Campus Desk :
Learning styles fall on a spectrum, with theoretical, concept-driven style on the one end and concrete, data-driven style on the other. The contrast between teachers’ conceptual and data-driven learning styles grows larger as you progress through high school and into college, since professors often spend a lifetime developing one of the two teaching styles. High schoolers should begin paying attention to the distinction between these two styles now in order to get the most out of college classes later. It is crucial to learn which style you prefer.
Exploring the style spectrum
To get a better idea of what each of the two learning styles looks like, think about it this way: The conceptual learner enjoys understanding ideas in the abstract, where he or she can analyze them and sniff out inconsistencies. If you are conceptually driven, handouts, Powerpoints, and precise definitions probably do not concern you that much since you are more interested in the overall flow of an argument or how an idea evolves over time. The data-driven learner, on the other hand, thrives on specificity. If your learning style is data-powered, you seek out exact formulations of ideas to use as conclusive definitions. You gravitate toward Powerpoint presentations and tables that categorize information succinctly and definitively.
Which style fits you?
Now, identifying where you fall on the conceptual vs. data-driven spectrum is crucial. Think about your current teachers and place them somewhere on the spectrum; then consider whether their styles add to or detract from your appreciation of them. Once you’ve ascertained where on the spectrum you are most comfortable, do two things. First, seek out classes with professors and teachers whose teaching style places them near you on the conceptual/data-driven spectrum. Second, if you find yourself in a class whose teaching style is on the opposite end from your learning style, learn to manipulate the learning materials you receive to make them match your style. If your teacher uses a conceptual style but you are a data-driven learner, for example, boil his abstract concepts down to flash card-sized bites and study the key terms and ideas that way.
A tale of two professors
For a real-world explication of the conceptual/data-driven spectrum, consider two of the philosophy professors at my alma mater whose teaching styles fall on opposite ends of the concept-data divide. One professor is very unstructured, with lectures driven primarily by students’ questions. His lectures ramble all over the place, but they are always interesting. Some students find his approach annoying: he rarely formulates his ideas concisely or exactly, and it is difficult to prepare stock answers for his tests. Others, however, seek out his classes because they appreciate the nuance with which he approaches the subject matter.
The other philosophy professor falls on the data end of the spectrum. His classes include routine handouts, carefully constructed Powerpoint presentations, and a clear outline of exactly what he is expecting from students on a quiz or final. Students don’t need to dig for the correct answer, but they do need to memorize it exactly once they find it.
The data-driven students who find the former professor’s approach frustrating are inclined to appreciate this professor much more, but the sentiment is reversed for the concept-driven students. They often find the latter professor’s lectures boring, since the structure can preclude discussion and critique.
Of course, neither of these approaches to learning is necessarily better than the other, and different disciplines are inherently better suited to different learning styles.
For example, math and science depend on exactitude, so the data-driven learner will tend to be more comfortable in those fields. Similarly, a literature or sociology student needs to be a more conceptual learner, since the conclusions of those fields are rarely decisive and easily formulated. Even so, both approaches show up in almost every discipline.
Learning styles fall on a spectrum, with theoretical, concept-driven style on the one end and concrete, data-driven style on the other. The contrast between teachers’ conceptual and data-driven learning styles grows larger as you progress through high school and into college, since professors often spend a lifetime developing one of the two teaching styles. High schoolers should begin paying attention to the distinction between these two styles now in order to get the most out of college classes later. It is crucial to learn which style you prefer.
Exploring the style spectrum
To get a better idea of what each of the two learning styles looks like, think about it this way: The conceptual learner enjoys understanding ideas in the abstract, where he or she can analyze them and sniff out inconsistencies. If you are conceptually driven, handouts, Powerpoints, and precise definitions probably do not concern you that much since you are more interested in the overall flow of an argument or how an idea evolves over time. The data-driven learner, on the other hand, thrives on specificity. If your learning style is data-powered, you seek out exact formulations of ideas to use as conclusive definitions. You gravitate toward Powerpoint presentations and tables that categorize information succinctly and definitively.
Which style fits you?
Now, identifying where you fall on the conceptual vs. data-driven spectrum is crucial. Think about your current teachers and place them somewhere on the spectrum; then consider whether their styles add to or detract from your appreciation of them. Once you’ve ascertained where on the spectrum you are most comfortable, do two things. First, seek out classes with professors and teachers whose teaching style places them near you on the conceptual/data-driven spectrum. Second, if you find yourself in a class whose teaching style is on the opposite end from your learning style, learn to manipulate the learning materials you receive to make them match your style. If your teacher uses a conceptual style but you are a data-driven learner, for example, boil his abstract concepts down to flash card-sized bites and study the key terms and ideas that way.
A tale of two professors
For a real-world explication of the conceptual/data-driven spectrum, consider two of the philosophy professors at my alma mater whose teaching styles fall on opposite ends of the concept-data divide. One professor is very unstructured, with lectures driven primarily by students’ questions. His lectures ramble all over the place, but they are always interesting. Some students find his approach annoying: he rarely formulates his ideas concisely or exactly, and it is difficult to prepare stock answers for his tests. Others, however, seek out his classes because they appreciate the nuance with which he approaches the subject matter.
The other philosophy professor falls on the data end of the spectrum. His classes include routine handouts, carefully constructed Powerpoint presentations, and a clear outline of exactly what he is expecting from students on a quiz or final. Students don’t need to dig for the correct answer, but they do need to memorize it exactly once they find it.
The data-driven students who find the former professor’s approach frustrating are inclined to appreciate this professor much more, but the sentiment is reversed for the concept-driven students. They often find the latter professor’s lectures boring, since the structure can preclude discussion and critique.
Of course, neither of these approaches to learning is necessarily better than the other, and different disciplines are inherently better suited to different learning styles.
For example, math and science depend on exactitude, so the data-driven learner will tend to be more comfortable in those fields. Similarly, a literature or sociology student needs to be a more conceptual learner, since the conclusions of those fields are rarely decisive and easily formulated. Even so, both approaches show up in almost every discipline.
