In this paper, we explore students' motivation to use digital technologies before and after a course aimed at contributing to students' digital empowerment and critical perspective on digitalization. Based on Self-Determination Theory (SDT), we designed a questionnaire to observe changes in students' perceived competence, autonomy and relatedness. In addition to students' motivation, we investigated students' technology use continuance intention following Information Systems Continuance Theory. The surveys took place in the winter terms of 2020/21 and 2021/22 in a pretest-postest format. The course was open to students of all disciplines. As the questionnaire of this study was also distributed in a similar course in the teacher education program, we compared the responses of the students from all disciplines with the responses of prospective teachers. Due to the timing of the questionnaires, we also contextualized our findings regarding distance learning regulations during the early years of the pandemic. We validated and evaluated the questionnaire using exploratory factor and exploratory correlation analysis and found that students' perceived competence and perceived relatedness increased after course participation in both years. In line with SDT, these findings are also consistent with students reporting an increase in intrinsic motivation in both years. Students' perceived autonomy became more diverse in both years. In this regard, our findings support research suggesting that COVID-19 and distance learning regulations had a negative impact on students' perceptions of digital media autonomy. The comparison of results between the all-discipline cohort and the teacher education cohort showed that students benefited differently from the course depending on their field of study, which will be taken into account in future course iterations.

The aim of this research-to-practice full paper is to present the results of most recent analyses of the long-term school pilot project named "learning office approach". The learning office represents an alternative branch of a technology-oriented vocational secondary school in Europe encompassing the grades K9–K13 and has been implemented at the IT department in 2016. Students of a learning office cohort have flexible course schedules allowing them to choose between several subjects on a daily basis, which in turn enables differentiation and individualization: Students seeking help or striving to deepen their knowledge and skills may visit chosen subjects more often. Since learning office subjects do not rely on lectures and direct instruction, teachers become facilitative coaches helping students become self-responsible learners and study in a self-driven, self-directed way, which in turn fosters their 21stcentury skills.The COVID-19 pandemic and resulting emergency remote teaching strategies demonstrated the supreme importance of digital competences and self-efficacy: Students were abruptly required to work autonomously with digital systems and study at home in a self-driven way. Since the learning office heavily relies on students working with digital educational material and tools in a self-directed and student-centered way, the question arises if learners show indications of increased ICT competences and self-efficacy in the learning office.This paper evaluates the interplay of students’ self-reported ICT skills, school-specific self-efficacy, and academic performance in the learning office approach and traditional classrooms. Based on an extensive literature review, a questionnaire was developed and distributed among all 552 students of the grades 9–13 and students’ final grades of the winter term 2021/22 were analyzed. The results show a significant increase in self-perceived problem coping skills and ICT competences in the learning office approach, including the sub-scales "general ICT self-concept", "communication", "process and store", and "generate content". We conclude that technology-enhanced student-centered approaches, such as the learning office, are a viable method to foster students’ 21stcentury digital skills.

This Research to Practice Full Paper investigates the impact of longer-term distance learning on the self-driven, student-centered learning office approach compared to traditional classrooms. The learning office is a school pilot project of a vocational secondary school (grades 9–13) introduced in 2016. Following the principles of learner-centered education, learning office students study in a self-driven way using flexible class schedules and self-explanatory course material. For the present study, we evaluated the average grade of all 548 students in both approaches upon the completion of the winter term 2020/21 and compared it to the data of the winter term 2019/20. Furthermore, we conducted an anonymous survey answered by 99 students allowing to gain insight into the students' perspective on online learning. The results revealed a significant shift in the location parameter of the average grade at the grades 9, 10, and 13 in the winter term 2020/21. The comparison of the grades of 2021 with the previous year showed that the average grade did not change significantly or has improved in the learning office. The average grading of the traditional approach has stayed at about the same level. These counter-intuitive findings, which may be a result of milder grading due to the challenging situation, are consistent with previous research found in the literature. The survey on emergency remote teaching revealed that the students did not report difficulties maintaining social contacts, were more satisfied with the used assessment methods in distance learning, had a quiet workspace, and felt that the teachers reacted appropriately to the switch to online learning. Learning office students reportedly saw the switch to online learning less difficult, felt more self-reliant, and perceived higher levels of support and appreciation by their teachers, but suffered from less cooperation compared to previous findings. We therefore conclude that while learning office students perform equally well in online learning, they are missing important advantages in distance learning otherwise granted by the self-directed learning office approach.

