Search results for "soft"

Predicting human performance in interactive tasks by using dynamic models

2017

The selection of an appropriate sequence of activities is an essential task to keep student motivation and foster engagement. Usually, decisions in this respect are made by taking into account the difficulty of the activities, in relation to the student's level of competence. In this paper, we present a dynamic model that aims to predict the average performance of a group of students at solving a given series of maths problems. The system takes into account both student- and task-related features. This model was built and validated by using the data gathered in an experimental session that involved 64 participants solving a sequence of 26 arithmetic problems. The data collected from the fir…

2020

To successfully learn using open Internet resources, students must be able to critically search, evaluate and select online information, and verify sources. Defined as critical online reasoning (COR), this construct is operationalized on two levels in our study: (1) the student level using the newly developed Critical Online Reasoning Assessment (CORA), and (2) the online information processing level using event log data, including gaze durations and fixations. The written responses of 32 students for one CORA task were scored by three independent raters. The resulting score was operationalized as “task performance,” whereas the gaze fixations and durations were defined as indicators of “pr…

Aplicaciones didácticas del lenguaje "Soundpainting" en diferentes ámbitos educativos: una herramienta para la creación en tiempo real

2017

El objetivo de esta investigación ha sido analizar y describir cómo se ha aplicado el lenguaje Soundpainting en tres ámbitos educativos diferentes. Partiendo de la metodología cualitativa se seleccionaron un conservatorio, un centro de educación primaria y una escuela de música como estudio de caso colectivo. Las diferentes sesiones fueron grabadas en vídeo, revisadas y codificadas con la ayuda de programas de análisis cualitativo. Posteriormente se realizó una entrevista semiestructurada a cada uno de los profesores. Los resultados muestran que aplicando el lenguaje Soundpainting desde la perspectiva del juego y haciendo un uso didáctico de los gestos se facilitó el desarrollo de las capac…

贝叶斯因子及其在JASP中的实现

2018

Statistical inference plays a critical role in modern scientific research, however, the dominant method for statistical inference in science, null hypothesis significance testing (NHST), is often misunderstood and misused, which leads to unreproducible findings. To address this issue, researchers propose to adopt the Bayes factor as an alternative to NHST. The Bayes factor is a principled Bayesian tool for model selection and hypothesis testing, and can be interpreted as the strength for both the null hypothesis H0 and the alternative hypothesis H1 based on the current data. Compared to NHST, the Bayes factor has the following advantages: it quantifies the evidence that the data provide for…

Pattern languages with and without erasing

1994

The paper deals with the problems related to finding a pattern common to all words in a given set. We restrict our attention to patterns expressible by the use of variables ranging over words. Two essentially different cases result, depending on whether or not the empty word belongs to the range. We investigate equivalence and inclusion problems, patterns descriptive for a set, as well as some complexity issues. The inclusion problem between two pattern languages turns out to be of fundamental theoretical importance because many problems in the classical combinatorics of words can be reduced to it.

Ensemble feature selection with the simple Bayesian classification

2003

Abstract A popular method for creating an accurate classifier from a set of training data is to build several classifiers, and then to combine their predictions. The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. One way to generate an ensemble of accurate and diverse simple Bayesian classifiers is to use different feature subsets generated with the random subspace method. In this case, the ensemble consists of multiple classifiers constructed by randomly selecting feature subsets, that is, classifiers constructed in randomly chosen subspaces. In this paper, we present an algorithm for building ensembles of simple Bayesian classifiers in random sub…

Parallel distance transforms on pyramid machines: Theory and implementation

1990

Abstract A distance transform of a binary image is an array each of whose elements gives the distance from the corresponding pixel to the closest ‘1’ in the binary image. Distance transforms have uses in image matching and shape analysis, among other applications. We present a parallel algorithm for weighted distance transforms that runs particularly efficiently on hierarchical cellular-logic machines, a subclass of the architectures known as pyramid machines. The algorithm computes the 3–4 distance transform; however it can be readily adapted to the city-block (‘Manhattan’) and chessboard distance measures. The algorithm runs in O(M) time, for an M × M image. Since it avoids using arithmet…

Towards a method to generate GUI prototypes from BPMN

2018

Business Process Model and Notation (BPMN) provides organizations with a standard that facilitates further compression of the business process. BPMN focuses on the functional processes, leaving the development of interfaces to one side. Thereby, interface design usually depends on the subjective experience of the analyst. This article aims to propose a new method to generate user interfaces from BPMN models and Class Diagrams. The proposed method is based on the identification of different rules and makes use of stereotypes to extend BPMN notation. The rules have been extracted from seven existing projects on the Bizagi repository. Specifically, the proposal is based on the extraction of ru…

Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap

2015

This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, so…

A principled approach to network-based classification and data representation

2013

Measures of similarity are fundamental in pattern recognition and data mining. Typically the Euclidean metric is used in this context, weighting all variables equally and therefore assuming equal relevance, which is very rare in real applications. In contrast, given an estimate of a conditional density function, the Fisher information calculated in primary data space implicitly measures the relevance of variables in a principled way by reference to auxiliary data such as class labels. This paper proposes a framework that uses a distance metric based on Fisher information to construct similarity networks that achieve a more informative and principled representation of data. The framework ena…