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Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.

Research on diagnosing in teacher education has primarily emphasized the accuracy of diagnostic judgments and has explained it in terms of factors such as diagnostic knowledge. However, approaches to scientific argumentation and information processing suggest differentiating between diagnostic judgment and diagnostic argumentation: When making accurate diagnostic judgments, the underlying reasoning can remain intuitive, whereas diagnostic argumentation requires controlled and explicable reasoning about a diagnostic problem to explain the reasoning in a comprehensible and persuasive manner. We suggest three facets of argumentation for conceptualizing diagnostic argumentation, which are yet to be addressed in teacher education research: justification of a diagnosis with evidence, disconfirmation of differential diagnoses, and transparency regarding the processes of evidence generation. Therefore, we explored whether preservice teachers’ diagnostic argumentation and diagnostic judgment might represent different diagnostic skills. We also explored whether justification, disconfirmation, and transparency should be considered distinct subskills of preservice teachers’ diagnostic argumentation. We reanalyzed data of 118 preservice teachers who learned about students’ learning difficulties with simulated cases. For each student case, the preservice teachers had to indicate a diagnostic judgment and provide a diagnostic argumentation. We found that preservice teachers’ diagnostic argumentation seldom involved all three facets, suggesting a need for more specific training. Moreover, the correlational results suggested that making accurate diagnostic judgments and formulating diagnostic argumentation may represent different diagnostic skills and that justification, disconfirmation, and transparency may be considered distinct subskills of diagnostic argumentation. The introduced concepts of justification, disconfirmation, and transparency may provide a starting point for developing standards in diagnostic argumentation in teacher education.

Zusammenfassung. Hintergrund: Die Rolle und der Einsatz von künstlicher Intelligenz (KI) und Machine Learning im Lernkontext wird seit der Digitalisierungsoffensive stark diskutiert. KI-basierte Werkzeuge bieten vielversprechende Möglichkeiten, um Lernprozesse besser zu verstehen und im weiteren Fortgang zu optimieren. Der Zuwachs an Daten und Metriken, die durch neue Sensoren und digitale Lernumgebungen zur Verfügung gestellt werden, ermöglicht den Einsatz von KI und Machine Learning. Solche Methoden bieten sich insbesondere an, wenn die menschliche Informationsverarbeitung nicht in der Lage ist, die komplexen und multimodalen Daten effizient und zeitnah zu verarbeiten sowie notwendige Schlüsse daraus zu ziehen. Methode: Für den aktuellen Artikel wurde eine selektive Literaturrecherche betrieben, um die unterschiedlichen Schritte bei KI-gestützten Lernsystemen genauer zu beleuchten. Diskussion: Es lassen sich drei zentrale Schritte im Prozess des KI-gestützten Lernens identifizieren: (i) Datenaufzeichnung, (ii) Mustererkennung und (iii) Adaptivität in digitalen Lernumgebungen. Der Mensch spielt in KI-gestützten Lernsystemen eine entscheidende und nicht durch die „Maschine“ zu ersetzende Rolle, die im aktuellen Artikel genauer erläutert wird. Zudem werden weitere wichtige Aspekte für die Umsetzung von KI-gestützten Lernsystemen wie die Notwendigkeit interdisziplinärer Zusammenarbeit, Theoriearbeit und der Schließung von Forschungslücken perspektivisch diskutiert und elaboriert.

Background: Occipital strokes often cause permanent homonymous hemianopia leading to significant disability. In previous studies, non-invasive electrical brain stimulation (NIBS) has improved vision after optic nerve damage and in combination with training after stroke. Objective: We explored different NIBS modalities for rehabilitation of hemianopia after chronic stroke. Methods: In a randomized, double-blinded, sham-controlled, three-armed trial, altogether 56 patients with homonymous hemianopia were recruited. The three experiments were: i) repetitive transorbital alternating current stimulation (rtACS, n = 8) vs. rtACS with prior cathodal transcranial direct current stimulation over the intact visual cortex (tDCS/rtACS, n = 8) vs. sham (n = 8); ii) rtACS (n = 9) vs. sham (n = 9); and iii) tDCS of the visual cortex (n = 7) vs. sham (n = 7). Visual functions were evaluated before and after the intervention, and after eight weeks follow-up. The primary outcome was change in visual field assessed by high-resolution and standard perimetries. The individual modalities were compared within each experimental arm. Results: Primary outcomes in Experiments 1 and 2 were negative. Only significant between-group change was observed in Experiment 3, where tDCS increased visual field of the contralesional eye compared to sham. tDCS/rtACS improved dynamic vision, reading, and visual field of the contralesional eye, but was not superior to other groups. rtACS alone increased foveal sensitivity, but was otherwise ineffective. All trial-related procedures were tolerated well. Conclusions: This exploratory trial showed safety but no main effect of NIBS on vision restoration after stroke. However, tDCS and combined tDCS/rtACS induced improvements in visually guided performance that need to be confirmed in larger-sample trials. NCT01418820 (clinicaltrials.gov)

To slow the spread of COVID-19, many universities have had to move to online teaching, which entails changing exams from in-person to online. Online exams can facilitate cheating when there is no d...

