Adoption of learning technologies in times of pandemic crisis

An introduction is presented in which the editor discusses the topics within the issue, including emergency remote teaching (ERT), the motivation of new students versus advanced students, and the structural equation modelling method.

Virtual academic conferences as learning spaces: Factors associated with the perceived value of purely virtual conferences

Background Objectives Methods Results and Conclusion The COVID‐19 outbreak came with an unprecedented opportunity to investigate how the new reality of social distancing and limited international travel will affect the organization of academic conferences.Drawing on conceptualization of academic conferences as professional learning spaces, in this study, we examine the factors associated with the perceived value of purely virtual academic conferences and how such perceptions differ between participants from different research fields. The aim was to gain knowledge about factors that should be considered when designing a virtual conference.Survey data from participants of three different virtual conferences were collected (N = 311). Kendall's rank correlation and χ2‐analyses were performed.Results show satisfaction with social interaction, the extent to which presentations met participants' topics of interest and the perceived importance of learning and getting an overview on the research topic to be related to the value rating. Researchers from different research fields differ significantly in their opinion about the most appropriate conference format regarding getting an overview on the research topic. For some researchers, virtual participation might be a valuable alternative to attending a conference in person. The study serves as a first attempt to understand how and for which target groups virtual conferences serve as a valuable learning event. Further research on this conference format is needed. [ABSTRACT FROM AUTHOR] Copyright of Journal of Computer Assisted Learning is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mobile Sensing with Smart Wearables of the Physical Context of Distance Learning Students to Consider Its Effects on Learning.

Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners' physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners' smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner.