What is it that’s so appealing about the software programs and apps teachers provide to their students? Some say it’s the engaging presentation; others cite ease of use. 

Perhaps the reason so many teachers rely on edtech software is that it individualizes instruction. As a result, teachers save time preparing differentiated lessons, and students spend more time on task. The edtech in use in today’s schools is customizable for every type of learner, regardless of the number of users.

By using algorithms, machine learning and artificial intelligence can create the kind of customization necessary for individualized learning. Edtech software companies like Elemental Path and Discovery Education rely on machine learning algorithms to create pathways of learning throughout their software programs. The algorithms determine what interventions students may or may not require.

But have you wondered what goes into those algorithms? 

In its simplest terms, an algorithm is the result of integrating multiple mathematical formulas. However, an algorithm is much more than a series of equations. Algorithms are layers of equations activated into computing a myriad of diverse results based on if/then conditions. 

3 types of algorithms 

Not all algorithms are created equally when it comes to machine learning.

Every algorithm can be categorized according to its purpose. Machine learning falls into one of three learning types: supervised, unsupervised, and reinforcement. Algorithms have well-defined data points (assessment), search for unknown variables (diagnosis), and cue learners (gamification), respectively. 

Programmers work with diversely different equations to teach responsive thinking and how to make appropriate decisions based on specific data sets. 

You can’t get rid of bias

Every Al algorithm has a unique style of its own. This style is sometimes referred to as inductive bias. Because each algorithm is built upon a set of assumptions used to predict the unknown, we can expect that some sort of bias will always exist in machine learning.

Programmers themselves don’t approach problem solving that way. Preference and style can influence their decisions regarding algorithm use. They can also create errors of commission and omission – either accidentally or on purpose.

Even if machine learning could write its own algorithms, these formulas likely would still carry the fingerprint of bias.

Every algorithm is customized 

These is no such thing as a one-size-fits-all algorithm. 

There are many types of algorithms in machine learning, each with a specific purpose.  Although these algorithms may share common traits, each is programmed as a unique formula designed to elicit a predictable outcome based on unpredictable data. 

It’s all in the data 

Algorithms are more than a series of math formulas. Programmers study data sets in order to write algorithms that can accurately solve problems. This requires visualizing outcomes, creating models, assessing accuracy, and interpreting results.

Your computer doesn’t think on its own. Machine learning does the thinking. The operations systems and software you’ve selected for your technology rely on a variety of algorithms that create predictable models. These programs can do everything from check your spelling to predict global temperatures twelve years from now. 

It’s critical that teachers and professors understand the technology they use to solve instructional problems. By learning which algorithms influence your decision-making and how they work, you can better understand the role of machine learning in your classroom and at your campus.