Purpose To advance the learning of professional practices in teacher education and medical education, this conceptual paper aims to introduce the idea of representational scaffolding for digital simulations in higher education. Design/methodology/approach This study outlines the ideas of core practices in two important fields of higher education, namely, teacher and medical education. To facilitate future professionals’ learning of relevant practices, using digital simulations for the approximation of practice offers multiple options for selecting and adjusting representations of practice situations. Adjusting the demands of the learning task in simulations by selecting and modifying representations of practice to match relevant learner characteristics can be characterized as representational scaffolding. Building on research on problem-solving and scientific reasoning, this article identifies leverage points for employing representational scaffolding. Findings The four suggested sets of representational scaffolds that target relevant features of practice situations in simulations are: informational complexity, typicality, required agency and situation dynamics. Representational scaffolds might be implemented in a strategy for approximating practice that involves the media design, sequencing and adaptation of representational scaffolding. Originality/value The outlined conceptualization of representational scaffolding can systematize the design and adaptation of digital simulations in higher education and might contribute to the advancement of future professionals’ learning to further engage in professional practices. This conceptual paper offers a necessary foundation and terminology for approaching related future research.

Recent advancements in artificial intelligence make its use in education more likely. In fact, existing learning systems already utilize it for supporting students’ learning or teachers’ judgments. In this perspective article, we want to elaborate on the role of humans in making decisions in the design and implementation process of artificial intelligence in education. Therefore, we propose that an artificial intelligence-supported system in education can be considered a closed-loop system, which includes the steps of (i) data recording, (ii) pattern detection, and (iii) adaptivity. Besides the design process, we also consider the crucial role of the users in terms of decisions in educational contexts: While some implementations of artificial intelligence might make decisions on their own, we specifically highlight the high potential of striving for hybrid solutions in which different users, namely learners or teachers, are provided with information from artificial intelligence transparently for their own decisions. In light of the non-perfect accuracy of decisions of both artificial intelligence-based systems and users, we argue for balancing the process of human- and AI-driven decisions and mutual monitoring of these decisions. Accordingly, the decision-making process can be improved by taking both sides into account. Further, we emphasize the importance of contextualizing decisions. Potential erroneous decisions by either machines or humans can have very different consequences. In conclusion, humans have a crucial role at many stages in the process of designing and using artificial intelligence for education.

In simulations, pre-service teachers need sophisticated feedback to develop complex skills such as diagnostic reasoning. In an experimental study with N = 178 pre-service teachers about simulated pupils with learning difficulties, we investigated the effects of automatic adaptive feedback, which is based on artificial neural networks, on pre-service teachers’ diagnostic reasoning. Diagnostic reasoning was operationalised as diagnostic accuracy and the quality of justifications. We compared automatic adaptive feedback with static feedback, which we provided in form of an expert solution. Further, we experimentally manipulated whether the learners worked individually or in dyads on the computer lab-based simulations. Results show that adaptive feedback facilitates pre-service teachers’ quality of justifications in written assignments, but not their diagnostic accuracy. Further, static feedback even had detrimental effects on the learning process in dyads. Automatic adaptive feedback in simulations offers scalable, elaborate, process-oriented feedback in real-time to high numbers of students in higher education., This research was supported by a grant of the German Federal Ministry of Research and Education (Grant No.: 16DHL1040) and by the Elite Network of Bavaria (K-GS-2012-209).

Collaborations between researchers and practitioners have recently become increasingly popular in education, and educational design research (EDR) may benefit greatly from investigating such partnerships. One important domain in which EDR on collaborations between researchers and practitioners can be applied is research on simulation-based learning. However, frameworks describing both research and design processes in research programs on simulation-based learning are currently lacking. The framework proposed in this paper addresses this research gap. It is derived from theory and delineates levels, phases, activities, roles, and products of research programs to develop simulations as complex scientific artifacts for research purposes. This dual-level framework applies to research programs with a research committee and multiple subordinate research projects. The proposed framework is illustrated by examples from the actual research and design process of an interdisciplinary research program investigating the facilitation of diagnostic competences through instructional support in simulations. On a theoretical level, the framework contributes primarily to the literature of EDR by offering a unique dual-level perspective. Moreover, on a practical level, the framework may help by providing recommendations to guide the research and design process in research programs.

Eine 40-jahrige Patientin stellt sich in der hausarztlichen Sprechstunde vor. Mit besorgter Miene berichtet sie uber seit sechs Monaten anhaltende allgemeine Schwache und Schweisausbruche. Die Symptomatik hatte sich in den letzten Tagen verstarkt. Sie habe grose Angst, an einer schweren Erkrankung zu leiden.

Reasoning about students who might have behavioral, developmental, or learning disorders is a relevant aspect of teachers’ everyday practice (Reinke et al., Sch Psychol Q 26(1):1, 2011). Therefore, this content area should be part of teacher education. Accordingly, we developed a simulation-based learning environment in which pre-service teachers gather information about six individual students. Learners examine reports about students’ behavior, e.g., in the classroom or at home, and analyze the students’ performance and records of their work. The pre-service teachers’ task is to integrate the given information and draw a diagnostic conclusion for each student. Several design aspects were investigated using the simulation. The most challenging design aspect was to automatically generate adaptive feedback on epistemic diagnostic activities and diagnostic outcomes (Schulz et al., e-teaching. org Themenspecial, Was macht Lernen mit digitalen Medien erfolgreich, 2019). The studies we conduct are replicated in a parallel project in medical education.

