Transactional distance (TD), the perception of psychological distance between the student and his peers, his instructor/teacher, and the learning content, has long been a prominent construct in research on distance education. Today, distance education primarily takes place over the internet, with technology mediating engagement and communication. Because transactional distance in online distance learning will always rely on technologically-mediated communication or interaction, we argue that in order to get the full picture, this aspect of technological mediation needs to be considered. For this purpose, we introduce a new scale for measuring transactional distance between students and the learning technology (TDSTECH), comprised of two interrelated dimensions. Reliability, convergent, and discriminant validity suggest a suitable scale. Preliminary inferential analyses are conducted with multiple linear regression and mediation analysis. Regression models show that TDSTECH is the single most important predictor of satisfaction in this population. This may have important implications for practitioners trying design and facilitate satisfying online distance learning experiences. Also, mediator analysis reveals that TDSTECH mediates the relationship between TD student-teacher and satisfaction, but not for TD student-content. Surprisingly, TD student-student shows no significant relationship with satisfaction. Implications for practice and further research are discussed.

Gamification has gained a lot of attention in recent years as a possible way to foster students’ motivation and learning behavior. As a high drop-out rate is associated with distance learning, in particular with students often struggling to engage with the material, the implementation of gamification may support and enhance more successful online learning. A distance learning Bachelor degree class was selected as a case study to investigate the implementation of a Moodle-based gamification concept as well as different variables associated in using a mixed-methods-approach. Eight students were interviewed and 32 participated in an online survey. Significant positive correlations were found between students’ online learning readiness in the dimension of technical competencies and both types of autonomous motivation (identified and intrinsic motivation). A significant positive correlation was also found between self-reported attitudes towards gaming and the dimension of coping of study-satisfaction. As expected, students who indicated rather low online learning readiness tended to show non-autonomous motivation (amotivation). Surprisingly, some students reported autonomous motivation, despite having expressed a rather dismissive attitude towards playing online and computer games in general. Acquiring digital badges reportedly felt like appreciation directly awarded by the students’ instructor. Progress bars were positively evaluated and were accepted as a management tool for individual learning strategies.

The concept of Social Presence is often cited by researchers trying to understand the mechanisms governing beneficial learning climates and interpersonal connections among online learners. However, convoluted definitions and problematic measurements of social presence have made it difficult to understand how exactly social presence and related social variables come to be, hindering the investigation of reliable design recommendations. This study attempts to advance the SIPS (Sociability, Social Interaction, Social Presence, Social Space) model. It shows how these variables are related to each other and to relevant outcome variables like satisfaction. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to assess the predictive capabilities of the model regarding the outcome variables. Results support the notion that a sociable learning environment fosters social interaction, leading to social presence and the emergence of a sound social space, in turn explaining the quality of the learning experience. Social presence, when measured in a non-convoluted way, has no effect on satisfaction. The SIPS model is predictive of satisfaction, perceived learning, and confidence.The sociability of the learning environment is predictive of social interaction.Social presence and social space are related but distinct constructs.Relationship of social presence to satisfaction is questionable.Social space and perceived learning are the main drivers of satisfaction.

Quality feedback is essential for supporting student learning in higher education, yet personalized feedback at scale remains costly. Advances in learning analytics and artificial intelligence now enable the automated delivery of personalized feedback to many students simultaneously. At the same time, recent feedback research increasingly emphasizes learner-centered approaches, particularly the role of feedback literacy—students' varying capacities to engage with and benefit from feedback. Despite growing interest, few studies have quantified how feedback literacy affects students' perceptions of feedback, especially in technology-supported contexts. To address this, we examined (1) students' perceptions of personalized, detailed feedback generated via learning analytics and (2) how feedback literacy moderated these perceptions. In a randomized field experiment, teacher education students (N = 196) participated in a week-long computer-supported collaborative learning task on cognitive activation in the classroom. Both groups received automated, personalized feedback: the control group received basic feedback on task completion, while the experimental group received detailed feedback on group processes and the quality of their collaborative statement. The highly informative feedback significantly improved perceptions of feedback helpfulness, enhanced learning insights, and supported self-reflection and self-regulation. Feedback literacy partially moderated these effects, influencing perceptions of feedback helpfulness and motivational regulation.

In 2020, Higher Education institutions were pressed to swiftly implement online-based teaching. Among many challenges associated with this, lecturers in Higher Education needed to promptly and flexibly adapt their teaching to these circumstances. This investigation adopts a resilience framing in order to shed light on which specific challenges were associated with this sudden switch and what helped an international sample of Higher Education lecturers (N = 102) in coping with these challenges. Results suggest that Emergency Remote Teaching was indeed challenging and quality of teaching was impeded but these effects are more nuanced than expected. Lecturers displayed instructional resilience by maintaining teaching quality despite difficulties of Emergency Remote Teaching and our exploration of predictors shows that personality factors as well as prior experience may have supported them in this. Our findings may contribute to the emerging literature surrounding Emergency Remote Teaching and contributes a unique resilience perspective to the experiences of Higher Education lecturers.