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In the human genome, heterozygous sites refer to genomic positions with a different allele or nucleotide variant on the maternal and paternal chromosomes. Resolving these allelic differences by chromosomal copy, also known as phasing, is achievable on a short-read sequencer when using a library preparation method that captures long-range genomic information. TELL-Seq is a library preparation that captures long-range genomic information with the aid of molecular identifiers (barcodes). The same barcode is used to tag the reads derived from the same long DNA fragment within a range of up to 200 kilobases (kb), generating linked-reads. This strategy can be used to phase an entire genome. Here, we introduce a TELL-Seq protocol developed for targeted applications, enabling the phasing of enriched loci of varying sizes, purity levels, and heterozygosity. To validate this protocol, we phased 2-200 kb loci enriched with different methods: CRISPR/Cas9-mediated excision coupled with pulse-field electrophoresis for the longest fragments, CRISPR/Cas9-mediated protection from exonuclease digestion for mid-size fragments, and long PCR for the shortest fragments. All selected loci have known clinical relevance: BRCA1, BRCA2, MLH1, MSH2, MSH6, APC, PMS2, SCN5A-SCN10A, and PKI3CA. Collectively, the analyses show that TELL-Seq can accurately phase 2-200 kb targets using a short-read sequencer.

Algorithms and machine learning models are being used more frequently in educational settings, but there are concerns that they may discriminate against certain groups. While there is some research on algorithmic fairness, there are two main issues with the current research. Firstly, it often focuses on gender and race and ignores other groups. Secondly, studies often find algorithmic bias in educational models but don't explore ways to reduce it. This study evaluates three drop-out prediction models used in an online learning platform to teach German spelling skills. The aim is to assess the fairness of the models for (in part) less-studied demographic groups, including first spoken language, home literacy environment, parental education background, and gender. To evaluate the models, four fairness metrics are used: predictive parity, equalized odds, predictive equality, and ABROCA. The study also examines ways to reduce algorithmic bias by analyzing the models at each stage of the machine learning process. The results show that all three models had biases that affected the fairness of all four demographic groups to varying degrees. However, the study found that most biases could be mitigated during the process. The methods used to mitigate bias differed by demographic group, and some methods improved fairness for one group but worsened it for others. Therefore, the study concludes that reducing algorithmic bias for lessstudied demographic groups is possible, but finding the right method for each algorithm and demographic group is crucial.

In order to cope with the COVID-19 pandemic, many schools have been closed for several months as of March 2020 in Germany. The unplanned and rapid shift to distance learning formats has led to fears that extensive learning deficits will be created and educational inequality will be further exacerbated. This paper, therefore, investigates the impact of the COVID-19 pandemic on spelling skills (capitalisation, sounds and letters, separate and compound spelling and punctuation) of students in German-speaking countries. For this purpose, we use granular learning process data from an online learning platform for spelling competences with over 1 million solved exercise sets. We compare successful completion of exercise sets before the pandemic and after the first wave of the pandemic and estimate personal competence by a Rasch model. The result shows a loss of competence in 2020 and an increase in inequality in some grade levels and spelling domains. A linear model cannot confirm an overarching loss of competence.

In this paper, I assess labor market returns of a substantial skill upgrade: college enrollment of the vocationally trained, non-traditional students who do not have the formal entry requirement. Using propensity-score-adjusted regressions and the National Educational Panel Study, I find that these enrollees face high opportunity costs as they forgo earnings during the enrollment period. In the long-run, enrollees tend to obtain higher cumulative earnings than those who continue with a vocational-training-based career, but, there is a large degree of uncertainty. On the positive side, enrollees attain jobs with a higher reputation in society, hinting at sizable non-monetary returns.

Vocabulary plays an important part in second language learning and there are many existing techniques to facilitate word acquisition. One of these methods is code-switching, or mixing the vocabulary of two languages in one sentence. In this paper the authors propose an experimental system for computer-assisted English vocabulary learning in context using a code-switching based approach for Japanese learners. First they introduce the CO-MIX system, an English vocabulary teaching system that uses code-switching for vocabulary acquisition. Next, they show how they utilize incidental learning techniques with graded readers to increase language proficiency. The authors present the system architecture, underlying technologies, and evaluate the system's performance through user interaction with both a baseline and the proposed system by using a semantic differential scale. They also perform separate factor analysis of participants' attitudes for both systems, an analysis of users' mistakes and compare users' language tests scores. Finally, the authors discuss the evaluation results and further development of this technology.

This article examines the concept of affect in Shakespeare's public theaters, specifically focusing on the relationship between staged emotions, individual emotional responses, and collective feelings in audiences. The author suggests that understanding affect as fluid dynamics can help us understand how emotions manifest in the complex space of the public theater. The article also discusses the historical and cultural context of fluid dynamics in early modern Europe, emphasizing the interconnectedness of technical knowledge, popular imagination, and everyday life. By studying affective dynamics rather than emotional content, the author argues that we can gain insights into the forces that drive movement and thought. Examples from Shakespeare's plays are used to illustrate the complex and often ambiguous nature of affective responses in audiences. The article explores the idea of affective transmission in early modern theater audiences, arguing that the crowded and turbulent nature of the theater created a collective and fluid experience of emotion. It draws on historical and scientific research to support this perspective, highlighting the physical and physiological aspects of audience engagement. The article suggests that understanding the dynamics of affect in the theater requires a fluid-dynamic perspective that considers both social and material factors. [Extracted from the article]