In their daily practice, physicians with different professional backgrounds often diagnose a patient’s problem collaboratively. In those situations, physicians not only need to be able to diagnose individually, but also need additional collaborative competences such as information sharing and negotiation skills (Liu et al., 2015), which can influence the quality of the diagnostic outcome (Tschan et al., 2009). We introduce the CDR model, a process model for collaborative diagnostic reasoning processes by diagnosticians with different knowledge backgrounds. Building on this model, we develop a simulation in order to assess and facilitate collaborative diagnostic competences among advanced medical students. In the document-based simulation, learners sequentially diagnose five patients by inspecting a health record for symptoms. Then, learners request a radiological diagnostic procedure from a simulated radiologist. By interacting with the simulated radiologist, the learners elicit more evidence for their hypotheses. Finally, learners are asked to integrate all information and suggest a final diagnosis.

The COVID-19 pandemic has made adolescents´ technology use unavoidable for sustaining teaching and learning processes. In the present paper we conducted two studies using a person-centered latent profile analysis to examine adolescents' technology use before (Study 1

This meta-analysis was conducted to systematically synthesize research findings on effects of gamification on cognitive, motivational, and behavioral learning outcomes. Results from random effects models showed significant small effects of gamification on cognitive (g = .49, 95% CI [0.30, 0.69], k = 19, N = 1686), motivational (g = .36, 95% CI [0.18, 0.54], k = 16, N = 2246), and behavioral learning outcomes (g = .25, 95% CI [0.04, 0.46], k = 9, N = 951). Whereas the effect of gamification on cognitive learning outcomes was stable in a subsplit analysis of studies employing high methodological rigor, effects on motivational and behavioral outcomes were less stable. Given the heterogeneity of effect sizes, moderator analyses were conducted to examine inclusion of game fiction, social interaction, learning arrangement of the comparison group, as well as situational, contextual, and methodological moderators, namely, period of time, research context, randomization, design, and instruments. Inclusion of game fiction and social interaction were significant moderators of the effect of gamification on behavioral learning outcomes. Inclusion of game fiction and combining competition with collaboration were particularly effective within gamification for fostering behavioral learning outcomes. Results of the subsplit analysis indicated that effects of competition augmented with collaboration might also be valid for motivational learning outcomes. The results suggest that gamification as it is currently operationalized in empirical studies is an effective method for instruction, even though factors contributing to successful gamification are still somewhat unresolved, especially for cognitive learning outcomes.

Funding Information: The authors gratefully acknowledge the European Commission for funding the InnoRenew project (Grant Agreement #739574 under the Horizon 2020 Widespread-2-Teaming program), the Republic of Slovenia (investment funding from the Republic of Slovenia and the European Regional Development Fund) and infrastructural ARRS program IO-0035. Part of this work was conducted during project BIO4ever (RBSI14Y7Y4), funded within call SIR by MIUR-Italy; the project Multi-spec (BI-IT/18-20-007), funded by ARRS-Slovenia; Archi-BIO (BI/US-20-054) funded by ARRS-Slovenia, J7-9404 (C) funded by ARRS-Slovenia, and CLICK DESIGN, ?Delivering fingertip knowledge to enable service life performance specification of wood?, (No. 773324) supported under the umbrella of ERA-NET Cofund ForestValue by the Ministry of Education, Science and Sport of the Republic of Slovenia. ForestValue has received funding from the European Union?s Horizon 2020 research and innovation programme. Funding Information: Funding: The authors gratefully acknowledge the European Commission for funding the InnoRenew project (Grant Agreement #739574 under the Horizon2020 Widespread-2-Teaming program), the Republic of Slovenia (investment funding from the Republic of Slovenia and the European Regional Development Fund) and infrastructural ARRS program IO-0035. Part of this work was conducted during project BIO4ever (RBSI14Y7Y4), funded within call SIR by MIUR-Italy; the project Multi-spec (BI-IT/18-20-007), funded by ARRS-Slovenia; Archi-BIO (BI/US-20-054) funded by ARRS-Slovenia, J7-9404 (C) funded by ARRS-Slovenia, and CLICK DESIGN, “Delivering fingertip knowledge to enable service life performance specification of wood”, (No. 773324) supported under the umbrella of ERA-NET Cofund ForestValue by the Ministry of Education, Science and Sport of the Republic of Slovenia. ForestValue has received funding from the European Union’s Horizon 2020 research and innovation programme. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. The service life performance of timber products exposed to natural weathering is a critical factor limiting the broad use of wood as an external building element. The goal of this study was to investigate the in-service characterization of an innovative biofinish coating system. It is a novel surface finishing solution based on the bioinspired concept of living fungal cells designed for effective wood protection. The performance of Scots pine (Pinus sylvestris L.) wood coated with biofinish was compared with uncoated references. Samples were exposed to natural weathering for 12 months under the climatic conditions of northern Italy. The visual appearance, colour, gloss, wettability, and 3D surface topography of the wood surface were examined. Results revealed that the total colour changes (∆E) of biofinish-coated wood were negligible. Untreated Scots pine wood revealed the changes in colour after just three months of exposure. The gloss changes of both surface types were small. The contact angle measured on biofinish-coated wood was higher compared to that of uncoated Scots pine. Surface roughness increased in uncoated wood due to the erosion effect caused by the weathering progress. Conversely, the surface roughness of biofinish-coated samples decreased along the exposure time. This phenomenon was explained by two self-healing mechanisms: migration of non-polymerized oil to the cracked surface, where it polymerizes and creates a closed layer, and local regrowth to cover damaged spots by living fungal cells present in the coating. The obtained results revealed the superior aesthetic performance of the biofinish surface treatment against natural weathering. By considering the fully bio-based nature of the investigated coating, it was concluded that this solution can be an attractive alternative for state-of-the-art wood protection technologies.

