Alexandra Posekany’s research while affiliated with University of Vienna and other places

Background The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program. Results Our results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction. Conclusions The dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources. Peer Review reports

In this paper we explore aspiring teachers’ motivation in using digital technology. Referring to the self determination theory, we examine changes in their perceived competence, autonomy and relatedness before and after a University course on “Digital Education” in the two consecutive winter terms of 2020/21 and 2021/22. This course aims at contributing to students’ digital empowerment and critical perspective on digitalisation. To get insight on students’ motivation, we arranged a questionnaire. Apart from their motivation, we asked for students’ technology use continuance intention. The validation of the questionnaire along with its evaluation is based on several statistical approaches: We compare the internal validity of content-related group of questions through correlation and factor analyses. Further, we explore the outcomes for two specific student cohorts in two succeeding years. Students perceived competence and realisation of digital media’s usefulness increased in both cohorts after course participation. KeywordsDigitalisationDigital media useMedia use continuanceSelf-determination theoryDesign-based researchPre-post test