The Effect of Pair Programming Learning Model on Learning Outcomes of Basic Programming of Vocational School Students

The main problem in formal learning today is the low absorption of students in understanding the material. This can be seen from the average student learning outcomes which are always still low. Low learning outcomes are indicated because the learning conditions are still conventional (lectures, practicums, and discussions). This study aims to determine the effect of pair programming learning models on learning outcomes of vocational high school students. This research was conducted through a literature review and relevant research results and was continued through a Focus Group Discussion (FGD). From the research it was found that there was a significant positive influence between the variable pair programming learning model and student learning outcomes, which means that student learning outcomes can be improved through the application of the pair programming learning model.

Analisa Struktur Dependensi Variabe Pembentukan Asuransi Pertanian Berbasis Indeks Cuaca dengan Multivariat Copula dan Vine Copula

The purpose of this study is to analyze the structure of the dependency on variables for calculation of insurance based on weather indices such as crop prices, yields, and rainfall. The object of research observation was secondary data on the sub-district of Dlingo Bantul District. In analyzing the dependency of variables that can be used in agricultural insurance calculations, it can be seen that both using multivariate copula and vine copula have the same results. A multivariate copula that directly looks at dependency relationships between three variables. Whereas copula vine can see the size values ​​of the variable pair dependency for each edge in the copula vine tree. In more detail the best dependency for the grain price and rainfall variable is Copula Joe with the parameter θ = 1.76. correlation τ = 0.3. The best dependency between rainfall and yield is Frank Copula with parameters θ = 4.98 and correlation τ = 0.46. The best dependency between rainfall and yield is Frank copula with parameters θ = 2.42 and correlation τ = 0.25.

Direction-dependence analysis

The concept of direction dependence has attracted growing attention due to its potential to help decide which of two competing linear regression models ( X → Y or Y → X) is more likely to reflect the correct causal flow. Several tests have been proposed to evaluate hypotheses compatible with direction dependence. In this issue, Thoemmes (2015) reports results of an empirical evaluation of direction-dependence tests using real-world data sets with known causal ordering and concludes that the tests (known to perform excellent in simulation studies) perform poorly in the real-world setting. The present article aims at answering the question how this is possible. First, we review potential conceptual issues associated with Thoemmes’ (2015) approach. We argue that direction dependence is best conceptualized as a confirmatory approach to test focused directional theories. Thoemmes’ (2015) evaluation is based on an exploratory use of direction dependence. It implicitly follows the tradition of causal search algorithms. Second, we discuss potential statistical issues associated with Thoemmes’ (2015) selection schemes used to decide whether a variable pair is suitable for direction-dependence analysis. Based on these issues, new tests of direction dependence as well as new guidelines for confirmatory direction-dependence analysis are proposed. An empirical example is given to illustrate the application of these guidelines.

A Multi-Parametric Particle Pairing Algorithm for Particle Tracking Velocimetry in Single and Multiphase Flows

The measurement of turbulent flows becomes problematic when considering a dispersed multiphase flow, which typically requires special techniques focusing on the simultaneous resolution of both the carrier and discrete phases present in the flowfield. The method presented in this paper, a multi-parametric particle pairing algorithm for particle tracking velocimetry (MP3-PTV), provides a powerful and flexible technique for the measurement of multiphase flows. Combined with a traditional Particle Image Velocimetry (PIV) system, the MP3-PTV employs a variable pair-matching algorithm which utilizes displacement preconditioning in combination with estimated particle size and intensity to match particle pairs between successive images. To improve the method’s efficiency, a new particle identification and segmentation routine was also developed. Validation of the new method was performed on two artificial data sets: a traditional single-phase flow published by the Visualization Society of Japan (VSJ) and an in-house generated multiphase flow having a bi-modal distribution of particles diameters. On the VSJ data set, the newly presented segmentation routine delivered a two-fold increase in identifying particles compared to other published methods. For the simulated multiphase flow data set, measurement efficiency of the dispersed phase improved from 9% to 41% for MP3-PTV as compared to traditional hybrid PTV.

Learning to Order BDD Variables in Verification

The size and complexity of software and hardware systems have significantly increased in the past years. As a result, it is harder to guarantee their correct behavior. One of the most successful methods for automated verification of finite-state systems is model checking. Most of the current model-checking systems use binary decision diagrams (BDDs) for the representation of the tested model and in the verification process of its properties. Generally, BDDs allow a canonical compact representation of a boolean function (given an order of its variables). The more compact the BDD is, the better performance one gets from the verifier. However, finding an optimal order for a BDD is an NP-complete problem. Therefore, several heuristic methods based on expert knowledge have been developed for variable ordering. We propose an alternative approach in which the variable ordering algorithm gains 'ordering experience' from training models and uses the learned knowledge for finding good orders. Our methodology is based on offline learning of pair precedence classifiers from training models, that is, learning which variable pair permutation is more likely to lead to a good order. For each training model, a number of training sequences are evaluated. Every training model variable pair permutation is then tagged based on its performance on the evaluated orders. The tagged permutations are then passed through a feature extractor and are given as examples to a classifier creation algorithm. Given a model for which an order is requested, the ordering algorithm consults each precedence classifier and constructs a pair precedence table which is used to create the order. Our algorithm was integrated with SMV, which is one of the most widely used verification systems. Preliminary empirical evaluation of our methodology, using real benchmark models, shows performance that is better than random ordering and is competitive with existing algorithms that use expert knowledge. We believe that in sub-domains of models (alu, caches, etc.) our system will prove even more valuable. This is because it features the ability to learn sub-domain knowledge, something that no other ordering algorithm does.