Algorithmic Processes that Help You Interpret Data Like a Pro
Got a problem in the classroom with instruction, learning, or behavior? Today’s quick answer is to solve it with an algorithm.
However, not everyone feels comfortable enough with algorithms to sketch out a few quick equations, predict runtime, check for accuracy, and then break everything down in manageable bites.
The average person doesn’t have the knowledge – or inclination – to write the kinds of complex algorithms that reveal academic and behavior challenges and successes.
Most people recognize that algorithms determine which ads appear on social media, which stories are fake news, and even whether they get picked for a promotional special. Algorithms also determine school enrollment, grade level placement and personalized learning recommendations for students. An algorithm is a tool for logically solving a problem.
Many people, however, don’t recognize that there are a variety of algorithms, each with different purposes and weakness. Know what each algorithm is capable of, and you’ll know how to interpret the data it gathers.
Predict and summarize student achievement
Algorithms also can identify scenarios before they become problems. Using continuous and discrete variables, an algorithm uses summary statistics to search for the distribution of responses and correlations between instructional interventions. Programmers write algorithms to produce the kinds of descriptive statistics that tell the mean, median, and mode, along with minimum and maximum values.
These numbers tell educators the average score on a test, highest and lowest scores, and which questions were missed the most. Teachers rely on these data sets to understand how a class performs over time and against other classes in the same school, district, and state.
The algorithms, however, don’t always tell the whole picture.
No educator should place complete trust in the algorithms behind summary statistics. They may hide or ignore other variables influencing achievement, and they may harbor bias. Summarization algorithms are most valuable when combined with other formulas written to give a broader picture.
Embedding recursive algorithms for better data management
Believe it or not, you can have too much data.
Nearly every teacher gets it. Vast quantities of data can be overwhelming, and Information overload can prevent them from doing their jobs. When that happens, it’s time to break down the problems into smaller pieces of information.
Resorting to sub-problems allows algorithm programmers to solve challenges on a smaller level by looking at a base condition and one recursive case. Working with smaller data sets can help teachers apply data analysis to larger sets and populations.
One can infer that what works with one subset may work with another. Be aware, though, that bias in a particular subset can skew any comparisons to second or third subsets.
Proceed with caution
It’s easy to suggest that all you need is an algorithm to solve your problems. Knowing which algorithms you need in each situation can make all the difference in getting the job done. You must also recognize the shortcomings of algorithms.
Fortunately, most teachers don’t need to spend their time writing algorithms. In an effort to improve instruction, fine-tune assessments, and streamline grading processes, edtech companies have already done that work for them.
The job isn’t done, though. Teachers should question the accuracy of the algorithms in the software they use. It’s wise to be cautious when relying exclusively on algorithmic conclusions without questioning the accuracy of the results.