This research explores whether the rapid digital transformation due to COVID-19 managed to close or exacerbate the digital divide concerning users' digital skills. We conducted a pre-registered survey with N = 1143 German Internet users. Our findings suggest the latter: younger, male, and higher educated users were more likely to improve their digital skills than older, female, and less educated ones. According to their accounts, the pandemic helped Internet users improve their skills in communicating with others by using video conference software and reflecting critically upon information they found online. These improved digital skills exacerbated not only positive (e.g., feeling informed and safe) but also negative (e.g., feeling lonely) effects of digital media use during the pandemic. We discuss this research's theoretical and practical implications regarding the impact of challenges, such as technological disruption and health crises, on humans' digital skills, capabilities, and future potential, focusing on the second-level digital divide.

Students often struggle with basic programming tasks after their first programming course. Adaptive tutoring systems can support students’ practice by generating tasks, providing feedback, and evaluating students’ progress in real-time. Here, we describe the first step for building such a system focusing on designing tasks that address common errors and misconceptions. To that end, we compiled a collection of Python tasks for novices. In particular, a) we identified errors occurring during introductory programming and mapped them to learning tasks; b) we conducted a survey to validate our mapping; c) we conducted semi-structured interviews with instructors to understand potential reasons for such errors and best practices for addressing them. Synthesizing our findings, we discuss the creation of a tasks’ corpus to serve as a basis for adaptive tutors. This work contributes to the standardization and systematization of computing education and provides insights regarding the design of learning tasks tailored to addressing errors.

This study explores the role of motor activity and tactile perception in Multimodal Immersive Learning Environments (MILEs) within the context of handwriting. A 2x2 factorial experimental design was used to investigate the impact of the intensity of motor activity and the sensitivity of tactile perception on learning performance, measured as memory recall. Mental effort and perceived workload were monitored as the mediating variables. Participants (N=20) completed a handwriting task, that is, copying text displayed on a prompter using a tablet and a stylus. During the task, the participants used additional pressure to increase the intensity of the motor activity and/or wore gloves to reduce the sensitivity of tactile perception. Results indicate no significant effect of either manipulation on recall, mental effort, or perceived workload. This may suggest that integrating supplementary haptic feedback technologies in MILEs does not impose additional cognitive load or obstruct learning. The findings contribute to the design of MILEs by informing the effective integration of wearable sensors to support authentic practice for skill acquisition. The study can inform future research that explores the effects of haptic senses and their broader applications in other learning contexts, contributing to a deeper understanding of embodied learning and dual-coding theory.

We explore the impact of Casual Affective Triggers (CAT) on response rates of online surveys. As CAT, we refer to objects that can be included in survey participation invitations and trigger participants’ affect. The hypothesis is that participants who receive CAT-enriched invitations are more likely to respond to a survey. We conducted a study where the control condition received invitations without affective triggers, and the experimental condition received CAT-enriched invitations. We differentiated the triggers within the experimental condition: one-third of the population received a personalized invitation, one-third received a picture of the surveyor’s cat, and one-third received both. We followed up with a survey to validate our findings. Our results suggest that CATs have a positive impact on response rates. We did not find CATs to induce response bias.

The provision of feedback in Massive Open Online Courses (MOOCs) is a non-trivial task for instructors. Current Learning Analytics (LA) solutions provide opportunities for scaling up such feedback strategies. Yet, most of these solutions lack course contextualization and guidance for MOOC instructors. This paper presents a conceptual framework and a web-based tool aimed at supporting instructors in the design and enactment of feedback strategies within MOOC learning designs. Both the framework and the tool are based on a structured process and include a set of catalogues facilitating the reflection and (re)design of these strategies. Additionally, this paper reports an evaluation of the framework catalogues and the usefulness of the framework process in an online workshop (N=11). As part of this ongoing work, we also explored the experience of participants with the tool. The results are positive, highlighting the usefulness of the framework catalogues, especially for non-experienced MOOC instructors. We envision that the initial evidence gathered can contribute to research on conceptual and technological tools supporting feedback design, and to promote the synergy among LA and human-centered approaches in MOOC settings.

Abstract People can learn a lot through (collaborative) reflection at work: In organizations, staff debate experiences and due to issues every day, thus reflecting together and learning from each other. While this is desirable, it is often hindered by differences in time and space. Online discussions in community-like systems may provide a means to overcome this issue and enable staff to share experiences and learn from them. In this paper, we study two different data sets from two software systems to support online collaborative reflection in order to analyze what possible factors influence the occurrence of aspects of reflection in online discussions, like experiences, suggestions and learning. Our results include findings that partially approve existing models of (collaborative) reflection, but also others that add to or even challenge these models. Overall, we found that collaborative reflection may take different paths towards learning. From this we derive that facilitation mechanisms need to take up these paths, and that facilitation may be successful in different ways than anticipated from existing literature. We describe possible facilitation mechanisms and discuss their implementation.

