Laura Froehlich

Higher distance education opens a wide range of opportunities for students with different socio-demographic backgrounds (e.g., age, gender, native language) and study-related skills (e.g., experience in scientific reading or online learning, previous degrees). Computer-Supported Collaborative Learning (CSCL), where students work together in virtual groups, is a common teaching method in distance education. The current research investigates the consequences of stereotypes for perceivers and targets during the formation and collaboration phases in multi-attributionally diverse CSCL groups by systematically examining the individual level and the CSCL-group level. N = 2,024 students in 286 CSCL groups participated in a longitudinal field study in the introductory module of the BSc Psychology curriculum at a large public distance teaching university in Germany. Analyses of multilevel models corresponding to pre-registered hypotheses replicated the findings of previous research that CSCL groups with high socio-demographic and high task-related diversity showed lower structural integration during collaboration. To explain this at-risk constellation for CSCL groups with highly diverse members, we investigated the role of being a target vs. perceiver of negative stereotypes within the group. Targets experiencing social identity threat reported a lower sense of belonging to the CSCL group and, consequently, lower motivation to initiate and maintain social relations with other group members. Moreover, targets showed higher social identity threat than perceivers, independent of the socio-demographic and task-related diversity of the group. Findings are discussed in terms of social-psychological approaches to microdynamics in CSCL groups and interventions to mitigate effects of diversity and stereotyping in higher distance education.

Stereotype content research identifies groups at risk of being negatively stereotyped. The present research investigated the dimensionality of the facets of social evaluation (ability, assertiveness, friendliness, morality) as well as their sociostructural predictors (status and threat) and consequences (intergroup emotions and behavioral intentions) based on the SCM/BIAS map. We tested our predictions in two different societal domains: occupations and migrants from different countries of origin. Across two preregistered studies (Study 1: N = 720, Study 2: N = 688) with samples representative of the German population regarding age and gender, results revealed comparatively domain-independent predictions of status and threat, but domain-dependent predictions of the facets on the downstream variables. We highlight the value of integrating the facets into the SCM/BIAS map for research on prejudice and discrimination within and across societal domains.

Global challenges such as climate change require cooperation across nations—not only among governments but also among individuals. In an incentivized cross-national study with 6,182 participants from 25 nations, we assessed individuals' prosociality as a core predictor of cooperation toward people from 25 different nations, yielding 625 distinct national dyads. Integrating theoretical approaches, this study examines how both individual perceptions and contextual factors—unique to each national dyad—shape intergroup prosociality. Our findings reveal significant variation in cross-national prosociality beyond ingroup favoritism. Prosociality increased with greater cultural and national stereotype similarity. Individuals from nations with reliable institutions exhibited greater prosociality, and those from wealthier nations were particularly prosocial toward individuals from less wealthy nations. In contrast, prosociality decreased with increasing perceived and actual militarized conflict between nations. These results highlight the importance of incorporating social perceptions and contextual factors that reflect bilateral relationships between nations in theories of global prosociality.

Collective action is a powerful tool for social change and is fundamental to women and girls’ empowerment on a societal level. Collective action towards gender equality could be understood as intentional and conscious civic behaviors focused on social transformation, questioning power relations, and promoting gender equality through collective efforts. Various instruments to measure collective action intentions have been developed, but to our knowledge none of the published measures were subject to invariance testing. We introduce the gender equality collective action intention (GECAI) scale and examine its psychometric isomorphism and measurement invariance, using data from 60 countries ( N = 31,686). Our findings indicate that partial scalar measurement invariance of the GECAI scale permits conditional comparisons of latent mean GECAI scores across countries. Moreover, this metric psychometric isomorphism of the GECAI means we can interpret scores at the country-level (i.e., as a group attribute) conceptually similar to individual attributes. Therefore, our findings add to the growing body of literature on gender based collective action by introducing a methodologically sound tool to measure collective action intentions towards gender equality across cultures.

Educational disparities between traditional and non-traditional student groups in higher distance education can potentially be reduced by alleviating social identity threat and strengthening students’ sense of belonging in the academic context. We present a use case of how Learning Analytics and Machine Learning can be applied to develop and implement an algorithm to classify students as at-risk of experiencing social identity threat. These students would be presented with an intervention fostering a sense of belonging. We systematically analyze the intervention’s intended positive consequences to reduce structural discrimination and increase educational equity, as well as potential risks based on privacy, data protection, and algorithmic fairness considerations. Finally, we provide recommendations for Higher Education Institutions to mitigate risk of bias and unintended consequences during algorithm development and implementation from an ethical perspective.

Educational disparities between traditional and non-traditional student groups in higher distance education can potentially be reduced by alleviating social identity threat and strengthening students’ sense of belonging in the academic context. We present a use case of how Learning Analytics and Machine Learning can be applied to develop and implement an algorithm to classify students as at-risk of experiencing social identity threat. These students would be presented with an intervention fostering a sense of belonging. We systematically analyze the intervention’s intended positive consequences to reduce structural discrimination and increase educational equity, as well as potential risks based on privacy, data protection, and algorithmic fairness considerations. Finally, we provide recommendations for Higher Education Institutions to mitigate risk of bias and unintended consequences during algorithm development and implementation from an ethical perspective.

Since 1969, Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) has been classified as a neurological disease in the International Classification of Diseases by the World Health Organization. Although numerous studies over time have uncovered organic abnormalities in patients with ME/CFS, and the majority of researchers to date classify the disease as organic, many physicians still believe that ME/CFS is a psychosomatic illness. In this article, we show how detrimental this belief is to the care and well-being of affected patients and, as a consequence, how important the education of physicians and the public is to stop misdiagnosis, mistreatment, and stigmatization on the grounds of incorrect psychosomatic attributions about the etiology and clinical course of ME/CFS.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe chronic illness and patients with ME/CFS are often medically underserved in Germany and other countries. One contributing factor is health professionals’ lack of knowledge about epidemiology, diagnostic criteria, and treatment of ME/CFS. Opportunities are scarce for health professionals to receive continuing medical education on ME/CFS. The current research addressed this need for further education and investigated the gain of knowledge from a webinar for German-speaking health professionals. In two studies (total sample: N = 378), participants in the intervention condition completed a knowledge test twice (before and after webinar participation). Study 2 also included a waiting-list control condition with repeated response to the knowledge test without webinar participation between measurements. Results showed that at baseline, most participants had seen patients with ME/CFS, but confidence in diagnosing and treating ME/CFS was only moderate-to-low. In the intervention condition, but not in the control condition, knowledge about ME/CFS increased between the first and the second knowledge test. These results indicate that the webinar was successful in increasing health professionals’ knowledge about ME/CFS. We concluded that webinars can be a cost-efficient and effective tool in providing health professionals with large-scale continuing medical education about ME/CFS.