Conscientiousness as a Predictor of the Gender Gap in Academic Achievement

In recent decades, female students have been more successful in higher education than their male counterparts in the United States and other industrialized countries. A promising explanation for this gender gap are differences in personality, particularly higher levels of conscientiousness among women. Using Structural Equation Modeling on data from 4719 Dutch university students, this study examined to what extent conscientiousness can account for the gender gap in achievement. We also examined whether the role of conscientiousness in accounting for the gender gap differed for students with a non-dominant ethnic background compared to students with a dominant ethnic background. In line with our expectations, we found that conscientiousness fully mediated the gender gap in achievement, even when controlling for prior achievement in high school. This was the case among both groups of students. These findings provide insight into the mechanisms underlying the gender gap in achievement in postsecondary education settings. The current study suggests that the use of conscientiousness measures in university admission procedures may disadvantage male students. Instead, the use of such measures may be a fruitful way to identify those students who may benefit from interventions to improve their conscientiousness. Future research could examine how conscientiousness can be fostered among students who are low in conscientiousness

The Care2Report System: Automated Medical Reporting as an Integrated Solution to Reduce Administrative Burden in Healthcare

Documenting patient medical information in the electronic medical record is a time-consuming task at the expense of direct patient care. We propose an integrated solution to automate the process of medical reporting. This vision is enabled through the integration of speech and action recognition technology with semantic interpretation based on knowledge graphs. This paper presents our dialogue summarization pipeline that transforms speech into a medical report via transcription and formal representation. We discuss the functional and technical architecture of our Care2Report system along with an initial system evaluation with data of real consultation sessions

Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019

One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is that of a: "Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of entities. [...] This grand challenge extends this further by asking if we can create a knowledge graph of "everything" ranging from common sense concepts to location based entities. This knowledge graph should be "open to the public" in a FAIR manner democratizing this mass amount of knowledge." Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides a unique testbed for experimenting and evaluating research hypotheses on open and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing evolution and long term preservation. We want to investigate this problem, that is to understand what preserving and supporting the evolution of KGs means and how these problems can be addressed. Clearly, the problem can be approached from different perspectives and may require the development of different approaches, including new theories, ontologies, metrics, strategies, procedures, etc. This document reports a collaborative effort performed by 9 teams of students, each guided by a senior researcher as their mentor, attending the International Semantic Web Research School (ISWS 2019). Each team provides a different perspective to the problem of knowledge graph evolution substantiated by a set of research questions as the main subject of their investigation. In addition, they provide their working definition for KG preservation and evolution

Diagnostic classification models for actionable feedback in education: Effects of sample size and assessment length

E-learning is increasingly used to support student learning in higher education. This results in huge amounts of item response data containing valuable information about students' strengths and weaknesses that can be used to provide effective feedback to both students and teachers. However, in current practice, feedback in e-learning is often given in the form of a simple proportion of correctly solved items rather than diagnostic, actionable feedback. Diagnostic classification models (DCMs) provide opportunities to model the item response data from formative assessments in online learning environments and to obtain diagnostic information to improve teaching and learning. This simulation study explores the demands on the data structure (i.e., assessment length, respondent sample size) to apply log-linear DCMs to empirical data. Thereby we provide guidance to educational practitioners on how many items need to be administered to how many students in order to accurately assess skills at different levels of specificity using DCMs. In addition, effects of misspecification of the dimensionality of the assessed skills on model fit indices are explored. Results show that detecting these misspecifications statistically with DCMs can be problematic. Recommendations and implications for educational practice are discussed

Cognitive diagnostic assessment in university statistics education: Valid and reliable skill measurement for actionable feedback using learning dashboards

E-learning is increasingly used to support student learning in higher education, facilitating administration of online formative assessments. Although providing diagnostic, actionable feedback is generally more effective, in current practice, feedback is often given in the form of a simple proportion of correctly solved items. This study shows the validation process of constructing detailed diagnostic information on a set of skills, abilities, and cognitive processes (so-called attributes) from students’ item response data with diagnostic classification models. Attribute measurement in the domain of statistics education is validated based on both expert judgment and empirical student data from a think-aloud study and large-scale assessment administration. The constructed assessments provide a valid and reliable measurement of the attributes. Inferences that can be drawn from the results of these formative assessments are discussed and it is demonstrated how this information can be communicated to students via learning dashboards to allow them to make more effective learning choices

Personality predicts academic achievement in higher education: Differences by academic field of study?