ZusammenfassungIm Rahmen des Beitrages werden die Potenziale von Gamification für den Einsatz in der Hochschulbildung verdeutlicht. Neben dem empirischen Stand der Forschung werden die Wirkungsweisen und theoretischen Modelle der Wirkung von Gamification auf Lernerfolg und Motivation skizziert. Des Weiteren werden die einzelnen Spielelemente wie Punkte, Leaderboards, Badges, Performance Graphs, Level-ups, Storytelling und Avatare und deren Wirkungsweise erläutert und auf ihre didaktische Eignung geprüft. Einige Empfehlungen für den Einsatz von Gamification in der Hochschulbildung (Designprinzipien) werden auf Basis der aktuellen empirischen Daten gegeben. Ergänzend wird die HFD Community Working Group „Netzwerk Serious Games und Gamification for Health“ vorgestellt. Der Beitrag verdeutlicht die Wirkungsweisen einzelner Spielelemente, um den Leser*innen zu ermöglichen, selbst zu prüfen, welche Spielelemente in der eigenen Lehre sinnvoll sein könnten.

BACKGROUND Learning with virtual patients is highly popular for fostering clinical reasoning in medical education. However, little learning with virtual patients is done collaboratively, despite the potential learning benefits of collaborative vs. individual learning. OBJECTIVE In this article, we describe the rationale behind the implementation of student collaboration in the CASUS virtual patient platform. METHODS The SimpleWebRTC library of andYet was used to implement the collaborative tool. It provided a basis for the conferencing platform and could be adapted to include features such as video communication and screensharing. An additional text chat was created based on the message protocol of the SimpleWebRTC library. We implemented a user interface for educators to set up and configure the collaboration. Educators can configure video, audio, and text-based chat communication, which are known to promote effective learning. RESULTS We tested the tool in a sample of 137 students working on virtual patients. The study results indicate that students successfully diagnosed 53% (SD = 26%) of the patients when working alone and 71% (SD= 20%) when collaborating using the tool (p < .05, eta2=.12). A usability questionnaire for the study sample shows a usability score of 82.16 (SD = 1.31), a B+ grade. CONCLUSIONS The approach provides a technical framework for collaboration that can be used with the CASUS virtual patient system. Additionally, the application programming interface is generic, so that the setup can also be used with other learning management systems. The collaborative tool helps students diagnose virtual patients and results in a good overall usability of CASUS. Using learning analytics, we are able to track students’ progress in content knowledge and collaborative knowledge and guide them through a virtual patient curriculum designed to teach both. More broadly, the collaborative tool provides an array of new possibilities for researchers and educators alike to design courses, collaborative homework assignments, and research questions for collaborative learning.

A brief survey among members of the German Neurorehabilitation Society aimed to document the hospital capacities (“beds”) for prolonged weaning from a mechanical ventilator for patients with neuro-disabilities that require simultaneous multi-professional neurorehabilitation treatment. Sixty-eight institutions declared to have capacities with a broad distribution across Germany and its federal states. Overall, 1094 “beds” for prolonged weaning (and neurorehabilitation) were reported, 871 together with further information regarding their identification and hence regional location. These units had on average 16.1 beds for prolonged weaning (95% confidence interval 12.6 to 19.6) with a range from 2 to 68 beds per organization. The data indicate substantial capacities for the combined prolonged weaning and neurorehabilitation treatment in Germany. For most “beds” included in this analysis a basic validation was possible. While a reasonable coverage of these specialized service capacities by the survey is likely, the number reported could still be biased by underreporting by non-response. Both the broad variation of number of “beds” for prolonged weaning per unit and their unequal geographical distribution across federal states (per capita rate) warrant a more refined follow-up survey that will provide insights into reasons for the observed pattern of variation for these specialized hospital capacities.

Functional electrical stimulation and robot-assisted gait training are techniques which are used in a clinical routine to enhance the rehabilitation process of stroke patients. By combining these technologies, therapy effects could be further improved and the rehabilitation process can be supported. In order to combine these technologies, a novel algorithm was developed, which aims to extract gait events based on movement data recorded with inertial measurement units. In perspective, the extracted gait events can be used to trigger functional electrical stimulation during robot-assisted gait training. This approach offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. In particular, the aim of this study was to test the robustness of the previously developed algorithm in a clinical setting with patients who suffered a stroke. A total amount of N = 10 stroke patients participated in the study, with written consent. The patients were assigned to two different robot-assisted gait trainers (Lyra and Lokomat) according to their performance level, resulting in five recording sessions for each gait-trainer. A previously developed algorithm was applied and further optimized in order to extract the gait events. A mean detection rate across all patients of 95.8% ± 7.5% for the Lyra and 98.7% ± 2.6% for the Lokomat was achieved. The mean type 1 error across all patients was 1.0% ± 2.0% for the Lyra and 0.9% ± 2.3% for the Lokomat. As a result, the developed algorithm was robust against patient specific movements, and provided promising results for the further development of a technique that can detect gait events during robot-assisted gait training, with the future aim to trigger functional electrical stimulation.