Time-based events, such as hackathons and codefests, have become a global phenomenon attracting thousands of participants to hundreds of events every year. While research on hackathons has grown considerably, there is still limited insight into what happens to hackathon projects after the event itself has ended. While case studies have provided rich descriptions of hackathons and their aftermath, we add to this literature a large-scale quantitative study of continuation across hackathons in a variety of domains. Our findings indicate that a considerable number of projects get continued after a hackathon has ended. Our results also suggest that short- and long-term continuation are different phenomena. While short-term continuation is associated with technical preparation, number of technologies used in a project and winning a hackathon, long-term continuation is predicated on skill diversity among team members, their technical capabilities in relationship to the technologies and their intention to expand the reach of a project. Moreover, we found intensive short-term activity to be associated with a lower likelihood of long-term project continuation.

In this paper, we explore how we can model students' response times to predict student performance in Intelligent Tutoring Systems. Related research suggests that response time can provide information with respect to correctness. However, time is not consistently used when modeling students' performance. Here, we build on previous work that indicated that the relationship between response time and student performance is non-linear. Based on this concept, we compare three models: a standard Additive Factors Analysis Model (AFM), an AFM model enhanced with a linear step duration parameter and an AFM model enhanced with a quadratic, step duration parameter. The results of this comparison show that the AFM model that is enhanced with the quadratic step duration parameter outperforms the other models over four different datasets and for most of the metrics we used to evaluate the models in cross validation and prediction.

Organizations conduct series of face-to-face meetings aiming to improve work practices. In these meetings, participants from different backgrounds collaboratively design artifacts, such as knowledge or process maps. Such meetings are orchestrated and carried out by facilitators and the success of the meetings almost solely depends on the experience of the facilitators. Previous research has mainly focused on approaches that support facilitators and participants in the upfront planning of such events. There is however, little guidance for facilitators and participants once a meeting has started. One critical aspect -- among others -- is that during a meeting, the facilitator and participants need to decide for how long the iterative process of discussion and design should continue. We argue that we can provide support for such decisions based on the evolution of artifacts collaboratively created during such meetings. This paper presents a multi-level, multi-method analysis of artifacts based on experts' observations in combination with network analytics. We study the use of automated analytics to assess the evolution of collaboratively created artifacts and to indicate maturity and established consensus of the collaborative practice. We propose a computational approach to support facilitators and participants in deciding when to stop face-to-face meetings.

This paper describes the analysis of the social and semantic structure of discussion forums in massive open online courses (MOOCs) in terms of information exchange and user roles. To that end, we analyse a network of forum users based on information-giving relations extracted from the forum data. Connection patterns that appear in the information exchange network of forum users are used to define specific user roles in a social context. Semantic roles are derived by identifying thematic areas in which an actor seeks for information (problem areas) and the areas of interest in which an actor provides information to others (expertise). The interplay of social and semantic roles is analysed using a socio-semantic blockmodelling approach. The results show that social and semantic roles are not strongly interdependent. This indicates that communication patterns and interests of users develop simultaneously only to a moderate extent. In addition to the case study, the methodological contribution is in combining traditional blockmodelling with semantic information to characterise participant roles.

In this paper, we propose a mathematical model to measure the visual stability of dynamic graph drawings. Our approach was validated through a study, which combined the use of questionnaires and an eye-tracking device. The participants of the study had to track members of a software development community over five periods of time in three different dynamic graph drawings. The results suggest that dynamic graph drawings making use of fixed positions are more efficient in the visual search but are limited in the recognition of single elements. On the other hand, dynamic graph drawings with the minimal changes or a constant structure improve the recognition of single elements but lose efficiency in the visual search.

In this paper we investigate patterns of resource usage for exam preparation based on a resource intensive blended learning course. To this end, we analyzed a blended learning, online course facilitated by Moodle. During the course, the students had to work individually and in teams. Furthermore, they were given access to a broad spectrum of learning resources such as videos, slides, wiki articles and quizzes. The logfiles obtained from Moodle were further processed and analyzed. Our analysis approach is based on association rule as well as sequential pattern mining. The results indicate that students’ activity with respect to resource usage follows common patterns during exam preparation either on the individual or the group level. These patterns also relate to the performance of students and to reflect their prior collaborative experience.

In this paper, we propose a method for the real time evaluation of collaborative activities. The method aims to classify collaborative activities using a memory-based learning model and to evaluate them using a reference set of pre-evaluated activities. The proposed approach uses time series to represent activities and classifies them regarding their similarity. In order to evaluate the results, the method is used as an automatic rater of collaboration quality and the ratings are compared to ratings of human experts.