In the present study it is investigated whether students enrolled in different academic fields of study have differing personality traits (i.e., conscientiousness and openness) and whether the relationship between these traits and academic achievement differs by academic field. Using Structural Equation Modeling on data from a large sample of university students, this study examined to what extent students' levels of conscientiousness and openness differ by academic field and whether these personality traits have differential predictive value for academic achievement for students in different academic fields. We found that students who are more open to experience and less conscientious are more likely to enroll in a program in the academic field of arts/humanities than in another field. There were no differences in the predictive value of these personality traits for academic achievement by academic field when controlling for prior performance in high school. These findings emphasize the general effectiveness of conscientiousness in explaining academic achievement and also call for the consideration of academic fields or college majors in personality research. Besides having theoretical implications, these findings have practical implications for higher education

Diagnostic classification models for actionable feedback in education: Effects of sample size and assessment length

E-learning is increasingly used to support student learning in higher education. This results in huge amounts of item response data containing valuable information about students' strengths and weaknesses that can be used to provide effective feedback to both students and teachers. However, in current practice, feedback in e-learning is often given in the form of a simple proportion of correctly solved items rather than diagnostic, actionable feedback. Diagnostic classification models (DCMs) provide opportunities to model the item response data from formative assessments in online learning environments and to obtain diagnostic information to improve teaching and learning. This simulation study explores the demands on the data structure (i.e., assessment length, respondent sample size) to apply log-linear DCMs to empirical data. Thereby we provide guidance to educational practitioners on how many items need to be administered to how many students in order to accurately assess skills at different levels of specificity using DCMs. In addition, effects of misspecification of the dimensionality of the assessed skills on model fit indices are explored. Results show that detecting these misspecifications statistically with DCMs can be problematic. Recommendations and implications for educational practice are discussed

Data_Sheet_2_Properties and performance of the one-parameter log-linear cognitive diagnosis model.pdf

Diagnostic classification models (DCMs) are psychometric models that yield probabilistic classifications of respondents according to a set of discrete latent variables. The current study examines the recently introduced one-parameter log-linear cognitive diagnosis model (1-PLCDM), which has increased interpretability compared with general DCMs due to useful measurement properties like sum score sufficiency and invariance properties. We demonstrate its equivalence with the Latent Class/Rasch Model and discuss interpretational consequences. The model is further examined in a DCM framework. We demonstrate the sum score sufficiency property and we derive an expression for the cut score for mastery classification. It is shown by means of a simulation study that the 1-PLCDM is fairly robust to model constraint violations in terms of classification accuracy and reliability. This robustness in combination with useful measurement properties and ease of interpretation can make the model attractive for stakeholders to apply in various assessment settings.</p

Data_Sheet_1_Properties and performance of the one-parameter log-linear cognitive diagnosis model.zip

Diagnostic classification models (DCMs) are psychometric models that yield probabilistic classifications of respondents according to a set of discrete latent variables. The current study examines the recently introduced one-parameter log-linear cognitive diagnosis model (1-PLCDM), which has increased interpretability compared with general DCMs due to useful measurement properties like sum score sufficiency and invariance properties. We demonstrate its equivalence with the Latent Class/Rasch Model and discuss interpretational consequences. The model is further examined in a DCM framework. We demonstrate the sum score sufficiency property and we derive an expression for the cut score for mastery classification. It is shown by means of a simulation study that the 1-PLCDM is fairly robust to model constraint violations in terms of classification accuracy and reliability. This robustness in combination with useful measurement properties and ease of interpretation can make the model attractive for stakeholders to apply in various assessment settings.</p