the best at Jeopardy!, answer questions via iPhone, and earn college credit on college advanced placement exams.  Today, Google, Microsoft and others are rushing to respond to IBM and Apple with ever more competent artificially intelligent systems that answer questions and support decisions.

What do such developments suggest for the future of education? Continue reading “Artificially Intelligent Educational Technology”

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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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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”