![]() ![]() The results? Mastery learning classroom practices produced a 1 sigma gain in student achievement in Bloom’s studies in other words, the average student in a mastery learning environment performed better than 84 percent of the students in a conventional instructional environment. In short, the class adjusts its pace and path to ensure foundational topics are mastered before moving on. In mastery learning, the teacher reteaches topics that the majority of students don’t master, small groups peer-tutor one another on challenging topics, and students individually review materials they’ve missed. Of course, the “problem” in the 2 Sigma Problem is that individual or small-group tutoring is “too costly for most societies to bear on a large scale.” (The Apollo 20 program is reported to cost $29 million annually for nine schools.) The challenge, then, as Bloom framed it, is this:Ĭan researchers and teachers devise teaching-learning conditions that will enable the majority of students under group instruction to attain levels of achievement that (at present) can be reached only under good tutoring conditions? From the Tutor to the ClassĮssentially, Bloom asks, what are the key factors at play in tutoring that could be scaled into more cost-effective classroom teaching models? Bloom identified several, the most important of which was mastery learning. Secondary students who received math tutoring outperformed their peers in the treatment schools who did not receive tutoring by 0.4 sigma, or 200 percent (32). In a more recent study, Roland Fryer at Harvard’s EdLabs evaluated the impact on student achievement of five instructional practices implemented in Houston ISD’s Apollo 20 program, including high-dosage tutoring. This included personalizing both path and pace-identifying and addressing missing prerequisite knowledge, and spending more time where necessary to ensure students achieved mastery of topics before moving on. Personalization is defined as differentiating instruction and providing regular corrective feedback based on the needs of each student. What was the difference? Personalization. In other words, the average tutored student performed better than 98 percent of the students in the traditional classroom. Now that all being said, this is obviously a new field and real estate is an asset class with very limited public information and great variances in the information each party in a deal has, so this is a hugely untested platform/investment method and remains to be proven whether it actually works (see the recent Zillow disaster).Thirty years ago this month, Benjamin Bloom posed a challenge to the learning sciences community: How could we replicate the effectiveness of one-to-one or small-group tutoring in a more cost-effective, scalable way (Bloom, “ The 2 Sigma Problem”)? In Bloom’s study, students who learned a topic through tutoring, combined with regular formative assessment and corrective instruction, performed two standard deviations (2 sigma) better than students who received conventional classroom instruction. This can also be applied to pricing models and being able to identify when an asset is underpriced very quickly rather than relying on market knowledge and experience. When it comes to real estate this probably means tracking certain metrics (as Shervin said, cellphone usage/foot traffic/transit usage and the like) to identify changes in patterns in given sub-markets to try and pick up on trends early, and more importantly I can almost guarantee you they're using some type of machine learning/AI to recognize these patterns and spit out recommendations or at least narrow the field. ![]() Assuming they are buying physical assets and not REIT stocks, as a quant fund they are coding all their own proprietary software. ![]()
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