Preschool teachers display a flexible pattern of pedagogical actions in promoting healthy habits in children

The school represents the optimal setting for promoting the physical, emotional, and social health of children, especially during the first years of life. Understanding the pedagogical actions of teachers to address health education is an important first step in promoting healthy behaviors in children. We inhere analyzed the pedagogical action patterns in the preschool teaching of healthy habits from a holistic health perspective. We used photography as a strategy for data collection and applied a Chi-square automatic interaction detection (CHAID) classification tree, a data mining procedure, to generate a pattern model. We found that the school space and the learning playfulness strategies for the development of executive functions, classified according to the exercise, symbolic, assembly, rules (ESAR) model, were the main factors that influence the pedagogical actions fostering healthy habits. By contrast, the school and the pedagogical resources of the classroom are factors with a much smaller impact on working with healthy habits. This pedagogical action pattern is flexible, since teachers conduct a multiplicity of pedagogical actions through different strategies, in different school spaces, at any time. In conclusion, our results unmask the interdependent relationships between the different factors that determine the teacher's actions at the preschool. It also contributes to the understanding of the teacher's practices in fostering healthy habits in a healthy learning environment

APPLYING CHAID TO IDENTIFY THE ACCOUNTING-FINANCIAL CHARACTERISTICS OF THE MOST PROFITABLE REAL ESTATE COMPANIES IN SPAIN

The aim of this study is the determination, from an empirical perspective, of the accounting and financial features which could condition financial profitability of real estate companies, to identify the performances that guarantee its permanency in the current marketplace, characterized by the world economic crisis, specially in Spain, whose housing sector represents an important contributor to the economic growth. Although at a theoretical level the DuPont Model establishes the relationships between a group of accounting ratios and financial profitability. This paper uses a sample of 5,484 Spanish real estate companies to quantify these relationships and to extract the most relevant ones and to obtain the patterns of the most profitable companies. We use ROE to measure profitability and we analyze various independent variables about solvency, liquidity, activity, turnover, financial equilibrium and investment structure. The main contribution is of methodological nature, as we have applied statistics tools that do not require initial hypotheses on the distribution of the variables, by using a data mining technique of classification and regression tree based on rule induction algorithms known as CHAID. The study provides quantitatively success profiles by means of a set of rules describing the patterns of the most profitable companies.CHAID; financial profitability; classification trees; accounting ratios; Spain.

Analisis CHAID Prediksi Ketepatan Waktu Lulus Berdasarkan Penguasaan Kompetensi Mahasiswa dengan dan tanpa Prediktor Utama

Kemampuan metode CHAID sebagai teknik nonparametrik berbentuk algoritma pohon klasifikasi yang efektif untuk data berukuran besar serta kemudahan interpretasi model prediksi yang berbentuk pohon keputusan menjadikan CHAID bermanfaat untuk membantu dalam proses analisis data pendidikan. Penelitian ini bertujuan untuk menunjukkan perbedaan hasil prediksi ketepatan waktu lulus mahasiswa Fakultas Keguruan dan Ilmu Pendidikan Universitas Islam Malang berdasarkan pada penguasaan kompetensi menggunakan metode CHAID dengan dan tanpa penentuan prediktor utama. Hasil penelitian menyimpulkan bahwa ada tidaknya penentuan prediktor utama memberikan perbedaan pada variabel-variabel prediktor yang terlibat untuk memprediksi variabel terikat dan juga pada segmentasi target yang ingin dicapai untuk tujuan prediksi variabel terikat. Akan tetapi, ada tidaknya penentuan prediktor utama tersebut tidak mempengaruhi jumlah prediktor yang terlibat dan tingkat akurasi analisis CHAID yang dilakukan.The ability of CHAID method as a nonparametric technique which is an effective classification tree algorithm for large data size as well as the decision tree prediction model which is easier to be interpreted make CHAID useful to help in educational data analysis. This paper aims to describe differences in predicting student’s graduation accuracy of Teacher Training and Education Faculty, University of Islam Malang, based on the competence mastery using CHAID method with and without determining a main predictor. The results conclude that determining or not determining the main predictor gives differences to the predictor variables involved and also to the segmentation targets in predicting dependent variable. However, the determining or not determining the main predictor does not affect the number of predictors involved and the level of accuracy of the CHAID analysis carried out.

Using data mining techniques to predict students at risk of poor performance

The achievement of good honours in Undergraduate degrees is important in the context of Higher Education (HE), both for students and for the institutions that host them. In this paper, we look at whether data mining can be used to highlight performance problems early on and propose remedial actions. Furthermore, some of the methods may also form the basis for recommender systems that may guide students towards their module choices to increase their chances of a good outcome. We use data collected through the admission process and through the students' degrees. In this paper, we predict good honours outcomes based on data at admission and on the first year module results. To validate the proposed results, we evaluate data relating to students with different characteristics from different schools. The analysis is achieved by using historical data from the Data Warehouse of a specific University. The methods used, however, are fairly general and can be used in any HE institution. Our results highlight groups of students at considerable risk of obtaining poor outcomes. For example, using admissions and first year module performance data we can isolate groups for one of the studied schools in which only 24% of students achieve good honour degrees. Over 67% of all low achievers in the school can be identified within this group

Applying of Data Mining and Statistical Techniques to Analyze the Impact of Socioeconomic Background on University Admission - A Case Study Using the Iranian Educational Data

The goal of this thesis was to conduct a focused and in-depth comprehensive study of the impact of socioeconomic status of the Iranian Wide Entrance Examination applicants’ family on the educational achievement of their children. To reach this goal we used various statistical methods and data mining techniques. The data over five years made it possible to construct classification and forecasting models for each year, separately. To the best of our knowledge, when dealing with the Iranian educational data, there is no comprehensive study that takes into account dynamic aspects