This chapter focuses on how pupil’s scientific understanding can be studied. The principles of a complex dynamic systems approach are highlighted.
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Studying real-time teacher-student interaction provides insight into student's learning processes. In this study, upper grade elementary teachers were supported to optimize their instructional skills required for co-constructing scientific understanding. First, we examined the effect of the Video Feedback Coaching intervention by focusing on changes in teacher-student interaction patterns. Second, we examined the underlying dynamics of those changes by illustrating an in-depth micro-level analysis of teacher-student interactions. The intervention condition showed significant changes in the way scientific understanding was co-constructed. Results provided insight into how classroom interaction can elicit optimal co-construction and how this process changes during an intervention.
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Albeit the widespread application of recommender systems (RecSys) in our daily lives, rather limited research has been done on quantifying unfairness and biases present in such systems. Prior work largely focuses on determining whether a RecSys is discriminating or not but does not compute the amount of bias present in these systems. Biased recommendations may lead to decisions that can potentially have adverse effects on individuals, sensitive user groups, and society. Hence, it is important to quantify these biases for fair and safe commercial applications of these systems. This paper focuses on quantifying popularity bias that stems directly from the output of RecSys models, leading to over recommendation of popular items that are likely to be misaligned with user preferences. Four metrics to quantify popularity bias in RescSys over time in dynamic setting across different sensitive user groups have been proposed. These metrics have been demonstrated for four collaborative filteri ng based RecSys algorithms trained on two commonly used benchmark datasets in the literature. Results obtained show that the metrics proposed provide a comprehensive understanding of growing disparities in treatment between sensitive groups over time when used conjointly.
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CRISPR/Cas genome engineering unleashed a scientific revolution, but entails socio-ethical dilemmas as genetic changes might affect evolution and objections exist against genetically modified organisms. CRISPR-mediated epigenetic editing offers an alternative to reprogram gene functioning long-term, without changing the genetic sequence. Although preclinical studies indicate effective gene expression modulation, long-term effects are unpredictable. This limited understanding of epigenetics and transcription dynamics hampers straightforward applications and prevents full exploitation of epigenetic editing in biotechnological and health/medical applications.Epi-Guide-Edit will analyse existing and newly-generated screening data to predict long-term responsiveness to epigenetic editing (cancer cells, plant protoplasts). Robust rules to achieve long-term epigenetic reprogramming will be distilled based on i) responsiveness to various epigenetic effector domains targeting selected genes, ii) (epi)genetic/chromatin composition before/after editing, and iii) transcription dynamics. Sustained reprogramming will be examined in complex systems (2/3D fibroblast/immune/cancer co-cultures; tomato plants), providing insights for improving tumor/immune responses, skin care or crop breeding. The iterative optimisations of Epi-Guide-Edit rules to non-genetically reprogram eventually any gene of interest will enable exploitation of gene regulation in diverse biological models addressing major societal challenges.The optimally balanced consortium of (applied) universities, ethical and industrial experts facilitates timely socioeconomic impact. Specifically, the developed knowledge/tools will be shared with a wide-spectrum of students/teachers ensuring training of next-generation professionals. Epi-Guide-Edit will thus result in widely applicable effective epigenetic editing tools, whilst training next-generation scientists, and guiding public acceptance.