Leaderboards, a key topic in the gamification literature, are often used to enhance student engagement and motivation through social comparison. Previous research has examined the overall effects of leaderboard versus a no leaderboard condition but paid less attention to how positions on different types of leaderboard may affect students' learning performance, intrinsic motivation, and course engagement. In the two studies, we exposed 50 postgraduate students from two fully online courses to two types of leaderboard (absolute and relative) and measured their learning performance, course engagement, intrinsic motivation, and perceptions. Results suggest that the absolute leaderboard helps intensify students’ sense of comparison and competitiveness more than the relative leaderboard. In the absolute leaderboard class, students at different positions showed similar levels of learning performance and course engagement, but a higher position was associated with higher intrinsic motivation. Conversely, in the relative leaderboard class, students ranked in the top third tended to display better learning performance than their peers in the lower two thirds did. Students who ranked in different positions showed similar levels of course engagement and intrinsic motivation for learning. Qualitative analyses based on a survey suggested that the students ranked in the bottom third preferred anonymous (vs public) comparison on an absolute leaderboard but favoured public (vs anonymous) comparison on a relative leaderboard. Most students reported positive attitude toward the use of leaderboards for sustaining comparison and competitiveness.

We investigated how medical students' collaborative diagnostic reasoning, particularly evidence elicitation and sharing, can be facilitated effectively using agent-based simulations. Providing adaptive collaboration scripts has been suggested to increase effectiveness, but existing evidence is diverse and could be affected by unsystematic group constellations. Collaboration scripts have been criticized for undermining learners' agency. We investigate the effect of adaptive and static scripts on collaborative diagnostic reasoning and basic psychological needs. We randomly allocated 160 medical students to one of three groups: adaptive, static, or no collaboration script. We found that learning with adaptive collaboration scripts enhanced evidence sharing performance and transfer performance. Scripting did not affect learners’ perceived autonomy and social relatedness. Yet, compared to static scripts, adaptive scripts had positive effects on perceived competence. We conclude that for complex skills complementing agent-based simulations with adaptive scripts seems beneficial to help learners internalize collaboration scripts without negatively affecting basic psychological needs.

We propose a model of contextual facilitators for learning activities involving technology (in short: C♭-model) for both on-site and distance learning environments in higher education. The C♭-model aims at systematizing research on digital teaching and learning and offers a roadmap for future research to understand the complex dynamic of factors that lead to successful digital teaching and learning in higher education via suitable learning activities. First, we introduce students' learning outcomes as central benchmarks of teaching and learning with digital technologies in higher education. Second, we want to focus on a major proximal factor for students' learning outcomes and thus apply a learning activities perspective. Learning activities involving digital technologies reflect cognitive processes of students when using digital technologies and are causally connected with students' learning outcomes. Third, we highlight several contextual facilitators for learning activities involving technology in the C♭-model: learning opportunities that result from higher education teachers' instructional use of technology and students' self-arranged learning opportunities involving digital technologies. Apart from these proximal facilitators, we include more distal factors, namely, higher education teachers' knowledge, skills, and attitudes toward digital technology; higher education teachers' qualification; students' and teachers’ digital technology equipment; and institutional, organizational, and administrative factors.

We investigated how often teachers apply digital technology in their teaching and which student learning activities teachers initiate. Further, we analyzed factors relating to technology use. 410 teachers in our sample, representative for the state of Bavaria (Germany), reported that they spend a substantial amount of time using digital technologies in a typical lesson. Results indicated that rather teachers’ basic digital skills and technology-related teaching skills than digital technology resources are crucial. Even though a certain threshold level of digital technology is necessary in school, our results suggest shifting the focus from equipping schools to teachers’ skills using technologies effectively.

Background Memory impairment (MI) is a common symptom of MS. Previous studies were conflicting in respect to the possible existence of early MI and the role of hippocampal atrophy. The objective of this study was to investigate MI and structural MRI correlates in homogenous groups of early and late MS, controlling for a potential information-processing speed (IPS) deficit, and utilizing multiple memory test paradigms. Methods 152 individually matched subjects were recruited: early MS (EMS, N = 25, disease duration 1.0 ± 0.8 years), late MS (LMS, N = 52, 16.5 ± 5.2 years), and corresponding controls. Five memory tests were utilized to account for differences in learning material (verbal, visual), encoding (incidental, intentional), and retrieval (free recall, recognition, recurring recognition). Performance was related to IPS, memory-specific (hippocampal volumes), and unspecific MRI measures (T1/T2LL, brain volume, cortical thickness). Results Memory was impaired across all tests in LMS, but not in EMS. LMS-patients were also significantly impaired in IPS which was correlated with several memory scores. Regression analyses revealed IPS and cortical thickness as predictors for visual MI, and IPS, sex, and left hippocampal volume as predictors for verbal MI. Conclusion Additionally to direct destructions in memory specific tracts such as the hippocampus, memory decline in MS may also be related to a general factor comprising slowed information-processing and global tissue loss.

