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Humans have an incredible capacity for learning, adapting, and developing expertise in various fields, including technology, music, and academic subjects such as reading, writing, math, science, and second languages. However, understanding how humans learn in academic courses is of particular importance, as it is a distinct feature of the human species and can be used to improve education.

New technologies have revolutionized how we approach academic learning, providing unprecedented volumes of data. Leveraging this data, researchers have developed cognitive and statistical models of skill acquisition to understand similarities and differences across learners. The results of this research have been published in a recent study, which provides insight into how much practice students need to reach mastery, how much students vary in their initial performance, and, most astonishingly, how similar students are in their learning rate.

LearnLab's Goals and Methodology

LearnLab is an organization that was established to identify the mental units of learning in academic courses, using these insights to design and demonstrate improved instruction in randomized controlled experiments embedded in courses and building models of learners that reveal significant similarities and differences across learners. To achieve this, researchers developed cognitive models of the mental units students acquire in academic courses, which were used to redesign course units. Random assignment field experiments comparing student use of the redesign (treatment) with the original design (control) demonstrated enhanced learning outcomes.

The cognitive models were used to decompose learning into discrete units, or knowledge components, which produced predictions that could be tested against student performance data across different contexts and at different times. Investigations across multiple datasets supported this knowledge component hypothesis.

The primary research questions of the study were to understand how many practice opportunities students need to reach a mastery level of 80% correctness, how much students vary in their initial performance, and how much they vary in their learning rate. Statistical growth models and cognitive models of skill acquisition were used to model data from student performance on groups of tasks that assess the exact skill component and provide follow-up instruction on student errors.


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The researchers applied their models to 1.3 million observations across 27 datasets of student interactions with online practice systems in the context of elementary to college courses in math, science, and language. The datasets were produced by students using educational technology in natural contexts of academic courses, involving common forms of instruction such as lectures and assigned readings, which typically preceded student practice within educational technology.

The researchers found that despite the availability of up-front verbal instruction, like lectures and readings, students demonstrated modest initial prepractice performance at about 65% accuracy. Despite being in the same course, students’ initial performance varied substantially from about 55% correct for those in the lower half to 75% for those in the upper half.

The most surprising finding was that students were astonishingly similar in their estimated learning rate, typically increasing by about 0.1 log odds or 2.5% in accuracy per opportunity. This finding challenges theories of learning to explain the odd combination of significant variations in student initial performance and striking regularity in student learning rate.

However, the researchers found substantial variation in the learning opportunities needed to master a typical knowledge component across students. This suggests that differences in student learning are due more to differences in learning opportunities than to student-inherent learning-rate differences.

The study's findings challenge current learning theories to explain the striking regularity in student learning rates despite the significant variation in student initial performance. It also highlights the importance of learning opportunities in academic settings, as students require extensive practice to reach mastery. The regularity in student learning rate suggests that the differences in student performance are more likely due to differences in learning opportunities than inherent differences in learning ability. This has important implications for education, emphasizing the need for high-quality instruction and personalized learning opportunities to ensure that all students have the opportunity to succeed.

The Importance of Learning Opportunities

This research poses a critical challenge to current theories of learning. Practice and repetition are essential components of the learning process. Students require extensive practice to reach a level of mastery. However, the astonishing regularity in student learning rates suggests that the differences in student performance are not due to inherent differences in learning ability. Instead, they are more likely due to differences in learning opportunities.

This has important implications for education. If we can provide all students with access to the same quality and quantity of learning opportunities, we can close the current achievement gaps. This means ensuring all students can access high-quality instruction and educational technologies that provide feedback and support their learning. It also means providing additional resources to students needing extra help or support.

This research also highlights the importance of personalized learning. Educational technologies that provide personalized feedback and instruction can help ensure that all students receive the learning opportunities they need to succeed. By tailoring instruction to individual student needs, we can help ensure that every student has the opportunity to reach their full potential.

This research provides important insights into the learning process. It suggests that extensive practice and repetition are essential to the learning process. Still, differences in student performance are more likely due to differences in learning opportunities than inherent differences in learning ability. This has important educational implications, highlighting the need for high-quality instruction and personalized learning opportunities to ensure that all students reach their full potential.

As we continue to explore the science of learning, it is important to remember that every student is unique. By understanding the individual needs of each student and providing them with the learning opportunities they need, we can help ensure that every student has the opportunity to succeed. This is not only important for the future success of individual students but also for the success of our society as a whole. By investing in education and providing every student with the opportunity to reach their full potential, we can create a brighter future for all.

You can read the original study here.

About the Author

jenningsRobert Jennings is the co-publisher of InnerSelf.com, a platform dedicated to empowering individuals and fostering a more connected, equitable world. A veteran of the U.S. Marine Corps and the U.S. Army, Robert draws on his diverse life experiences, from working in real estate and construction to building InnerSelf.com with his wife, Marie T. Russell, to bring a practical, grounded perspective to life’s challenges. Founded in 1996, InnerSelf.com shares insights to help people make informed, meaningful choices for themselves and the planet. More than 30 years later, InnerSelf continues to inspire clarity and empowerment.

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This article is licensed under a Creative Commons Attribution-Share Alike 4.0 License. Attribute the author Robert Jennings, InnerSelf.com. Link back to the article This article originally appeared on InnerSelf.com

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