Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

To provide more refined diagnostic feedback with collateral information in item response times (RTs), this study proposed joint modelling of attributes and response speed using item responses and RTs simultaneously for cognitive diagnosis. For illustration, an extended deterministic input, noisy 'and' gate (DINA) model was proposed for joint modelling of responses and RTs. Model parameter estimation was explored using the Bayesian Markov chain Monte Carlo (MCMC) method. The PISA 2012 computer-based mathematics data were analysed first. These real data estimates were treated as true values in a subsequent simulation study. A follow-up simulation study with ideal testing conditions was conducted as well to further evaluate model parameter recovery. The results indicated that model parameters could be well recovered using the MCMC approach. Further, incorporating RTs into the DINA model would improve attribute and profile correct classification rates and result in more accurate and precise estimation of the model parameters.

Providing diagnostic feedback about growth is crucial to formative decisions such as targeted remedial instructions or interventions. This article proposed a longitudinal higher-order diagnostic classification modeling approach for measuring growth. The new modeling approach is able to provide quantitative values of overall and individual growth by constructing a multidimensional higher-order latent structure to take into account the correlations among multiple latent attributes that are examined across different occasions. In addition, potential local item dependence among anchor (or repeated) items can be taken into account. Model parameter estimation is explored in a simulation study. An empirical example is analyzed to illustrate the applications and advantages of the proposed modeling approach.

In this article, the JAGS software program is systematically introduced to fit common Bayesian cognitive diagnosis models (CDMs), including the deterministic inputs, noisy "and" gate (DINA) model, the deterministic inputs, noisy "or" gate (DINO) model, the linear logistic model, the reduced reparameterized unified model (rRUM), and the log-linear CDM (LCDM).The unstructured latent structural model and the higher-order latent structural model are both introduced. We also show how to extend those models to consider the polytomous attributes, the testlet effect, and the longitudinal diagnosis. Finally, an empirical example is presented as a tutorial to illustrate how to use the JAGS codes in R. Keywords: cognitive diagnosis modeling, Bayesian estimation, Markov chain Monte Carlo, DINA model, DINO model, rRUM, testlet, longitudinal diagnosis, polytomous attributes 2013), their commercialization prevents unauthorized users especially students from accessing these software without purchasing. In this article, we demonstrate how to use the freeware, JAGS, to fit several popular CDMs and present the code. It is expected that the researchers can adapt the code to fit extended CDMs,which cannot be fitted in existing software or packages for their research or application purposes. In general, JAGS makes it easy to construct a Markov chain for parameters. It does not require users to derive the posterior distribution of the model parameters by hand. Movereover,, the R2jags package (Version 0.5-7; Su & Yajima, 2015) in R could be easily used to call the JAGS. Furthermore, It should be noted that the JAGS code presented in this study can be generalized easily to other BUGS software programs by minor editing including WinBUGS and OpenBUGS 2 . The following sections first illustrate JAGS codes for five CDMs: (1) the DINA model; (2) the DINO model; (3) the LLM; (4) the rRUM, and (5) the LCDM. Besides those five models, which are based on the unstructured (or saturated) latent structuralels, the higher-order latent structural model (de la Torre & Douglas, 2004) is also demonstrated. Further, the extensions to the polytomous attributes, the testlet effect, and the longitudinal diagnosis using JAGS are presentedas well. Lastly, an empirical example analysis is conductedto illustrate how to use the R2jags package to run the JAGS code.

The within-item characteristic dependency (WICD) means that dependencies exist among different types of item characteristics/parameters within an item. The potential WICD has been ignored by current modeling approaches and estimation algorithms for the deterministic inputs noisy ''and'' gate (DINA) model. To explicitly model WICD, this study proposed a modified Bayesian DINA modeling approach where a bivariate normal distribution was employed as a joint prior distribution for correlated item parameters. Simulation results indicated that the model parameters were well recovered and that explicitly modeling WICD improved model parameter estimation accuracy, precision, and efficiency. In addition, when potential item blocks existed, the proposed modeling approach still demonstrated good performance and high robustness. Furthermore, the fraction subtraction data were analyzed to illustrate the application and advantage of the proposed modeling approach.