Showing posts with label plenary talk. Show all posts
Showing posts with label plenary talk. Show all posts

Friday, October 14, 2016

Conference Attendance Teaching and Learning Summit 2016 - Keynote

Critical Thinking: Why is it so hard to teach? - Dr Daniel T. Willingham

Critical thinking is intertwined with content knowledge.  We saw a sequence of four examples (If vowel then even number, if alcohol then 21, if gin, then haddock, if entering then cholera vaccine), for each example, there is a claim about a set of cards: If X then Y.  Given four cards, verify the claim.  If the problems were formulated based on permissions, then the success rate was high.  Each problem is technically, P -> Q, but having just completed a semester of logic has no impact on results.

Scientific reasoning is taught in two pieces scientific concepts and scientific method.  So consider designing a learning experiment.  The group is split into intervention and control.  How do you know that the random sample is valid?  Background knowledge is required to determine the appropriateness of the split.

Critical thinking occurs from learning at the deep level.  The surface story is say, "tumors and rays".  The deep question is whether it is modus pones, Netwon's third law, etc?  However, memory is focused on the surface facts.  Recall is based on those components.

Why not teach the deep structure immediately?  Abstractions are hard to understand.  Instead, learners have to see lots of surface structures all overlaying the same deep structure.

Sometimes failures in critical thinking are actually failures in basic knowledge.  Furthermore, there are also innate biases, such as words refer to objects and attributes, and the world is full of agents and purposes.

Takeaway 1: Most of critical thinking is domain-specific.
Takeaway 2: In each domain, faculty should identify what they consider the important critical thinking skills.
Takeaway 3: Select content with an eye toward teaching these skills.  Teach the critical thinking in the context of the content.
Takeaway 4: Critical thinking is a curricular issue.  These skills require more than 1 semester to acquire.
Takeaway 5: Certain foundational concepts may run counter to the mind's biases.  Students have functional knowledge that has worked so far.  For example, "equals sign means put answer here".

Q. Translating domain skills in interdisciplinary work?
A. Don't know.  Probably needing to know enough of the skills in the home domain to be able explore the other domain.

Q. If critical thinking is domain specific, how specific are domains?
A. Domains are nested.  Proper application requires domain knowledge.  Moving from cognitive psychology to social leaves [the speaker] less skilled, but still better than average.  Into clinical psychology, they have a common basis, but limited ability to apply.

Wednesday, June 17, 2015

Conference Attendance FCRC - Day 5 - Plenary Summary

Plenary Talk today, which pulls together all of the conference attendees.  Sunday's talk was based in databases, with Michael Stonebraker speaking on his Turing-award winning work.  Monday's talk discussed interdisciplinary work, primarily centered in CS theory, and was given by Andrew Yao (a prior Turing Award winner).  On Tuesday, Olivier Temam discussed neural networks in hardware, which focused on his work and efforts to better model or mimic the capabilities of the brain.

The F# Path to Relaxation -
There are opportunities to introduce new work toward relaxing and improving.  Or perhaps create opposing camps.  Thesis <-> Antithesis ==> synthesis.  Or Functional <=> Interop.  Back in 2003, functional languages were isolated, non-interoperable, using their own VMs.  F# (along with Scala, Swift, ...) instead seeks to have an exosystem, being the external industry-standard runtimes.  Another tension is between Enterprise and Openness.  So F# is open and cross-platform.  Tools are available for Android and iOS, as well as packages for Linux.

Functional <=> Objects
Thus embrace objects, without being object-oriented.  Some cases in the cross-product of the expected features for objects and functions requires particular care for synthesis.

Circularities and Modularity in the Wild
Lambdas, generics, etc are clearly being embraced in modern language design.  However, circular type dependencies are unfortunately also widely present.  Languages need to enforce acyclicity.

Pattern Matching <=> Abstraction
How does the language support the functional concept of pattern matching, when you want to include type abstraction?  Alas, the speaker skipped the solution quickly.

Code <=> Data
Most development is to providing tools for the information revolution.  There is exponential growth in Open APIs for accessing data from the internet.  This data then comes with dynamic types, where the types are only known once the data (or schema) has been accessed.  The type creation can also enable blending code for other languages into the F# environment.  For example, the support can allow opening csv or json files and having types for the data.  This feature is, by far, the most exciting and interesting of the presentation.  Not quite worth the price of admission, but clearly a great development.

Applied PL design comes from the synthesis at the heart of these contradictions.  This tension also is part of the proliferation of languages.