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Geogebra (Geometry and Algebra) - combines geometric objects like circles and graphs of functions with its algebraic representation e.g. + = representing a circle with the radius . Designed for use in schools and educational settings.
There are three main types of computer environments for studying school geometry: supposers [vague], dynamic geometry environments (DGEs) and Logo-based programs. [2] Most are DGEs: software that allows the user to manipulate ("drag") the geometric object into different shapes or positions.
The concept of intelligent machines for instructional use date back as early as 1924, when Sidney Pressey of Ohio State University created a mechanical teaching machine to instruct students without a human teacher. [5] [6] His machine resembled closely a typewriter with several keys and a window that provided the learner with questions. The ...
On the ancient lore of dice and the odds against making a point 1968 Dec: The world of the Möbius strip: endless, edgeless and one-sided 1969 Jan: Dr. Matrix gives his explanation of why Mr. Nixon was elected President 1969 Feb: Boolean algebra, Venn diagrams and the propositional calculus: 1969 Mar: The multiple fascinations of the Fibonacci ...
Desmos was founded by Eli Luberoff, a math and physics double major from Yale University, [3] and was launched as a startup at TechCrunch's Disrupt New York conference in 2011. [4] As of September 2012 [update] , it had received around 1 million US dollars of funding from Kapor Capital , Learn Capital, Kindler Capital, Elm Street Ventures and ...
The ideas of teaching machines and programmed learning provided the basis for later ideas such as open learning and computer-assisted instruction. Illustrations of early teaching machines can be found in the 1960 sourcebook, Teaching Machines and Programmed Learning. [12] An "Autotutor" was demonstrated at the 1964 World's Fair. [13]
It is critical that students learn math concepts using a variety of tools. For example, as students learn to make patterns, they should be able to create patterns using all three of these tools. Seeing the same concept represented in multiple ways as well as using a variety of concrete models will expand students’ understandings.
It reinforced winning strategies by making the moves more likely, by supplying extra beads. [8] This was one of the earliest versions of the Reinforcement Loop, the schematic algorithm of looping the algorithm, dropping unsuccessful strategies until only the winning ones remain. [4] This model starts as completely random, and gradually learns. [9]