Learning in the Metaverse: Are University Students Willing to Learn in Immersive Virtual Reality?

Immersive virtual reality (IVR) technology provides several educational affordances that make it a valuable tool for learning, especially from a constructivist learning perspective. Combined with the increasing availability of Metaverse social platforms, such as ENGAGE and AltSpace VR, where students and teachers can meet and work together, IVR may transform how students learn and interact with educational content. However, little is known about students' attitudes toward IVR in education. To address this gap, we surveyed 329 undergraduate students from different universities in Italy. We used the Unified Theory of Acceptance and Use of Technology (UTAUT) to predict students' intention to adopt IVR for learning. We further explored the role that different individual factors, including students' learning styles, affordances perceptions, and personal innovativeness, have on their attitudes toward IVR. A hierarchical multiple regression analysis revealed that the four constructs of the UTAUT, namely performance expectancy, effort expectancy, social influence, and facilitating conditions were the strongest predictors of students' intention to use IVR in education and that individual factors only had little impact on it. Based on these results, this study provides helpful indications for researchers and educators who wish to introduce IVR effectively in educational contexts. Given the new possibilities provided by Metaverse applications based on IVR technology for learning, it is indeed crucial to fully understand the attitudes different stakeholders in education have toward adopting this technology in educational contexts.

LEXpander: Applying colexification networks to automated lexicon expansion.

Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually curated sets of seed words. Despite the wide use of this approach, we still lack an exhaustive comparative analysis of the performance of lexicon expansion methods and how they can be improved with additional linguistic data. In this work, we present LEXpander, a method for lexicon expansion that leverages novel data on colexification, i.e., semantic networks connecting words with multiple meanings according to shared senses. We evaluate LEXpander in a benchmark including widely used methods for lexicon expansion based on word embedding models and synonym networks. We find that LEXpander outperforms existing approaches in terms of both precision and the trade-off between precision and recall of generated word lists in a variety of tests. Our benchmark includes several linguistic categories, as words relating to the financial area or to the concept of friendship, and sentiment variables in English and German. We also show that the expanded word lists constitute a high-performing text analysis method in application cases to various English corpora. This way, LEXpander poses a systematic automated solution to expand short lists of words into exhaustive and accurate word lists that can closely approximate word lists generated by experts in psychology and linguistics.

LEIA: Linguistic Embeddings for the Identification of Affect.

The wealth of text data generated by social media has enabled new kinds of analysis of emotions with language models. These models are often trained on small and costly datasets of text annotations produced by readers who guess the emotions expressed by others in social media posts. This affects the quality of emotion identification methods due to training data size limitations and noise in the production of labels used in model development. We present LEIA, a model for emotion identification in text that has been trained on a dataset of more than 6 million posts with self-annotated emotion labels for happiness, affection, sadness, anger, and fear. LEIA is based on a word masking method that enhances the learning of emotion words during model pre-training. LEIA achieves macro-F1 values of approximately 73 on three in-domain test datasets, outperforming other supervised and unsupervised methods in a strong benchmark that shows that LEIA generalizes across posts, users, and time periods. We further perform an out-of-domain evaluation on five different datasets of social media and other sources, showing LEIA's robust performance across media, data collection methods, and annotation schemes. Our results show that LEIA generalizes its classification of anger, happiness, and sadness beyond the domain it was trained on. LEIA can be applied in future research to provide better identification of emotions in text from the perspective of the writer.

Collective emotions during the COVID-19 outbreak.

The COVID-19 pandemic has exposed the world's population to unprecedented health threats and changes to social life. High uncertainty about the novel disease and its social and economic consequences, together with increasingly stringent governmental measures against the spread of the virus, likely elicited strong emotional responses. We analyzed the digital traces of emotional expressions in tweets during 5 weeks after the start of outbreaks in 18 countries and six different languages. We observed an early strong upsurge of anxiety-related terms in all countries, which was related to the growth in cases and increases in the stringency of governmental measures. Anxiety expression gradually relaxed once stringent measures were in place, possibly indicating that people were reassured. Sadness terms rose and anger terms decreased with or after an increase in the stringency of measures and remained stable as long as measures were in place. Positive emotion words only decreased slightly and briefly in a few countries. Our results reveal some of the most enduring changes in emotional expression observed in long periods of social media data. Such sustained emotional expression could indicate that interactions between users led to the emergence of collective emotions. Words that frequently occurred in tweets suggest a shift in topics of conversation across all emotions, from political ones in 2019, to pandemic related issues during the outbreak, including everyday life changes, other people, and health. This kind of time-sensitive analyses of large-scale samples of emotional expression have the potential to inform risk communication. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Gratitude and Social Media: A Pilot Experiment on the Benefits of Exposure to Others' Grateful Interactions on Facebook.

Facebook and other social networking sites allow observation of others' interactions that in normal, offline life would simply be undetectable (e.g., a two-voice conversation viewable on the Facebook wall, from the perspective of a real, silent witness). Drawing on this specific property, the theory of social learning, and the most direct implications of emotional contagion, our pilot experiment (N = 49) aimed to test whether the exposure to others' grateful interactions on Facebook enhances (a) users' felt gratitude, (b) expressed gratitude, and (c) their subjective well-being. For the threefold purpose, we created ad hoc Facebook groups in which the exposure to some accomplices' exchange of grateful messages for 2 weeks was experimentally manipulated and users' felt/expressed gratitude and well-being were consequently assessed. Results partially supported both hypotheses. Observing others' exchange of grateful posts/comments on Facebook appeared to enhance participants' in-person expression of gratitude (i.e., self-reported gratitude expression within face-to-face interactions), but not their direct and subjective experiences of gratitude. Similarly, exposure to others' grateful messages improved some components of subjective well-being, such as satisfaction with life, but not negative and positive affect. Taken together, however, our preliminary findings suggest for the first time that social networking sites may actually amplify the spreading of gratitude and its benefits. Implications of our results for professionals and future research in the field of health, education, and social media communication are discussed.

Colexification Networks Encode Affective Meaning.

Colexification is a linguistic phenomenon that occurs when multiple concepts are expressed in a language with the same word. Colexification patterns are frequently used to estimate the meaning similarity between words, but the hypothesis that these are related is still missing direct empirical validation at scale. Here, we show for the first time that words linked by colexification patterns capture similar affective meanings. Using pre-existing translation data, we extend colexification databases to cover much longer word lists. We achieve this with an unsupervised method of affective lexicon extension that uses colexification network data to interpolate the affective ratings of words that are not included in the original lexicon. We find positive correlations between network-based estimates and empirical affective ratings, which suggest that colexification networks contain information related to affective meanings. Finally, we compare our network method with state-of-the-art machine learning, trained on a large corpus, and show that our simple linguistics-informed unsupervised algorithm yields comparable performance with high explainability. These results show that it is possible to automatically expand affective norms lexica to cover exhaustive word lists when additional data are available, such as in colexification networks. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-021-00033-1.

A structured open dataset of government interventions in response to COVID-19.

In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.