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Social presence is an important construct in online group learning. It influences the way how social interaction unfolds online and affects learning and social outcomes. However, what precisely social presence is has been under debate, as presently a plethora of different definitions and measures exist preventing the development of a coherent research field regarding social presence and its defining role in online group learning. To solve the issue, we went back to the original social presence theory as devised by the communication researchers Short et al. (1976) to show that although they had a clear idea of social presence-namely \"realness\" of other persons in the interaction-their definition is ambiguous, not operationalizable, and the measurement of it questionable. We, therefore, disentangled their social presence theory and (1) reformulated the social presence definition to enable an operationalization in line with the previous conceptualization of social presence; (2) departed from the technological determinism of social presence; and (3) identified two other constructs closely linked to social presence, namely, sociability (as a medium attribute) and social space (as a group attribute). By reformulating the definition of social presence and by linking it to social space and sociability, we hope to contribute to a more coherent line of social presence research and to better understand interpersonal communication, group learning, and group dynamics when learning and working together in an online setting.(DIPF/Orig.).

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.

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.

Creativity is an increasingly recognized construct in technology-enhanced learning. However, our understanding of how creativity interacts with the design of online learning environments to affect learning experiences is still limited. For example, do creative students benefit from different learning environment designs than those benefitting their less creative peers? This experimental study ( N  = 187) explores this question by investigating the visual design of a self-paced online learning environment, specifically the degree of visual structure, in relation to students' creativity. Creativity was measured in different ways, along the lines of vocational/study choice, self-reported personality and behavior, and creative production. Students were randomly assigned to either a visually unstructured (experimental group) or a visually highly structured (control group) learning environment. They reported their preference, impulse for activation, and situational motivation after the learning experience. Results indicate interaction effects consistent with the role of creativity in perception and learning. More specifically, creative students reported more motivation after learning in an unstructured environment, whereas non-creative students reported relatively better learning experiences in the highly structured condition. These results contribute to resolving previous conflicting findings from separated studies, yet some ambiguities remain. Results and implications are discussed, and recommendations for future research are laid out.

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.

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.

This article contributes to the debate on the growing number of interdisciplinary study programs in learning and technology, and aims to understand the diversity of programs as well as curricula structure in an international landscape. Scientific fields share their knowledge and recruit young researchers by offering discipline-specific study programs. Thus, study programs are a reflection of the fields they represent. As technology-enhanced learning is considered to be particularly interdisciplinary and heterogenous, it is important to better understand the landscape of study programs that represents the field. This article presents an analysis of master programs in technology-enhanced learning. A systematic review and analysis of master programs offered in English has been conducted and further used as input for hierarchical cluster analysis. The study identified general characteristics, curricula structure, and organization of topics of these programs. Hierarchical cluster analysis and qualitative content analysis helped us to identify the major types of curricular structures and typical topics covered by the courses. Results show that most study programs rely on interdisciplinary subjects in technology-enhanced learning with a considerable number of subjects from education, learning and psychology. Subjects related to technology, information and computer science appear in such programs less frequently.

Communication is a core competence in medical care. Failure of physicians to properly communicate inherent risks of medical interventions has been linked with inadequate training at school. This study analyses a medical curriculum for assessing the content and quality of teaching risk communication to students. A checklist based on the national guidelines of core competencies on risk communication required of physicians was developed. Participant observers surveyed all teaching sessions at a medical school during a semester to record the frequency, characteristics and clinical context used by lectures during classes. Data were analyzed using statistical and descriptive methods to determine the prevalence and quality of teaching content. 231 teaching sessions were surveyed. The inter-rater reliability was 81%. Lecturers mentioned topics of risk communication in 61.5% of teaching sessions (83.7% in surgery, 43.3% in internal medicine) but core biostatistics concepts were not discussed in more than 80% of these sessions. Important topics such as patient safety and preventable diseases were underrepresented. Risk communication was mainly taught in large-group, theoretical sessions and rarely with supplementary teaching material (7.4%). Students asked questions in 15.2% of courses, more often in surgery classes than in internal medicine. Statistical and clinical topics relevant for teaching risk communication to medical students are not only underrepresented but also minimally explained by lecturers. Supplementary material on risk communication is rarely provided to students during classes. High-resource demanding, small-group teaching formats are not necessarily interactive as students ask few questions.

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.

Researchers tasked with understanding the effects of educational technology innovations face the challenge of providing evidence of causality. Given the complexities of studying learning in authentic contexts interwoven with technological affordances, conducting tightly-controlled randomized experiments is not always feasible nor desirable. Today, a set of tools is available that can help researchers reason about cause-and-effect, irrespective of the particular research design or approach. This theoretical paper introduces such a tool, a simple graphical formalism that can be used to reason about potential sources of bias. We further explain how causal graphs differ from structural equation models and highlight the value of explicit causal inference. The final section shows how causal graphs can be used in several stages of the research process, whether researchers plan to conduct observational or experimental research.