Researchers
May 20, 2020

How Current Data Trends Can be Used to Measure and Increase Academic Transparency and Impact

Big data drives every decision made in academics today. But how those trends are determined depends on the way the data is analyzed. Student achievement and the academic impact of a source is determined by data. If the data is skewed, there is a ripple effect of consequences. Because of this concern, academic transparency is required more than ever in the past, and current data trends show the importance of this clarity.
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Big data analytics and the results are everywhere today. This widely controversial subject is, in short, the process of researching a specific data set, examining the results, and uncovering the information it provides. It’s a multi-billion dollar field of study that guides businesses into making informed decisions, so it’s crucial that the data used is accurate. This “big data” drives everything from marketing to program development, and in the field of education, it sets up a road map to follow that delineates the next line of academic focus and all the lesson plans, textbooks, sources, and testing that should be used to accomplish the goal.

It’s safe to say that big data, in fact, drives every decision made in the world of academics today. The way the data is analyzed determines the next big trend that researches of academia focus on, such as student achievement. The academic impact of a specific source that is focused on is almost solely determined by the data analyzed. But if that data is skewed, the consequences can be widespread and costly. As such, all variables must be visible, transparent, and as controlled as possible when it comes to studying the data and determining the end result. This means academic transparency is a must in order to weed out the factors that could be muddying the results or explain the outliers.

Separating the Wheat From the Chaff with Big Data

Big data is uncovered by professional researchers in what is called “data mining.” These experts spend their working hours determining what data is relevant, which parts are distractors, and which aspects are true. From there, the collected factual data is sent further down the line to attempt to determine further research combinations and how to apply the information in the classroom and beyond. Educators and policymakers can then use this thoroughly analyzed information to drive further improvement in education. The general idea is that by discovering patterns in data, it’s possible to identify and overcome obstacles to learning before they occur.

For this to work, data miners must, in basic terms, separate the wheat from the chaff. They must determine which aspects of the data are valuable and which parts are worthless. Sometimes this involves following the thread of the results all the way back to the implementation to see all the variables involved that led to the final data, then deciding based on predetermined guidelines whether that data was skewed or accurate.

Changes in Data with the Advent of the Digital Era

With the significant impact of technology over the previous decade, it’s not surprising to see that data trends have changed since the entrance of the Digital Era. Data and analytics have changed how researchers approach academics. With easy access to so much metadata, the tide has turned from subject-specific analysis to cross-curricular integrated studies. Entirely new models of teaching have been generated from the results of big data over the past decade.

The challenge then becomes deciding which aspects of the data and analytics mined have value and then putting that data in the right light in front of the organizations that would be able to use it. Because it is so easy to accumulate too much data now, it’s become a commodity in which the results are aggregated and then discounted or used based on not only the necessity of the information but the scholarly expertise of the researchers and the clarity of the results as they are presented.

Correlating Statistical Results and Academic Transparency

When the problem becomes “too much available data,” the next step is in ensuring the data chosen is accurate. Statistical results must be parallel with academic transparency. This then helps to create targeted user profiles for use in the real world.

To ensure the data targeted is accurate, the thread from inception to analysis must be made clear. Although variables are always ambiguous in education, being able to identify them can make the difference between a result being relevant versus worthless. The end result of how much of an academic impact an idea or a source made can best be determined when the steps followed are transparently clear. Teachers should not become automatons for this to occur, though.

Saving Individuality While Increasing Transparency

The question then becomes how to increase transparency without decreasing individuality in the classroom. Instructors may not have the final say on the content they must disseminate to their students, but they do have the choice of how to get the information to the class. Each student is unique and should have differentiated instruction as necessary as much as possible. 

This differentiation, while valuable, is also a variable that skews data results. With academic transparency, the data compiled can be balanced with the original information being analyzed. It’s not as difficult as it seems, as long as the instructors follow a basic format to guide their instruction:

●      Educators use student data to drive lesson planning

●      A common rubric or method of assessment is used across the grade level

●      A focus on each student’s strengths and weaknesses is used for differentiation

●      Student mistakes are addressed and corrected 

●      Educators use goal-setting methods and data to determine if goals are reached 

●      School-level goals are transparent and a plan to reach them is followed

When this roadmap is used to guide instruction, the data collected becomes more accurate. The results can then be used to help students learn how to monitor their own education and move this intrinsic knowledge into the real world. Teachers can use the data to analyze student performance, adapt their instructional strategies, and tailor lessons as necessary.

How Impactio Can Help Get Your Big Data Seen

Big data is important, but it’s also a field bogged down by the competition. Numbers are everywhere and organizations are skeptical of the information they see until it’s proven to be relevant.

When you use Impactio to get your data seen, the results are far-reaching. From the easy templates that generate your information into text and graph to the ability to find and cite scholarly research, Impactio is a professional system that proves to your audience that you know what you’re saying.

To get your big data seen as the crucial information it is, head to Impactio, the program used by experts around the world.

Tags Academic Impact Data analytics Academic Transparency
Jason Collins
Writer
Jason is a writer for many niche brands with experience “bringing stories to life” for both startups and corporate partners.

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