ZusammenfassungEine standardisierte Therapie der oberen Extremität bei Patienten mit einer Störung des zentralen Nervensystems (ZNS) liegt bisher nicht vor. In der letzten Dekade hat die roboter- und gerätegestützte Therapie die Behandlung der motorischen Funktionsstörungen der oberen Extremität nach Schlaganfall in der Neurorehabilitation deutlich beeinflusst. Die vorliegende Arbeit gibt einen Überblick zu den technischen Grundlagen und zur Differenzierung der verschiedenen Gerätetypen. Unter Einbeziehung ausgewählter Studien wird die Praxis der roboter- und gerätegestützten Rehabilitation der oberen Extremität dargestellt. Die Effektivität der gerätegestützten Therapie der oberen Extremität ist, im Vergleich zu einer gleichwertig intensiven konventionellen Therapie, weiterhin diskutabel, nicht zuletzt auch aufgrund der recht heterogenen Studienlage. Dennoch weisen die Entwicklung und der Einsatz roboter- und gerätegestützter Therapieformen der oberen Extremität in eine vielversprechende Zukunft. Sie können das Personal entlasten und stellen aktuell eine sinnvolle Ergänzung zur konventionellen Therapie dar.

Neurorehabilitation of patients with upper limb motor dysfunction due to central nervous system damage still lacks adequate standardization. During the last decade, robot- and device-assisted rehabilitation has become more feasible for the treatment of functional disorders of the upper limb after stroke. Here we present an overview of technological aspects and differential use of devices for upper limb rehabilitation as well as a review of relevant clinical studies. We also discuss the potential for standardized evaluation in the context of limited health care resources. The effectiveness of device-assisted therapy, in comparison to conventional approaches, remains a matter of debate, largely due to the heterogeneous design of the available clinical studies. However, we believe that a better understanding of the timing, intensity, and quality of upper limb rehabilitation, as well as technological progress, will lead to the establishment of a central role for robot- and device-assisted rehabilitation in the next decade.

The main aim of gamification, i.e. the implementation of game design elements in real-world contexts for non-gaming purposes, is to foster human motivation and performance in regard to a given activity. Previous research, although not entirely conclusive, generally supports the hypothesis underlying this aim. However, previous studies have often treated gamification as a generic construct, neglecting the fact that there are many different game design elements which can result in very diverse applications. Based on a self-determination theory framework, we present the results of a randomized controlled study that used an online simulation environment. We deliberately varied different configurations of game design elements, and analysed them in regard to their effect on the fulfilment of basic psychological needs. Our results show that badges, leaderboards, and performance graphs positively affect competence need satisfaction, as well as perceived task meaningfulness, while avatars, meaningful stories, and teammates affect experiences of social relatedness. Perceived decision freedom, however, could not be affected as intended. We interpret these findings as general support for our main hypothesis that gamification is not effective per se, but that specific game design elements have specific psychological effects. Consequences for further research, in particular the importance of treatment checks, are discussed. Motivational effects of gamification are analysed from an educational psychology perspective.Gamification is not effective per se, but different game design elements can trigger different motivational outcomes.A self-determination theory framework was used to study the effects of different configurations of game design elements.Competence and autonomy regarding task meaningfulness was affected by badges, leaderboards, and performance graphs.Social relatedness was positively influenced by avatars, a meaningful story, and teammates.

Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data. Diagnosing is an exceptionally difficult skill to obtain but vital for many different professions (e.g., medical doctors, teachers). Previous case simulation systems are limited to multiple-choice questions and thus cannot give constructive individualized feedback on a student's diagnostic reasoning process. Given initially only limited data, we leverage a (replaceable) NLP model to both support experts in their further data annotation with automatic suggestions, and we provide automatic feedback for students. We argue that because the central model consistently improves, our interactive approach encourages both students and instructors to recurrently use the tool, and thus accelerate the speed of data creation and annotation. We show results from two user studies on diagnostic reasoning in medicine and teacher education and outline how our system can be extended to further use cases., Comment: EMNLP 2019 - Demo

Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation. To speed up and ease annotations, we investigate the viability of automatically generated annotation suggestions for such tasks. As an example, we choose a task that is particularly hard for both humans and machines: the segmentation and classification of epistemic activities in diagnostic reasoning texts. We create and publish a new dataset covering two domains and carefully analyse the suggested annotations. We find that suggestions have positive effects on annotation speed and performance, while not introducing noteworthy biases. Envisioning suggestion models that improve with newly annotated texts, we contrast methods for continuous model adjustment and suggest the most effective setup for suggestions in future expert tasks., Comment: To appear in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)

Diagnostic argumentation can be decomposed referring to the dimensions of content (see Toulmin 2003) and explicated strategy use indicated by epistemic activities (see Fischer et al. 2014). We propose a conceptual framework to analyze these two dimensions within diagnostic argumentation and explore its use within initial applications using the method of Epistemic Network Analysis (Shaffer 2017). The results indicate that both approaches of solely analyzing the dimension of content and solely analyzing the dimension of epistemic activities offer less insights into diagnostic argumentations than an analysis that includes both dimensions.

