Abstract:

In the field of Inferential Statistics if every member of a population cannot be examined, then a sample from the same population is used to estimate an interval which is likely to contain some measure, such as the mean, of the population itself. This interval is called confidence interval because it has associated the probability that the true value of measure is contained in the interval. In this work, we built learning objects for teaching and learning inferential statistics using mobile devices equipped with Android operating system. With these learning objects, students can calculate confidence intervals based in either a large or a small data sample obtained from a normal or a non-normal population. These objects are being used successfully by student of the economic-administrative area. For the implementation of use of the tools designed in this project, we will use of learning activities based in the collaborative work and significant learning, by means of the visualization of short videos (no more than 5 minutes), exercises applied the student's area of study in order that he use the tools designed in his resolution, brief readings than implicate basic concepts of statistics and probability, auto-evaluations by mean of short quizzes, combined with activities of independent investigation. This work is part to the results generated by the project “Statistics-to-Go” that is being currently developed in the Department of Mathematics of the University of Sonora at Mexico. In works previous, we have designed tools in order to be used in descriptive statistics, and build histograms, bar chart, frequencies polygon; visualize summary measures (average, median, mode, standard deviation, and variance), and asymmetry or skewness measures (coefficients of Fisher and Pearson, and kurtosis coefficient); display a scatter-plot; estimate the Pearson correlation index; calculate the linear regression equation, and plot the regression line on the original scatter-plot.

Keywords:

Statistics, m-learning, Android, Learning Objects, Mobile Computing.