# Using Computer Science Principles for Decision Making with Design-Oriented Theory

I came across Brian Christian’s and Tom Griffiths’ book entitled, Algorithms to Live By this past summer. I typically don’t read books about math and computer science, but I decided to stretch myself and read books that are outside of my comfort zone while relaxing by the pool.

Brian Christian and Tom Griffiths submit that “algorithms are a finite sequence of steps used to solve a problem” (p. 7). Christian and Griffiths definition got me to thinking, what algorithm can teachers use to solve the problem of personalizing learning for each student within a web-enhanced classroom?

I’m not a mathematician, nor statistician, nonetheless, after reading Algorithms to live by, I started to understand why mathematicians say that math is all around us. Essentially, algorithms help us to solve problems. Hence, how can we use algorithms to help us solve the problem of personalizing learning for all students within a web-enhanced classroom? What would that algorithm even look like? See my shameless attempt below.

I’m sure that my attempt at writing an algorithm that represents my problem is not the correct way, nonetheless, I still believe that I am on the right track regarding the usage of algorithms in instructional-design theory.  In any case, as I got deeper into Christian’s and Griffiths’ book, I realized that connections could be made between computer science principles and instructional-design theories for web-enhanced classrooms.

Teachers in a web-enhanced classroom using principles of instructional-design theory should consider employing the following principles from computer science for instructional decision making:

• The optimal stopping problem – knowing when to stop analysis of information.
• Sorting – organization of information; making order.
• Caching – storage of information; maintaining direct access to the most needed information.
• Scheduling – knowing how much time should be allocated to a given task, first things first.
• Bayes’s Rule – updating a belief about a hypothesis in light of new evidence, forecasting.
• Overfitting – a statistical model that describes random error or noise within the data instead of an underlying relationship.
• Relaxation – A relaxation is an approximation of a difficult problem by a nearby problem that is easier to solve. A solution of the relaxed problem provides information about the original problem.
• Randomness – The lack of pattern or predictability in events. A random sequence of events, symbols or steps has no order and does not follow an intelligible pattern or combination.
• Networking – making connections between sources of information.
• Game Theory – a branch of mathematics concerned with the analysis of strategies for dealing with competitive situations where the outcome of a user’s choice of action depends critically on the actions of other users.

There is much to ponder here, and I will continue to grapple with this issue as I document this journey of helping teachers personalize learning for students within their web-enhanced classroom.