The Covid-19 pandemic has raised significant challenges for the higher education community worldwide. A particular challenge has been the urgent and unexpected request for previously face-to-face university courses to be taught online. Online teaching and learning imply a certain pedagogical content knowledge (PCK), mainly related to designing and organising for better learning experiences and creating distinctive learning environments, with the help of digital technologies. With this article, we provide some expert insights into this online-learning-related PCK, with the goal of helping non-expert university teachers (i.e. those who have little experience with online learning) to navigate in these challenging times. Our findings point at the design of learning activities with certain characteristics, the combination of three types of presence (social, cognitive and facilitatory) and the need for adapting assessment to the new learning requirements. We end with a reflection on how responding to a crisis (as best we can) may precipitate enhanced teaching and learning practices in the postdigital era.

Transcript analysis is an important methodology to study asynchronous online educational discourse. The purpose of this study is to revisit reliability and validity issues associated with transcript analysis. The goal is to provide researchers with guidance in coding transcripts. For validity reasons, it is suggested that the first step is to select a sound theoretical model and coding scheme. Particular focus is placed on exploring the advantages of the option of a negotiated approach to coding the transcript. It is concluded that researchers need to consider the advantages of negotiation when coders and researchers are not familiar with the coding scheme.

The Covid-19 pandemic has presented an opportunity for rethinking assumptions about education in general and higher education in particular. In the light of the general crisis the pandemic caused, especially when it comes to the so-called emergency remote teaching (ERT), educators from all grades and contexts experienced the necessity of rethinking their roles, the ways of supporting the students’ learning tasks and the image of students as self-organising learners, active citizens and autonomous social agents. In our first Postdigital Science and Education paper, we sought to distil and share some expert advice for campus-based university teachers to adapt to online teaching and learning. In this sequel paper, we ask ourselves: Now that campus-based university teachers have experienced the unplanned and forced version of Online Learning and Teaching (OLT), how can this experience help bridge the gap between online and in-person teaching in the following years? The four experts, also co-authors of this paper, interviewed aligning towards an emphasis on pedagogisation rather than digitalisation of higher education, with strategic decision-making being in the heart of post-pandemic practices. Our literature review of papers published in the last year and analysis of the expert answers reveal that the ‘forced’ experience of teaching with digital technologies as part of ERT can gradually give place to a harmonious integration of physical and digital tools and methods for the sake of more active, flexible and meaningful learning.

The Framework for the Rational Analysis of Mobile Education (FRAME) model was originally developed as a basis for assessing the effectiveness of mobile devices for distance learning [1]. The FRAME model is the first comprehensive theoretical model to describe mobile learning as a process resulting from the convergence of mobile technologies, human learning capacities, and social interaction. It addresses contemporary pedagogical issues of information overload, knowledge navigation, and collaborative learning. It is hoped that this model will help to guide the development of future mobile devices, the development of learning materials destined for mobile learning, and the specification of teaching and learning strategies for mobile education

We give an approximation method for analyzing the performance of call centers with skill-based routing, for both blocking and delay systems. We use this method to determine optimal skill sets for call center employees.