The purpose of this study is to investigate the causal associations among academic achievement, motivation, burnout, self-regulation, and life satisfaction by two partial-structural regression models. Participants of the study were 294 college of education students majoring in eight different undergraduate programs. Student version of Maslach Burnout, Academic Motivation, Self-Regulation, and Life Satisfaction self-report inventories were used to gather data. Research results showed that burnout has a negative direct effects on academic achievement, life satisfaction, and academic motivation. Furthermore, self-regulation has a positive direct effect on academic achievement. The study indicated no direct effect from motivation to academic achievement, although it has an indirect effect through the mediating factor self-regulation.

Many studies in the literature indicate relationships between epistemological beliefs, metacognition, and critical thinking. All these constructs either directly or indirectly affect learning and cognition. In this research, we aim to disclose the magnitude of the direct effect the epistemological beliefs has on metacognition and critical thinking as well as determining the size of direct effect of metacognition and indirect effect of epistemological beliefs on critical thinking. To determine magnitude of the postulated direct and indirect causal effects between the three constructs, we collected and analyzed a set of data reflecting 234 college students' level of epistemological beliefs, metacognition, and critical thinking. After careful examination, 18 cases were outliers and were removed prior to the analyses. Therefore, the analysis proceeded with a sample size of 215 participants Then a specific structural equation model (SEM), namely structural regression (SR) model, employed for data analysis. The results of this study suggested that fostering epistemological beliefs of learners on naive-sophisticated axis might develop their metacognitive and critical thinking skills.

In this study, we describe the methodology used to identify and validate a set of expert-defined fraction subtraction related attributes. These attributes are expected to be mastered by 6th grade students toward proficiency in fraction subtraction. This research argues and demonstrates that state standards guiding subject instruction plays an important role in the identification of the domain related fundamental attributes. This study also illustrates complete implementation of cognitive diagnosis model framework, which is used to extract diagnostic information about students' specific strengths and weaknesses.

Richer diagnostic information about examinees' cognitive strength and weaknesses are obtained from cognitively diagnostic assessments (CDA) when a proper cognitive diagnosis model (CDM) is used for response data analysis. To do so, researchers state that a preset cognitive model specifying the underlying hypotheses about response data structure is needed. However, many real data CDM applications are adds-on to simulation studies and retrofitted to data obtained from non-CDAs. Such a procedure is referred to as retrofitting, and fitting CDMs to traditional test data is not uncommon. To deal with a major validity concern of item/test bias in CDAs, some recent DIF detection techniques compatible with various CDMs have been proposed. This study employs several DIF detection techniques developed based on CTT, IRT, and CDM frameworks and compares the results to understand the extent to which DIF flagging behavior of items is affected by retrofitting. A secondary purpose of this study is to gather evidence about test booklet effects (i.e., item ordering) on items' psychometric properties through DIF analyses. Results indicated severe DIF flagging prevalence differences for items across DIF detection techniques employing Wald test, Raju's area measures, and Mantel-Haenzsel statistics. The largest numbers of DIF cases were observed when the data were retrofitted to a CDM. The results further revealed that an item might be flagged as DIF in one booklet, whereas it might not be flagged in another.

Well-designed assessment methodologies and various cognitive diagnosis models (CDMs) to extract diagnostic information about examinees' individual strengths and weaknesses have been developed. Due to this novelty, as well as educational specialists' lack of familiarity with CDMs, their applications are not widespread. This article aims at presenting the fundamentals of CDM and demonstrating the various implementations using a freeware R package, namely, the GDINA. Present article explains the basics of CDM and provide sufficient details on the implementations so that it may guide novice researchers in CDM applications.