PPT-The Kaplan Model Depth and Complexity in Literature

By April Payne Overview of the Lesson The intent of this lesson is to create literature reflection booklets These can serve as an alternative to book reports and encourage accountability during inclass reading time The following icons in relationship to literature will ensure that students are engaged in higher order thinking skills reflecting the concepts of depth and complexity described in the Kaplan Model. Approximating the . Depth. via Sampling and Emptiness. Approximating the . Depth. via Sampling and Emptiness. Approximating the . Depth. via Sampling and Emptiness. Example: Range tree. S = Set of points in the plane. Depth First Search (DFS). We Already Covered . Breadth First Search(BFS). Traverses the graph one level at a time. Visit all outgoing edges from a node before you go deeper. Needs a queue. BFS creates a tree called BFS-Tree. &. . Convergecast. Downcast & . Upcast. . Distributed Algorithms for Multi-Agent Networks. Instructor: K. . Sinan. YILDIRIM. A broadcast operation is initiated by a single . processor. , the source. . Graduate School Seminar. College of Business and Law. Friday December 2. nd. , 2 to 4.30. Why a specific seminar on LR?. Critical step / deliverable in PhD research. Critical skill of the researcher. Depth First Search (DFS). We Already Covered . Breadth First Search(BFS). Traverses the graph one level at a time. Visit all outgoing edges from a node before you go deeper. Needs a queue. BFS creates a tree called BFS-Tree. Artificial Intelligence. Dr. Daniel Tauritz (Dr. T). Department of Computer Science. tauritzd@mst.edu. http://web.mst.edu/~tauritzd/. general course website: . http://web.mst.edu/~tauritzd/courses/intro_AI.html. Artificial Intelligence. Dr. Daniel Tauritz (Dr. T). Department of Computer Science. tauritzd@mst.edu. http://web.mst.edu/~tauritzd/. general course website: . http://web.mst.edu/~tauritzd/courses/intro_AI.html. CSD 15-780: Graduate Artificial Intelligence. Instructors: . Zico. . Kolter. and Zack Rubinstein. TA: Vittorio . Perera. 2. Search. Search lectures.. Readings:. Section II in . Norvig. and . Russel. Artificial Intelligence. Dr. Daniel Tauritz (Dr. T). Department of Computer Science. tauritzd@mst.edu. http://web.mst.edu/~tauritzd/. general course website: . http://web.mst.edu/~tauritzd/courses/intro_AI.html. Curtis Mobley. Vice President for Science and Senior Scientist. Sequoia Scientific, Inc.. Belleue. , WA 98005. curtis.mobley@sequoiasci.com. Overview of HydroLight-EcoLight. IOCCG Course. Villefranche-sur-Mer. Parallel Computation. Complexity Measures for Parallel Computation. Problem parameters:. n. index of problem size. p. number of processors. Algorithm parameters:. t. p. running time on p processors. Terri Verhaegen. AP/AVID/GATE Program Specialist. SAUSD. An overview . with classroom implications and . application. Overview: . Who . are. your GATE students?. Characteristics. Referral. Assessment Process. Guy E. Blelloch, Phillip B. Gibbons, Harsha Vardhan Simhadri. Slides by Endrias Kahssay. Why Parallel Cache Oblivious Algorithms? . Modern machines have multiple layers of cache – L1, L2, L3. . . Roughly 4 cycles, 10 cycles, and 40 cycles respectively. . MODELS OF TEACHING LITERATURE IN ENGLISH DR. HUSNIAH SAHAMID EDUATION FACULTY UNIVERSITI PUTRA MALAYSIA Different models different models suggested on the teaching of literature to ESL/EFL students (Carter & Long, Lazar).