Yesterday I saw this tweet:
The main insight is thus that…
…we found students to be astonishingly similar in estimated learning rate, typically increasing by about 0.1 log odds or 2.5% in accuracy per opportunity.
Why is this new study by Kenneth et al zo important?
The learning rate question is practically important because it bears on fundamental questions regarding education and equity. Can anyone learn to be good at anything they want? Or is talent, like having a “knack for math” or a “gift for language”, required? Our evidence suggests that given favorable learning conditions for deliberate practice and given the learner invests effort in sufficient learning opportunities, indeed, anyone can learn anything they want. If true, this implication is good news for educational equity — as long as our educational systems can provide the needed favorable conditions and can motivate students to engage in them. The variety of well-designed interactive online practice technologies used to produce our datasets point to a scalable strategy to provide these favorable conditions. Importantly, these technologies were well engineered to provide the key features of deliberate practice including well-tailored task design, sufficient repetition in varied contexts, feedback on learners’ responses, and embedded instruction when learners need it. At the same time, students do not learn from these technologies if they do not use them. Recent research providing human tutoring to increase student motivation to engage in difficult deliberate practice opportunities suggests promise in reducing achievement gaps by reducing opportunity gap
How big is this finding? Well, wait a minute. Yes, the dataset is huge. The results are not dissimilar to what Graham Nuttall described, and the importance of prior knowledge and the effects of deliberate practice have been known for years. At the same time: no influence of background or genetics on the learning rate? A tough one to chew on, and even more important: this is a preprint. I’m looking forward to a peer-reviewed version.
Abstract of the pre-print:
Leveraging a scientific infrastructure for exploring how students learn, we have developed cognitive and statistical models of skill acquisition and used them to understand fundamental similarities and differences across learners. Our primary question was why do some students learn faster than others? Or do they? We model data from student performance on groups of tasks that assess the same skill component and that provide follow-up instruction on student errors. Our models estimate, for both students and skills, initial correctness and learning rate, that is, the increase in correctness after each practice opportunity. We applied our 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. Despite the availability of up-front verbal instruction, like lectures and readings, students demonstrate modest initial pre-practice performance, at about 65% accuracy. Despite being in the same course, students’ initial performance varies substantially from about 55% correct for those in the lower half to 75% for those in the upper half. In contrast, and much to our surprise, we found students to be astonishingly similar in estimated learning rate, typically increasing by about 0.1 log odds or 2.5% in accuracy per opportunity. These findings pose a challenge for theories of learning to explain the odd combination of large variation in student initial performance and striking regularity in student learning rate.
From experience to meaning... 
Sounds quite a lot like what Ben Bloom wrote in 1971 and John Carroll in 1956!!!
Uit “How Teaching Happens”:
Carroll made clear that if students’ aptitudes are normally distributed, then giving all students the same instruction will lead to exam results along the curve. Carroll, however, saw aptitude, and with it the variables for achieving mastery as (1) the amount of time that the learner needs to attain mastery of a learning task, (2) the quality of instruction as the “degree to which the presentation, explanation, and ordering the elements of the task to be learned approach the optimum for a given learner” (p. 47), and (3) the ability to understand instruction in terms of understanding the nature of the task and procedures that need to be followed in learning the task. Hence, if the kind and quality of instruction along with the amount of time available for learning are made appropriate to the characteristics and needs of each student, then the majority of students should achieve mastery.
Carroll was 1963