This study investigated the initiation of digitally supported learning activities and personal and institutional factors associated with them in different higher education courses, based on the C♭-model. The C♭-model is a theoretical framework that systematizes contextual factors, which influence students‘ learning activities as the most important facilitator of students’ learning success. Using a self-assessment instrument with anchored scenarios in a sample of 1625 higher education teachers, we were able to identify three levels at which higher education teachers initiated digital learning activities: a low level (powerpointers), a moderate level (clickerers), and a high level (digital pros). The findings also support the relevance of the contextual factors specified in the C♭-model for initiating a high level of digital learning activities, namely digitalization policy and commitment of university administration, institutional equipment, technical and educational support, self-assessed basic digital skills, and self-assessed technology-related teaching skills. All of these factors explain a substantial amount of variance in the level of initiated digital learning activities. We conclude that a comprehensive approach rather than isolated measures might contribute to successful teaching and learning in higher education.

This is the introductory article for the special issue “Technology-related knowledge, skills, and attitudes of pre- and in-service teachers”. It (1) specifies the concept of technology-related knowledge, skills, and attitudes (KSA) of teachers, (2) presents how these KSA are currently assessed, and (3) outlines ways of fostering them among pre- and in-service teachers. The eight articles in the special issue are structured accordingly, and we demonstrate how they contribute to knowledge in these three areas. Moreover, we show how the afterword to the special issue widens the perspective on technology integration by taking into account systems and cultures of practice. Due to their quantitative empirical nature, the eight articles investigate technology at the current state of the art. However, the potential of artificial intelligence has not yet been fully exploited in education. We provide an outlook on potential developments and their implications on teachers’ technology-related KSA. To this end, we introduce the concept of augmentation strategies., Highlights • Review of teachers' technology related knowledge, skills, and attitudes (KSA). • Three areas: specification, assessment, and ways of fostering. • Explanation of contribution of the eight articles in the special issue. • Outlook on KSA changes due to technological developments: artificial intelligence.

Instruments that assess teachers' skills and attitudes on the basis of a broad range of specific standards and demands for teaching with digital technologies are lacking to date. Based on the K19 framework, we validated the scenario-based instrument IN.K19 that simultaneously assesses technology-related teaching skills and attitudes via self-assessment. In our study with N = 90 teachers and student teachers with teaching experience, we demonstrate that the instrument has satisfactory factorial validity in our confirmatory factor analyses. To investigate its predictive validity, we examined the instruments' relationships with teachers' frequency of technology use in class and teachers' initiation of different types of student learning activities involving technology. Results from structural equation modelling show relationships between self-assessed skills in different phases of teaching with technology and the self-reported initiation of student learning activities involving overt actions (active, constructive, and interactive learning activities), supporting the predictive validity of our instrument. Positive attitudes towards technology-related teaching also exhibit positive relationships with the initiation of learning activities involving digital technologies, but more specifically learning activities that do not include observable actions by learners (passive learning activities). Thus, teachers' self-assessed technology-related skills rather than attitudes might contribute to facilitating learning activities crucial for students’ learning.

Measurements of domain knowledge very often use and report Cronbach's alpha or similar indicators of internal consistency for test construction. In this short article, we argue that this approach is often at odds with the theoretical conception of knowledge underlying the measure. While domain knowledge is usually described as a formative construct (formed by the manifest observations) theoretically, the use of Cronbach's alpha to construct and evaluate an empirical measure implies a reflective model (the construct reflects in manifest behaviors). After illustrating the difference between reflective and formative models, we illustrate how this mismatch between theoretical conception and empirical operationalization can have substantial implications for the assessment and modeling of domain knowledge. Specifically, the construct may be operationalized too narrowly or even be misinterpreted by applying criteria for item selection that focus on homogeneity such as Cronbach's alpha. Rather than maximizing items internal consistency, researchers constructing measures of domain knowledge should, therefore, make strong arguments for the theoretical merit of their items even if they are not correlated to each other.

Research in social sciences and humanities of ten involves analysing data to draw scientific conclusions. This however requires the manual coding of the data, which is highly time-consuming. A use case is the coding of students' essays in education to draw conclusions about students' reasoning and argumentation. The NeuralWeb API tackles this problem by providing automatic codings to other software components. These codings can for example be used in annotation platforms in terms of recommendations for expert coders from social sciences and humanities. After some initial manual annotations, the expert coders then merely need to verify the correctness of the automatic codings instead of manually annotating all data.

Although there are several empirical studies about active learning in higher education, there are some open research questions. Especially, it appears to be relevant to find out what type of activity (active, interactive and constructive) has a positive effect on the learning result of participants in task-based activities. In order to answer this question an experimental study was conducted with students in German higher education (N=50). The results do not show a significant difference between active, interactive and constructive activities on learning performance in task-based learning. But all groups have benefited from the classroom activities according to the test results.

