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Abstract Computer‐based interactive items have become prevalent in recent educational assessments. In such items, the entire human‐computer interactive process is recorded in a log file and is known as the response process. These data are noisy, diverse, and in a nonstandard format. Several feature extraction methods have been developed to overcome the difficulties in process data analysis. However, these methods often focus on the action sequence and ignore the time sequence in response processes. In this paper, we introduce a new feature extraction method that incorporates the information in both the action sequence and the response time sequence. The method is based on the concept of path signature from stochastic analysis. We apply the proposed method to both simulated data and real response process data from PIAAC. A prediction framework is used to show that taking time information into account provides a more comprehensive understanding of respondents' behaviors. 

Computerized assessments and interactive simulation tasks are increasingly popular and afford the collection of process data, i.e., an examinee’s sequence of actions (e.g., clickstreams, keystrokes) that arises from interactions with each task. Action sequence data contain rich information on the problem-solving process but are in a nonstandard, variable-length discrete sequence format. Two methods that directly extract features from the raw action sequences, namely multidimensional scaling and sequence-to-sequence autoencoders, produce multidimensional numerical features that summarize original sequence information. This study explores the utility of action sequence features in understanding how problem-solving behavior relates to cognitive proficiencies and demographic characteristics. This is empirically illustrated with the process data from the 2012 PIAAC PSTRE digital assessment. Regularized regression results showed that action sequence features are more predictive of examinees’ demographic and cognitive characteristics compared to final outcomes. Partial least squares analysis further aided the identification of behavioral patterns systematically associated with demographic/cognitive characteristics. 

Diagnostic classification tests are designed to assess examinees’ discrete mastery status on a set of skills or attributes. Such tests have gained increasing attention in educational and psychological measurement. We review diagnostic classification models and their applications to testing and learning, discuss their statistical and machine learning connections and related challenges, and introduce some contemporary and future extensions. 

Diagnostic classification tests are designed to assess examinees’ discrete mastery status on a set of skills or attributes. Such tests have gained increas- ing attention in educational and psychological measurement. We review diagnostic classification models and their applications to testing and learning, discuss their statistical and machine learning connections and related challenges, and introduce some contemporary and future extensions.