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Many students often cram for individual unit tests throughout the semester, relying on binge-watching lectures and rereading textbooks. However, this approach often leads to a shallow understanding of concepts and results in forgetting crucial material when it comes to the cumulative exams at the end of the year.

Using this approach is highly inefficient, as it requires spending significant amounts of time relearning the material. Moreover, you may struggle to retain the information as you progress to higher-level classes, placing you at a significant disadvantage.

 

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Evidence-Based Learning Techniques

Before delving into the techniques, I believe it's important that I at least introduce the physiological basis of what is happening when we learn.

 

First, we begin with sensory inputs which include visual or auditory stimuli that mostly go unnoticed and are subsequently forgotten.

 

If any attention is given to these inputs, they enter the working memory which is a temporary storage system where information is actively processed. The working memory allows us to hold and manipulate a limited amount of information simultaneously, enabling us to make sense of the inputs. Without further action, the working memory will forget the information within seconds to minutes.

 

For durable learning to occur, information from working memory needs to be transferred to long-term memory through a process called encoding. The long-term memory is able to then store the information for extended periods, ranging from hours to months. In order for the information to return back into the working memory for utilization, it must go through a retrieval process.

 

 

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By understanding these cognitive processes, we can employ effective learning strategies that optimize the transfer of information between the working memory and long-term memory, ensuring better retention and recall of the material.

Now let's closely examine four really important principles that will guide our learning system.

 

Principle One: Active Recall

Defining Active Recall

Active recall involves deliberate and effortful retrieval of information from memory in order to strengthen neural pathways and optimize long-term learning. It is imperative to 12

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distinguish between active or “free” recall from the more ineffective but commonly used cued recall method.

 

An example of cued recall is the use of flashcards as this simply requires a student to make a connection between a term (cue) and a definition (target) for isolated and decontextualized facts. This, like the many passive techniques mentioned before, creates an illusion of knowing and often will leave students performing poorly on exams as inference or application-based questions are more prevalent in higher education.

 

Instead, you should practice the way you would be tested on an exam by synthesizing, reorganizing, comparing, applying, and contextualizing the information through retrieval practice. This approach allows you to create meaningful structure in your brain with links between concepts that create deeper impressions on your memory.

If any of the four principles were more important than the others, it would likely be active recall, as numerous studies have shown its incredible effectiveness across various learning applications. Rather than dedicating the next 20 pages to reviewing each experiment individually, let's focus on a study published in 2011 by Karpicke and Blunt.

This study examined multiple important factors through its intelligent design.

 

Examining the Scientific Research:

Introduction of Experiments 1 & 2

This study used two separate experiments in order to compare the effectiveness of encoding and retrieval practices on long-term retention. The study aimed to challenge two assumptions regarding human learning:

 

1. Learning happens mostly through the encoding process

2. Retrieval can measure how well previous information was embedded into memory but does not produce learning itself

These two assumptions have kept most education research focused on enhancing the processing that occurs when students encode knowledge. As a result, methods like highlighting, rereading, and summarizing have gained widespread popularity within the field.

 

Moreover, novel elaborative encoding methods were believed to offer additional benefits, leading educators to heavily rely on them. In both experiments, students were instructed to create concept maps as a means to illustrate elaborative encoding.

 

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Experiment 1

Eighty undergraduate students participated in the first experiment where they were separated into the following four separate groups:

1. Study (1x): These participants studied the text in a single study period.

2. Repeated study (4x): These participants studied the text in four consecutive study periods.

3. Concept mapping: These participants studied the text and then created a concept map of the concepts while still viewing the text.

4. Retrieval practice: These participants studied the text and then recalled as much of the information as they could on a free recall test.

 

* The total amount of learning time was exactly matched in the concept mapping and retrieval practice conditions.

 

After the study sessions for the four groups, the students predicted the percentage of information that they would retain in one week—this was called their "metacognitive predictions”.

 

Then after one week had passed, the students returned to the lab for testing, where they were presented with two types of questions that required short answer responses.

The two types were:

1. Verbatim questions which assess conceptual knowledge stated directly in the text.

2. Inference questions which require students to connect multiple concepts from the text.

 

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These were the results of their test performances along with their metacognitive predictions:

 

 

 

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