The release and rapid diffusion of ChatGPT have caught the attention of educators worldwide. Some educators are enthusiastic about its potential to support learning. Others are concerned about how it might circumvent learning opportunities or contribute to misinformation. To better understand reactions about ChatGPT concerning education, we analyzed Twitter data (16,830,997 tweets from 5,541,457 users). Based on topic modeling and sentiment analysis, we provide an overview of global perceptions and reactions to ChatGPT regarding education. ChatGPT triggered a massive response on Twitter, with education being the most tweeted content topic. Topics ranged from specific (e.g., cheating) to broad (e.g., opportunities), which were discussed with mixed sentiment. We traced that authority decisions may influence public opinions. We discussed that the average reaction on Twitter (e.g., using ChatGPT to cheat in exams) differs from discussions in which education and teaching–learning researchers are likely to be more interested (e.g., ChatGPT as an intelligent learning partner). This study provides insights into people's reactions when new groundbreaking technology is released and implications for scientific and policy communication in rapidly changing circumstances.

Systematic reviews and meta-analyses are crucial for advancing research, yet they are time-consuming and resource-demanding. Although machine learning and natural language processing algorithms may reduce this time and these resources, their performance has not been tested in education and educational psychology, and there is a lack of clear information on when researchers should stop the reviewing process. In this study, we conducted a retrospective screening simulation using 27 systematic reviews in education and educational psychology. We evaluated the sensitivity, specificity, and estimated time savings of several learning algorithms and heuristic stopping criteria. The results showed, on average, a 58% (SD = 19%) reduction in the screening workload of irrelevant records when using learning algorithms for abstract screening and an estimated time savings of 1.66 days (SD = 1.80). The learning algorithm random forests with sentence bidirectional encoder representations from transformers outperformed other algorithms. This finding emphasizes the importance of incorporating semantic and contextual information during feature extraction and modeling in the screening process. Furthermore, we found that 95% of all relevant abstracts within a given dataset can be retrieved using heuristic stopping rules. Specifically, an approach that stops the screening process after classifying 20% of records and consecutively classifying 5% of irrelevant papers yielded the most significant gains in terms of specificity (M = 42%, SD = 28%). However, the performance of the heuristic stopping criteria depended on the learning algorithm used and the length and proportion of relevant papers in an abstract collection. Our study provides empirical evidence on the performance of machine learning screening algorithms for abstract screening in systematic reviews in education and educational psychology.

During the COVID-19-related school closures teachers and students were challenged to suddenly switch to digital teaching at a distance. In particular, the challenge was to organize high-quality teaching in which students stay on task. Familiarity with technology may have helped to master the situation. However, only few studies so far have examined the quality of digital distance teaching (e.g., cognitive activation) during school closures and its relation to students' learning (e.g., effort investment). Moreover, systematic research concerning the role of familiarity with technology-enhanced teaching and learning acquired during face-to-face teaching is yet lacking. In our study, we used data from 729 ninth graders to investigate how student-observed learning activities when using technology at a distance were related to students' effort in learning in two subjects (mathematics, German). In addition, we examined whether student-perceived cognitive activation mediated this relation. Finally, the sample provides the unique opportunity to examine the role of familiarity, as some of the classes had been randomly equipped with tablet computers one year before the school closures and thus had the opportunity to gain familiarity with using technology in the classroom. Results from structural equation models showed that student-observed learning activities were associated with students' learning effort in both subjects. Student-perceived cognitive activation mediated this association. Familiarity with face-to-face technology-enhanced teaching gained before the COVID-19 pandemic appeared to be less important for high-quality digital distance teaching. Thus, infrastructural measures, such as equipping schools with digital devices so that teachers and students can familiarize themselves with technology, do not seem to be decisive for high-quality digital (distance) teaching-at least in the case of short-term change from face-to-face to digital distance teaching, as was necessary during the COVID-19 pandemic.

To support professional competence development in teacher education, learning environments should allow learners to engage with professional tasks. It is crucial for knowledge and skill transfer in such learning environments to real-life context that preservice teachers perceive the task as authentic. However, due to a lack of prior knowledge, novices may have difficulties in recognizing relevant elements of practice. It is thus assumed that different factors may guide their perception of task authenticity independently of the task that has to be mastered. Such factors could be, for example, overt design features of the learning environments on a physical level or the familiarity with the learning context and learning prerequisites, which act as important links for knowledge acquisition. In this study, preservice teachers’ perception of task authenticity is contrasted between two implementation types (video vs. role-play) of the same simulation aiming to foster diagnostic competence. The two types differ in approximating real-life practice concerning the professional task that has to be mastered. In an experimental, longitudinal study, N = 119 mathematics preservice teachers participated online in one type of the simulation four times during one semester (n = 66 video, n = 53 role-play). Perceived task authenticity was higher for the video simulation type and increased with repeated participation in the simulation independently of the implementation type. Further, preservice teachers’ task utility value positively influenced their perception of task authenticity. The results illustrate the role of learning prerequisites as well as familiarity with the task for novices’ perception. Also, they could be an initial indication that, depending on the level of learners’ professional development, the way of approximating real-life practice in simulations might influence the perception of task authenticity.