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INTRODUCTION: Tau-positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis. METHODS: Using head-to-head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed "CenTauR" [CTR]). JPM is a statistical model that simultaneously models the relationships between head-to-head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale. RESULTS: A strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach. DISCUSSION: Preliminary findings using the JPM support the development and adoption of a universal scale for tau-PET quantification. HIGHLIGHTS: Tested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed "CenTauRs". Tested a Centiloid-like linear regression approach. Using five cohorts with head-to-head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions.

© 2017 Elsevier Ltd. Mobile technologies and their applications have the potential to benefit various learning contexts. Users' perceptions of mobile learning (m-learning) technologies are of great importance and precede the successful integration of these technologies in education. M-learning adoption has been investigated in the literature with reference to various factors and learning analytics, but largely without considering the role of different configurations (i.e., specific combinations of variables), and how these configurations might affect the adoption of various user groups. For instance, users with different backgrounds, experiences, learning styles, and so on might not be represented by the one-model-fits-all produced from the common regression approaches. In this study, we briefly review factors that have been proven important in the context of mobile learning adoption, and build on complexity theory and configuration theory in order to explore the causal patterns of factors that stimulate the use of mobile learning. To test its propositions, the study employs fuzzy-set qualitative comparative analysis (fsQCA) on a data sample from 180 experienced m-learning users. Findings indicate eight configurations of cognitive and affective characteristics, and social and individual factors, that explain m-learning adoption. This research study contributes to the literature by (1) offering new insights on how predictors of m-learning adoption interrelate; (2) extending existing knowledge on how cognitive and affective characteristics, and social and individual factors, combine to lead to high m-learning adoption; and (3) presenting a step-by-step methodological approach for how to apply fsQCA in the area of learning systems and learning analytics.

© 2017 IEEE. Programming-based making activities are at the core of teaching strategies to engage young students in learning programming for developing computational thinking skills. Despite the initial evidences of enthusiastic participation in such activities, more systematic studies are needed to better understand drivers of students' intentions to participate in programming-based making activities. In this paper, we present an exploratory study which aim to address this issue by examining the interrelations among cognitive (i.e., perceived usefulness, perceived ease of use) and affective (i.e., enjoyment) characteristics for both boys and girls. To this end, we build on complexity theory and configuration theory, present a conceptual model, and employ fuzzy-set Qualitative Comparative Analysis (fsQCA) on a sample of 105 young students, to identify such interrelations. The findings provide insights on how the examined factors may have a different influence for boys and girls, an outcome that can be used to re-design educational programs targeting maximizing engagement regardless gender.

This study uses complexity theory to understand the causal patterns of factors that stimulate students' intention to continue studies in computer science (CS). To this end, it identifies gains and barriers as essential factors in CS education, including motivation and learning performance, and proposes a conceptual model along with research propositions. To test its propositions, the study employs fuzzy-set qualitative comparative analysis on a data sample from 344 students. Findings indicate eight configurations of cognitive and noncognitive gains, barriers, motivation for studies, and learning performance that explain high intention to continue studies in CS. This research study contributes to the literature by (1) offering new insights into the relationships among the predictors of CS students' intention to continue their studies and (2) advancing the theoretical foundation of how students' gains, barriers, motivation, and learning performance combine to better explain high intentions to continue CS studies.

This paper is an attempt to provide the basic guidelines on how to implement configurational analysis in the context of learning analytics. In detail, we offer a step by step approach on the fuzzy set qualitative comparative analysis (fsQCA). Learning analytics gain increased popularity, however studies use traditional symmetric statistical methods to analyze them. Building on the theory of complexity and configuration theory we suggest on using fsQCA in order to gain a deeper understanding of the data, which may lead to understanding different learning phenomena as well as to the creation of new theories. We further describe the steps on how to perform a contrarian case analysis, which will help in identifying asymmetric relations among the data. Finally, testing for predictive validity with fsQCA is explained. Many of the steps described here may be implemented in various contexts, however we tried to provide examples and instructions for learning analytics oriented research.

Researchers have been working to understand the high dropout rates in computer science (CS) education. Despite the great demand for CS professionals, little is known about what influences individuals to complete their CS studies. We identify gains of studying CS, the (learning) environment, degree’s usefulness, and barriers as important predictors of students’ intention to complete their studies in CS (retention). The framework aims to identify reasons that may contribute to dropout, using responses from 344 CS students. The eight-predictor model accounts for 39 % of the explained variance in student retention. A high level for degree’s usefulness has a positive effect on retention. Further, cognitive gains and supportive environment positively impact degree’s usefulness, while non-cognitive gains hinder it. Lastly, negative feelings (personal values) are found to reduce student retention. The overall outcomes are expected to contribute to theoretical development, in order to allow educators and policy makers to take appropriate measures to enhance students’ experience in CS studies and increase retention.