Student and teacher performance during COVID-19 lockdown:An investigation of associated features and complex interactions using multiple data sources

Due to the COVID-19 pandemic, testing what is required to support teachers and students while subject to forced online teaching and learning is relevant in terms of similar situations in the future. To understand the complex relationships of numerous factors with teaching during the lockdown, we used administrative data and survey data from a large Danish university. The analysis employed scores from student evaluations of teaching and the students’ final grades during the first wave of the COVID-19 lockdown in the spring of 2020 as dependent targets in a linear regression model and a random forest model. This led to the identification of linear and non-linear relationships, as well as feature importance and interactions for the two targets. In particular, we found that many factors, such as the age of teachers and their time use, were associated with the scores in student evaluations of teaching and student grades, and that other features, including peer interaction among teachers and student gender, also exerted influence, especially on grades. Finally, we found that for non-linear features, in terms of the age of teachers and students, the average values led to the highest response values for scores in student evaluations of teaching and grades

ASPECT-BASED SENTIMENT ANALYSIS FOR UNIVERSITY TEACHING ANALYTICS

Aspect-based sentiment analysis (ABSA) is a natural language processing method to analyze sentiments from large amounts of unstructured text in a much more fine-grained manner at the aspect level. In this research work, we apply it to analyze open text replies from surveys regarding online teaching. Like most other educational institutions, Copenhagen Business School (CBS) had to shift to online teaching from one day to the next. Using ABSA, we investigated the impact of this forced online learning experiment on teaching quality in the spring semester of 2020. Our findings reveal that students disliked online teaching due to insufficient information and unadjusted teaching methods. However, students liked its flexibility and possibility to learn at an individual pace. We show that ABSA can extract valuable information in an easily interpretable manner to support teaching and learning processes. Finally, our findings show that ABSA is a valuable tool to analyze unstructured text quantitatively

Fra kaos til læring? I Covid-19’s slipstrøm

Gentagne nedlukninger af universiteterne har skabt store udfordringer for studerende og undervisere. Gennem spørgeskemabaserede evalueringer ser vi på erfaringerne fra SDU og CBS. Vi finder, at de studerendes tilfredshed faldt i foråret 2021, mens tilfredsheden ellers var sammenlignelig med tidligere. Ser vi på fællesfaget Mikroøkonomi var der ingen betydelig ændring i de faktiske karakterer. Særligt præoptagede videoer og quizzer bliver vurderet positivt af såvel undervisere som studerende. Intense erfaringer med digitale redskaber under Covid-19 giver mulighed for nye undervisningsmetoder, dog kræver det fortsat pædagogisk udvikling, hvis dette skal transformeres til læring

From Algorithmic Management to Data- driven Labour Organising. A trade union approach to workplace datafication

The increasing datafication of the workplace is often cast as a means of imposing organisational and managerial control on workers. This reflection note moves beyond this view and coins the term data-driven labour organising to discuss the potential of work- place datafication as a way to inform workers about their working conditions and how to use data to advocate for their collective goals. Forging a research agenda on data-driv- en labour organising, the reflection note engages with the historical roots of Scandinavi- an IS research, particularly the trade union (TU) approach. Mobilising the TU approach as a vantage point for re-imagining research on workplace datafication, the reflection note outlines three emerging research topics critical for shifting the research focus from using data for managerial purposes to using data for labour organising. The reflection note concludes by discussing how the TU tradition also invokes a certain research ethos of prac- tical and political engagement, prompting IS researchers to get their hands dirty by actively seeking to reshape the trajectory of digitalisation through practical engagement

Effects of using coding potential, sequence conservation and mRNA structure conservation for predicting pyrroly-sine containing genes

BACKGROUND: Pyrrolysine (the 22nd amino acid) is in certain organisms and under certain circumstances encoded by the amber stop codon, UAG. The circumstances driving pyrrolysine translation are not well understood. The involvement of a predicted mRNA structure in the region downstream UAG has been suggested, but the structure does not seem to be present in all pyrrolysine incorporating genes. RESULTS: We propose a strategy to predict pyrrolysine encoding genes in genomes of archaea and bacteria. We cluster open reading frames interrupted by the amber codon based on sequence similarity. We rank these clusters according to several features that may influence pyrrolysine translation. The ranking effects of different features are assessed and we propose a weighted combination of these features which best explains the currently known pyrrolysine incorporating genes. We devote special attention to the effect of structural conservation and provide further substantiation to support that structural conservation may be influential – but is not a necessary factor. Finally, from the weighted ranking, we identify a number of potentially pyrrolysine incorporating genes. CONCLUSIONS: We propose a method for prediction of pyrrolysine incorporating genes in genomes of bacteria and archaea leading to insights about the factors driving pyrrolysine translation and identification of new gene candidates. The method predicts known conserved genes with high recall and predicts several other promising candidates for experimental verification. The method is implemented as a computational pipeline which is available on request