Multiple Sklerose (MS) ist eine entzundliche Erkrankung des zentralen Nervensystems, die einen axonal-destruktiven Anteil aufweist, der masgeblich die Behinderung im Laufe der Erkrankung bestimmt. In den letzten 3 Dekaden etablierte sich die Magnetresonanztomografie (MRT) zum wichtigsten Verfahren in der Diagnosestellung der MS. Die hohe Sensitivitat der konventionellen MRT erlaubt die In-vivo-Detektion von fokalen und diffusen entzundlichen Komponenten dieser Erkrankung. Der Nachweis und die Quantifizierung von fokalen Pathologien der in der klinischen Routine eingesetzten MRT tragt entscheidend zur fruhen Diagnose der MS bei. Der Nachweis einer Lasionslast im MRT zu Beginn der Erkrankung erhoht die Wahrscheinlichkeit einer fruhen Konversion zur klinisch definitiven MS und einer hoheren Behinderung in den ersten Jahren der Erkrankung. Dieser diagnostische und prognostische Informationsgewinn zu einem fruhen Zeitpunkt der Erkrankung fuhrte 2001 zu einer grundlegenden Revision der diagnostischen Kriterien. Das Konzept der Diagnosestellung beinhaltet klinische und kernspintomografische Kriterien. Diese zuletzt 2010 revidierten diagnostischen Kriterien weisen das Hauptmerkmal auf, dass eine subklinische kernspintomografische Aktivitat der Erkrankung einen Schub ersetzt. Daruber hinaus kann die Diagnose MS bereits nach dem ersten Schub und einem MRT gestellt werden, vorausgesetzt, es lassen sich kernspintomografische Aktivitatszeichen nachweisen. Die fruhe Diagnosestellung unter Berucksichtigung der MRT eroffnet die Moglichkeit einer fruhzeitigen Behandlung innerhalb eines offenbar gunstigeren therapeutischen Fensters.

Occipital stroke often leads to visual field loss, for which no effective treatment exists. Little is known about the potential of non-invasive electric current stimulation to ameliorate visual functions in patients suffering from unilateral occipital stroke. One reason is the traditional thinking that visual field loss after brain lesions is permanent. Since evidence is available documenting vision restoration by means of vision training or non-invasive electric current stimulation future studies should also consider investigating recovery processes after visual cortical strokes. Here, protocols of repetitive transorbital alternating current stimulation (rtACS) and transcranial direct current stimulation (tDCS) are presented and the European consortium for restoration of vision (REVIS) is introduced. Within the consortium different stimulation approaches will be applied to patients with unilateral occipital strokes resulting in homonymous hemianopic visual field defects. The aim of the study is to evaluate effects of current stimulation of the brain on vision parameters, vision-related quality of life, and physiological parameters that allow concluding about the mechanisms of vision restoration. These include EEG-spectra and coherence measures, and visual evoked potentials. The design of stimulation protocols involves an appropriate sham-stimulation condition and sufficient follow-up periods to test whether the effects are stable. This is the first application of non-invasive current stimulation for vision rehabilitation in stroke-related visual field deficits. Positive results of the trials could have far-reaching implications for clinical practice. The ability of non-invasive electrical current brain stimulation to modulate the activity of neuronal networks may have implications for stroke rehabilitation also in the visual domain.

Objective:We investigated corticospinal tract (CST) integrity in the absence of white matter (WM) lesions using diffusion tensor imaging (DTI) in early MS disease stages.Methods:Our study comprised 19 patients with clinically isolated syndrome (CIS), 11 patients with relapsing-remitting MS (RRMS), and 32 age- and sex-matched healthy controls, for whom MRI measures of CST integrity (fractional anisotropy [FA], mean diffusivity [MD]), T1- and T2-based lesion load, and brain volumes were available. The mean (SD) disease duration was 3.5 (2.1) months, and disability score was low (median Expanded Disability Status Scale 1.5) at the time of the study.Results:Patients with CIS and RRMS had significantly lower CST FA and higher CST MD values compared with controls. These findings were present, irrespective of whether WM lesions affected the CST. However, no group differences in the overall gray or WM volume were identified.Conclusions:In early MS disease stages, CST integrity is already affected in the absence of WM lesions or brain atrophy.

The Wellbeing Game uses game design elements to promote wellbeing. Players document their daily activities in the game and categorize them to one or more of five wellbeing-related factors. The users join teams and can create team events to work together and improve their wellbeing status. The present study aims to review the application and the theoretical base of 'TheWellbeing Game', to adapt it to the German context, and to evaluate its health effects in different settings. Additional aims are to analyze the current state of research regarding the links between health, wellbeing, and gamification and to identify crucial game design elements that have to be implemented in the application in order to address the needs of competence, autonomy, and social relatedness according to the self-determination theory. The Wellbeing Game uses game design elements to promote wellbeing. Players document their daily activities in the game and categorize them to one or more of five wellbeing-related factors. The users join teams and can create team events to work together and improve their wellbeing status. The present study aims to review the application and the theoretical base of ‘The Wellbeing Game’, to adapt it to the German context, and to evaluate its health effects in different settings. Additional aims are to analyze the current state of research regarding the links between health, wellbeing, and gamification and to identify crucial game design elements that have to be implemented in the application in order to address the needs of competence, autonomy, and social relatedness according to the self-determination theory.