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Designing and generating new data under targeted properties has been attracting various critical applications such as molecule design, image editing and speech synthesis. Traditional hand-crafted approaches heavily rely on expertise experience and intensive human efforts, yet still suffer from the insufficiency of scientific knowledge and low throughput to support effective and efficient data generation. Recently, the advancement of deep learning has created the opportunity for expressive methods to learn the underlying representation and properties of data. Such capability provides new ways of determining the mutual relationship between the structural patterns and functional properties of the data and leveraging such relationships to generate structural data, given the desired properties. This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation. First, the article raises the potential challenges and provides preliminaries. Then the article formally defines controllable deep data generation, proposes a taxonomy on various techniques and summarizes the evaluation metrics in this specific domain. After that, the article introduces exciting applications of controllable deep data generation, experimentally analyzes and compares existing works. Finally, this article highlights the promising future directions of controllable deep data generation and identifies five potential challenges. 

This study presents a comprehensive analysis of three types of multimodal data‐response accuracy, response times, and eye‐tracking data‐derived from a computer‐based spatial rotation test. To tackle the complexity of high‐dimensional data analysis challenges, we have developed a methodological framework incorporating various statistical and machine learning methods. The results of our study reveal that hidden state transition probabilities, based on eye‐tracking features, may be contingent on skill mastery estimated from the fluency CDM model. The hidden state trajectory offers additional diagnostic insights into spatial rotation problem‐solving, surpassing the information provided by the fluency CDM alone. Furthermore, the distribution of participants across different hidden states reflects the intricate nature of visualizing objects in each item, adding a nuanced dimension to the characterization of item features. This complements the information obtained from item parameters in the fluency CDM model, which relies on response accuracy and response time. Our findings have the potential to pave the way for the development of new psychometric and statistical models capable of seamlessly integrating various types of multimodal data. This integrated approach promises more meaningful and interpretable results, with implications for advancing the understanding of cognitive processes involved in spatial rotation tests. 

Abstract In computer‐based tests allowing revision and reviews, examinees' sequence of visits and answer changes to questions can be recorded. The variable‐length revision log data introduce new complexities to the collected data but, at the same time, provide additional information on examinees' test‐taking behavior, which can inform test development and instructions. In the current study, we used recently proposed statistical learning methods for sequence data to provide an exploratory analysis of item‐level revision and review log data. Based on the revision log data collected from computer‐based classroom assessments, common prototypes of revisit and review behavior were identified. The relationship between revision behavior and various item, test, and individual covariates was further explored under a Bayesian multivariate generalized linear mixed model. 

Attribute hierarchy, the underlying prerequisite relationship among attributes, plays an important role in applying cognitive diagnosis models (CDM) for designing efficient cognitive diagnostic assessments. However, there are limited statistical tools to directly estimate attribute hierarchy from response data. In this study, we proposed a Bayesian formulation for attribute hierarchy within CDM framework and developed an efficient Metropolis within Gibbs algorithm to estimate the underlying hierarchy along with the specified CDM parameters. Our proposed estimation method is flexible and can be adapted to a general class of CDMs. We demonstrated our proposed method via a simulation study, and the results from which show that the proposed method can fully recover or estimate at least a subgraph of the underlying structure across various conditions under a specified CDM model. The real data application indicates the potential of learning attribute structure from data using our algorithm and validating the existing attribute hierarchy specified by content experts.