This Research to Practice Full Paper represents the second comprehensive evaluation of the learning office, a pilot project introduced at a higher technical secondary school. The project has reached its third year and encompasses six of our twelve cohorts from grade 9 to grade 11. Students taking part in the learning office project have a more flexible course schedule, allowing them to choose time and scope for almost all of their school subject with some restrictions. The results revealed that the dropout rate could be reduced from 54% to 40% in the learning office over the first two years and also confirmed previous evaluations showing that students choosing the learning office approach struggle in their first year due to the change of systems. However, after they passed the first year, the overall subject-specific learning outcome shows no noteworthy differences. The learning office students of grade 11 even slightly outperformed their control group in some regards. This suggests that once the learning office students adapted to the new system, their gain in transversal, personal, and social skills do not happen at the cost of subject-specific competences, but is an additional asset improving our students' qualification to meet the challenges of tomorrow.

This Research to Practice Full Paper depicts and evaluates a school pilot project introducing student-centered classrooms for applied mathematics and software development at a higher technical secondary school. These student-centered classrooms are an alternative organizational form: If students choose to participate in the pilot project, they have non-traditional class schedules. Students can choose freely which subject they want to work on each day and visit the respective room, called “learning office”. Self-explanatory learning material is available in each learning office and students work independently on exercises and projects at their own pace and level, allowing them to specialize according to their personal interests. Teachers accompany pupils as coaches who help them plan and structure.The evaluation showed that students choosing the student-centered form need time, about half a year, to adapt to the change of system and thus perform worse in their first attempt at exams. However, after passing the first year, there is no notable difference in software development and only a weakly significant difference in applied mathematics. The average grades and passing rate of the two approaches show no notable differences. By cooperating significantly more frequently they develop social skills and show significantly higher motivation for attending school.

MOTIVATION: Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus important and of general interest. RESULTS: We propose to this end assessing different noise models on a large number of microarray experiments. The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom. Inference of differentially expressed genes is taken into consideration at a second mixing level. For attaining far reaching validity, our investigations cover a wide range of analysis platforms and experimental settings. As the most striking result, we find irrespective of the chosen preprocessing and normalization method in all experiments that a heavy-tailed noise model is a better fit than a simple Gaussian. Further investigations revealed that an appropriate choice of noise model has a considerable influence on biological interpretations drawn at the level of inferred genes and gene ontology terms. We conclude from our investigation that neglecting the over dispersed noise in microarray data can mislead scientific discovery and suggest that the convenience of Gaussian-based modelling should be replaced by non-parametric approaches or other methods that account for heavy-tailed noise.

BACKGROUND: While microarrays are the predominant method for gene expression profiling, probe signal variation is still an area of active research. Probe signal is sequence dependent and affected by probe-target binding strength and the competing formation of probe-probe dimers and secondary structures in probes and targets. RESULTS: We demonstrate the benefits of an improved model for microarray hybridization and assess the relative contributions of the probe-target binding strength and the different competing structures. Remarkably, specific and unspecific hybridization were apparently driven by different energetic contributions: For unspecific hybridization, the melting temperature Tm was the best predictor of signal variation. For specific hybridization, however, the effective interaction energy that fully considered competing structures was twice as powerful a predictor of probe signal variation. We show that this was largely due to the effects of secondary structures in the probe and target molecules. The predictive power of the strength of these intramolecular structures was already comparable to that of the melting temperature or the free energy of the probe-target duplex. CONCLUSIONS: This analysis illustrates the importance of considering both the effects of probe-target binding strength and the different competing structures. For specific hybridization, the secondary structures of probe and target molecules turn out to be at least as important as the probe-target binding strength for an understanding of the observed microarray signal intensities. Besides their relevance for the design of new arrays, our results demonstrate the value of improving thermodynamic models for the read-out and interpretation of microarray signals.