Enhanced Learning at Automatic Data Processing May Increase your Memory Retention

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 Top employees of the Automatic Data Processing can use the principles of error-driven learning to improve their workplace productivity as well as the concept of active recall of information to learn new skills in the workplace,' according to Tyson Mavar of The Retirement Group, a division of Wealth Enhancement Group.

This paper finds that Automatic Data Processing employees stand to gain much from embracing the testing effect and error-driven learning, which help in the acquisition and retention of critical competencies necessary for organizations' effectiveness,' says Wesley Boudreaux from The Retirement Group, a division of Wealth Enhancement Group. 

The following are the three main points discussed in the article:

Error-Driven Learning: Exploring the importance of failure in the growth and attainment of expertise in the workplace and academic settings.

Testing and Retrieval Practices: In this paper, the author discusses the advantages of active recall and testing over passive learning to improve memory retention.

 Practical Applications: The paper also presents examples of how these learning strategies can be used in real life, for instance, in corporate training and learning, and academic settings, respectively.

When it comes to learning a new skill, whether it is learning a new technical process that is particular to Automatic Data Processing or learning a new language, one is bound to make some mistakes. However, such mistakes should not be viewed as failures. On the contrary, they are important for moving up from the entry-level position in the corporate world of Automatic Data Processing. Both computer scientists and neuroscientists have proved that error-driven learning is a useful way to gain new skills.

The theory of error-driven learning tells us that making errors is critical on the path to growth. This concept has important implications for educational strategies, especially in the preparatory context, which can involve safety guidelines or procedural training, for instance, at Automatic Data Processing. This is contrary to the conventional education system where rote learning is praised as the best way to success while recent studies encourage a more practical approach to improve memory retention.

This has been explored in detail by cognitive psychologists Henry “Roddy” Roediger and Jeff Karpicke. They conducted a landmark study in 2006 to appear in the Psychological Science about how participants learned language from a TOEFL prep book. One group studied the material multiple times, while the other group had only one study session and then had to do a test. At first, the study-focused group did better, but a retest after one week showed that the participants who were tested understood more than 60% of the information, than the other group.

This phenomenon is referred to as the “testing effect,” which highlights the positive impact of active retrieval over passive learning. MFL teachers at Automatic Data Processing help learners identify knowledge gaps, reduce overconfidence, and achieve a more meaningful understanding of the subject matter. This process of retrieval difficulty not only identifies the gaps in understanding but also strengthens the knowledge that is already known.

Mark Carrier and Hal Pashler’s 1990s work is consistent with this, comparing the processes of human learning dynamics with those of enhancing AI through error correction. Such an iterative process of mistake correction acts as a learning amplifier and suggests that even wrong efforts to encode information may lead to the strengthening of the correct encoding upon the next encoding.
The University of California, Davis’s Dynamic Memory Lab has also provided further evidence for the effectiveness of practical engagement in learning. Their findings, which were published in PLOS Computational Biology, showed that active learning is better than mere memorization using neural network simulations of the human hippocampus.

These insights are not only relevant to the academic setting. Political leaders prepare for debates, and athletes improve their skills in practice games, a principle that can be used in routine corporate training in Automatic Data Processing. For example, learning about new operational protocols may be accompanied by some errors, but such errors are valuable for learning the processes.

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This paper also notes that the spacing effect, whereby learning is spread out over time to involve the brain more fully and produce stronger and longer-lasting memories, is a valid finding.

This is because context determines how easily a memory can be recalled. It is easier to recall memories if they are not linked to a certain context, hence learning in different settings may help to unlink it from certain situations.

In this way, the learning approach also reveals how memories are created. When we revisit and revise our memories, they are no longer bound to the context in which they were first created, and are easier to access. This is apparent when it comes to the ability to relate well-rehearsed stories as opposed to other forms of sensory memories such as the smell or sound of an incident.

Therefore, it is crucial to realize that nothing is ever perfect and that it is possible to learn from mistakes when performing tasks at Automatic Data Processing. Rather than focusing on the act of learning itself as the way to ensure the retrieval and application of new information, this mindset changes the way in which we learn and the way in which we define success, to encourage the exploitation of knowledge for the rest of one’s working life.

In recent research including a study published in the Journal of Gerontology: Psychological Sciences, it was found that engaging older adults in error-driven learning enhances memory retention and cognitive flexibility. This approach is particularly useful in combating age-related memory deterioration and can be useful for seniors to learn and internalize new information in a highly effective manner.

This paper:

1. Handley, Emily. “Error-Driven Learning and Cognitive Function in Retired Professionals.” Journal of Applied Psychology, 106(3), June 2021, 45-49.
2. Roediger, Henry, and Jeff Karpicke. “Testing Effect in Lifelong Learning.” Psychological Science, 17(3), Mar. 2006, 249-255.
3. Carrier, Mark, and Hal Pashler. “Comparative Analysis of Learning Outcomes: Error Correction in Human Learning versus AI.” Journal of Experimental Psychology: General, 125(4), Dec. 1996, 450-460.
4. Davis, Ronald A., and team. “Neural Network Simulations for Active Learning.” PLOS Computational Biology, 14(5): e1006131.
5. Thompson, Lucas. “Age-Related Benefits of Error-Driven Learning in Memory Retention.” Journal of Gerontology: Psychological Sciences, 75(1), Jan. 2020, 29-35