Doutoramento

Facebook Twitter

Multiple Intelligences and Learning Styles. From Emerging Perspectives on Learning, Teaching and Technology Emily Giles, Sarah Pitre, Sara Womack Department of Educational Psychology and Instructional Technology, University of Georgia Introduction Various theories on learning have been developed with increasing frequency in the last few decades.

Multiple Intelligences and Learning Styles

In order to understand the relationship between these theories, Curry's onion model (Curry, 1983) was developed with four layers -- personality learning theories, information processing theories, social learning theories, and multidimensional and instructional theories. Personality learning theories define the influences of basic personality on preferences to acquiring and integrating information. Information processing theories encompass individuals' preferred intellectual approach to assimilating information, and includes David Kolb's model of information processing, which identifies two separate learning activities: perception and processing. Multiple Intelligences Section Ms. Mrs. Profesorado — ocwus. Sir Ken Robinson. May 25 - Interview with Sir Ken Robinson on Creativity. Join me Wednesday, May 25th, for a live and interactive FutureofEducation.com webinar with Sir Ken Robinson about the extensively revised and updated version of his best-selling classic, Out of Our Minds: Learning to be Creative .

May 25 - Interview with Sir Ken Robinson on Creativity

We'll talk about why it is essential to promote creativity, what happens in schools to make us think we are not creative, and what schools and organizations can do to develop creativity and innovation in a deliberate and systematic way. Date: Wednesday, May 25th, 2011Time: 5pm Pacific / 8pm Eastern / 12am GMT (next day--international times here)Duration: 1 hourLocation: In Elluminate. Log in at The Elluminate room will be open up to 30 minutes before the event if you want to come in early. To make sure that your computer is configured for Elluminate, please visit Recordings of the session will be posted within a day of the event here.Event Recording: Full Elluminate MP3 His book The Element: How Finding Your Passion Changes Everything.

The Future of Education - Charting the Course of Teaching and Learning in a Networked World. Steve Hargadon. Project Zero Classroom with Programs in Professional Education at the Harvard Graduate School of Education. What You Will Learn Create classrooms, instructional materials and out-of-school learning environments that promote deep learning and understanding.

Project Zero Classroom with Programs in Professional Education at the Harvard Graduate School of Education

Program Overview What constitutes an effective and powerful learning experience in the 21st century? As we examine the shifting terrain of education, it is essential to be responsive to complex social developments and to create learning experiences that are engaging and exciting for all learners. How do we best prepare young people for a future that is hard to imagine? The Project Zero Classroom features various frameworks and tools that enable you to look at teaching analytically, develop new approaches to planning and make informed decisions about instruction. Program Objectives The program addresses fundamental educational questions, such as: Main Page - Researching Virtual Initiatives in Education. ICPSR Summer Program. Welcome to the 2014 program!

ICPSR Summer Program

We welcome you to the ICPSR Summer Program in Quantitative Methods of Social Research. Last year, we celebrated a half century as a leading program in training in social science research methodologies and technologies. In 2014, we continue to serve diverse multidisciplinary and growing international constituencies, and are working to make our program even better known. One of the Summer Program's outstanding strengths is its supportive and unique social environment. It facilitates participants' exchange of ideas, approaches, and techniques to develop theories and analyses of social science topics. When the 2014 schedule is finalized, it will list over 90 beginning and advanced courses at our primary location in Ann Arbor and several "off-site" institutions.

We appreciate your interest in the ICPSR Summer Program, and I hope to see you as a participant this summer. John A. Questions? Using R for statistical analyses - Non-parametric statistics. Studentized Range - Q The studentized range statistic is commonly used in post-hoc analyses.

Using R for statistical analyses - Non-parametric statistics

The distribution function is built-in to R and we may access it in one of two ways. ptukey(q, nmeans, df) qtukey(p, nmeans, df) Using R for statistical analyses - Non-parametric statistics. Studentized Range - Q The studentized range statistic is commonly used in post-hoc analyses.

Using R for statistical analyses - Non-parametric statistics

The distribution function is built-in to R and we may access it in one of two ways. ptukey(q, nmeans, df) qtukey(p, nmeans, df) In the first case we input a confidence level and get the corresponding Q value. In the second case we input a Q value and get the corresponding confidence level. With respect to our Kruskal-Wallis post-hoc test the easiest way to proceed would be to calculate the value of Q that results when using the U value calculated by the pairwize U-test (using the formula as shown above).

Next enter the values into the ptukey() command using: q= the value of Q you just found. nmeans= the number of samples in the original K-W test (e.g. 6 for our carbs data set) df= Inf The result is the Confidence Interval not a p-value. An alternative method would be to work out the critical value of Q first of all. Qtukey(CI, nmeans, df= Inf) Which U-value? However, there is a potential problem. Kruskal-Wallis Test. A collection of data samples are independent if they come from unrelated populations and the samples do not affect each other.

Kruskal-Wallis Test

Using the Kruskal-WallisTest, we can decide whether the population distributions are identical without assuming them to follow the normal distribution. Example In the built-in data set named airquality, the daily air quality measurements in New York, May to September 1973, are recorded. The ozone density are presented in the data frame column Ozone. > head(airquality) Ozone Solar.R Wind Temp Month Day 1 41 190 7.4 67 5 1 2 36 118 8.0 72 5 2 .....

Problem Without assuming the data to have normal distribution, test at .05 significance level if the monthly ozone density in New York has identical data distributions from May to September 1973. Solution The null hypothesis is that the monthly ozone density are identical populations. Answer. An R Introduction to Statistics. ENGAGE Learning. SAGE Journals Online. Quantitative Methods in Education Research. Beginning Research | Action Research | Case Study | Interviews | Observation Techniques | Education Research in the Postmodern Evaluation Research in Education | Narrative| Presentations | Qualitative Research | Quantitative Methods | Questionnaires | Writing up Research Quantitative Methods in Education Research Dr Ulrike Hohmann Originally prepared by Professor John Berry © J Berry, Centre for Teaching Mathematics, University of Plymouth, 2005.

Quantitative Methods in Education Research