Machine learning (ML) provides a powerful framework for the analysis of high‐dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non‐linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large‐scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML‐based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large‐scale assessment in education. Context and implications: Rationale for this study: In 2020, the worldwide SARS‐COV‐2 pandemic forced the educational sciences to perform a rapid paradigm shift with classrooms going online around the world—a hardly novel but now strongly catalysed development. In the context of data‐driven education, this paper demonstrates that the widespread adoption of machine learning techniques is central for the educational sciences and shows how these methods will become crucial tools in the collection and analysis of data and in concrete educational applications. Helping to leverage the opportunities and to avoid the common pitfalls of machine learning, this paper provides educators with the theoretical, conceptual and practical essentials. Why the new findings matter: The process of teaching and learning is complex, multifaceted and dynamic. This paper contributes a seminal resource to highlight the digitisation of the educational sciences by demonstrating how new machine learning methods can be effectively and reliably used in research, education and practical application. Implications for educational researchers and policy makers: The progressing digitisation of societies around the globe and the impact of the SARS‐COV‐2 pandemic have highlighted the vulnerabilities and shortcomings of educational systems. These developments have shown the necessity to provide effective educational processes that can support sometimes overwhelmed teachers to digitally impart knowledge on the plan of many governments and policy makers. Educational scientists, corporate partners and stakeholders can make use of machine learning techniques to develop advanced, scalable educational processes that account for individual needs of learners and that can complement and support existing learning infrastructure. The proper use of machine learning methods can contribute essential applications to the educational sciences, such as (semi‐)automated assessments, algorithmic‐grading, personalised feedback and adaptive learning approaches. However, these promises are strongly tied to an at least basic understanding of the concepts of machine learning and a degree of data literacy, which has to become the standard in education and the educational sciences. Demonstrating both the promises and the challenges that are inherent to the collection and the analysis of large educational data with machine learning, this paper covers the essential topics that their application requires and provides easy‐to‐follow resources and code to facilitate the process of adoption.

The “digit span backwards” (DSB) is the most commonly used test in clinical neuropsychology to assess working memory capacity. Yet, it remains unclear how the task is solved cognitively. The present study was conducted to examine the use of visual and verbal cognitive strategies in the DSB. Further, the relationship between the DSB and a complex span task, based on the Simultaneous Storage and Processing task ( Oberauer et al., 2003 ), was investigated. Visualizers performed better than verbalizers in the dual task condition (r_PB = .23) only when the relevant digits were presented optically. Performance in the DSB correlated only weakly with the complex span task in all conditions (all τ ≤ .21). The results indicate that the processing modality is determined by the preference for a cognitive strategy rather than the presentation modality and suggest that the DSB measures different working aspects than commonly used experimental working memory tasks.

The molecular pathomechanisms of major depressive disorder (MDD) are still not completely understood. Here, we follow the hypothesis, that mitochondria dysfunction which is inevitably associated with bioenergetic disbalance is a risk factor that contributes to the susceptibility of an individual to develop MDD. Thus, we investigated molecular mechanisms related to mitochondrial function in induced neuronal progenitor cells (NPCs) which were reprogrammed from fibroblasts of eight MDD patients and eight non-depressed controls. We found significantly lower maximal respiration rates, altered cytosolic basal calcium levels, and smaller soma size in NPCs derived from MDD patients. These findings are partially consistent with our earlier observations in MDD patient-derived fibroblasts. Furthermore, we differentiated MDD and control NPCs into iPS-neurons and analyzed their passive biophysical and active electrophysiological properties to investigate whether neuronal function can be related to altered mitochondrial activity and bioenergetics. Interestingly, MDD patient-derived iPS-neurons showed significantly lower membrane capacitance, a less hyperpolarized membrane potential, increased Na⁺ current density and increased spontaneous electrical activity. Our findings indicate that functional differences evident in fibroblasts derived from MDD patients are partially present after reprogramming to induced-NPCs, could relate to altered function of iPS-neurons and thus might be associated with the aetiology of major depressive disorder.

There has been considerable debate and interest regarding the factor structure of executive functioning (EF). Therefore, the aim of the current study was to delve into this issue differently, by investigating EF and other cognitive constructs, such as working memory capacity (WMC), relational integration, and divided attention, which may contribute to EF. Here, we examined whether it is possible to provide evidence for a definite model of EF containing the components of updating, shifting, and inhibition. For this purpose, 202 young adults completed a battery of EF, three WMC tests, three relational integration tests, and two divided attention tests. A confirmatory factor analysis on all the cognitive abilities produced a five-factor structure, which included one factor predominately containing shifting tasks, the next factor containing two updating tasks, the third one predominately representing WMC, the fourth factor consisting of relational integration and antisaccade tasks, and finally, the last factor consisting of the divided attention and stop signal tasks. Lastly, a subsequent hierarchical model supported a higher-order factor, thereby representing general cognitive ability.