Subjects: Psychology >> Statistics in Psychology submitted time 2019-11-08
Abstract： Problem-solving competence is defined as the capacity to engage in cognitive processing to understand and resolve problem scenarios where a solution is not obvious. Computer-based assessments usually provide an interactive environment in which students can solve a problem by choosing among a set of available actions and taking one or more steps to complete a task. All students’ actions are automatically recorded in system logs as coded and time-stamped strings. These strings are called process data. The process data have multi-level structures in which the actions are nested within a single individual and therefore they are logically interconnected. Recently, researches have focused on characterizing process data and analyzing the response strategies to solve the problem. This study proposed an extended MMixIRT model which incorporated the multilevel structure into a mixture IRT model. It can classify latent groups at process level that have different problem solving strategies, and estimate the students’ abilities at the student level simultaneously. This model takes the accumulated response information as the specific steps at the process level and defines a more free matrix to determine the weight information used for ability estimation at the student level. Specifically, in the standard MMixIRT model, the student-level latent variables are generally obtained from the measurement results made by the process-level response variables, while students’ final responses are used to estimate their problem-solving abilities in the extended MMixIRT model. This research applied process data recorded in one of the items (Traffic CP007Q02) of problem solving in PISA 2012. The samples were 3196 students from Canada, Hongkong-China, Shanghai-China, Singapore, and America. Based on the log file of the process record, there were 139,990 records in the final data file. It was found that (1) The model can capture different problem-solving strategies used by students at the process level, as well as provide ability estimates at the student level. (2) The model can also analyze the typical characteristics of students’ strategy in problem-solving across different countries for targeted instructional interventions. It is concluded that the extended MMixIRT model can analyze response data at process and student levels. These analyses not only play an important role in the scoring, but also provide valuable information to psychometricians and test developers, help them to better understand what distinguishes well performing students from the ones that are not, and eventually lead to better test design. "
Subjects: Psychology >> Physiological Psychology submitted time 2019-11-08
Abstract： Persons who are addicted are known to show cue-induced responses (such as psychological craving) to drug-related cues. Previous research showed that both tool-related (e.g., syringe) and action-related (e.g., use of the syringe) drug cues can elicit craving. However, whether the two types of drug related cues can elicit the same brain reactivity and similar degree of disinhibition is still unclear, especially because of the scarcity of ERP studies on this topic. Using a behavioral task and the ERP technique, the present study investigated the behavior reactivity and EEG characteristics shown by men addicted to heroin and healthy controls in response to tool-related and action-related drug cues. Participants were 36 men, 19 of whom were addicted to heroin and 17 of whom were healthy non-drug users, matched on age and years of education. Participants engaged in the two-choice Oddball task, which included two conditions: A. the tool condition, with a picture of a “cup” serving as the standard stimulus and pictures of drug-use tools serving as deviant stimuli; B. the action condition, with a picture of “drinking water” serving as the standard stimulus and pictures of drug-use actions serving as deviant stimuli. In this experiment, the probabilities of standard stimuli and deviant stimuli were 70% and 30%. Participants were asked to press different keys on the keyboard in response to standard stimuli and deviant stimuli as rapidly and accurately as possible. Behavioral results indicated that in men who were addicted to heroin, greater disinhibition was seen in a longer reaction time in response to action cues than tool cues. Between-group analyses of the ERP data showed that compared to the healthy controls, men who were addicted to heroin demonstrated a smaller N2 and larger P3 amplitude in response to drug related cues. Moreover, action cues elicited a smaller N2 amplitude in the heroin addicted group than the control group, especially in the frontal, central and central-parietal areas of the brain, and a larger P3 amplitude, especially in the central and parietal areas of the brain. Within-group analyses in just the heroin addicted group showed that the N2 was smaller in response to action cues versus tool cues. Meanwhile, the action cues elicited a larger amplitude of P3 than the tool cues, especially in the central, central-parietal and parietal regions of the brain. These findings provide behavioral and ERP evidence for the hypothesis that different types of drug-related cues produce different cue-induced reactivity. More specifically, drug use action stimuli, which appear to trigger greater disinhibition and greater ERP reactivity in the brain areas associated with motor resonance, should be considered in the treatment of addiction and in relapse prevention.
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