A Bibliometric Analysis of the Papers Published in the Journal of Artificial Intelligence in Education from 2015-2019

To analyze the current research status and trends of the artificial intelligence in education field, we applied bibliometric methods to examine the articles published in one of the representative journals of the field, International Journal of Artificial Intelligence in Education, from 2015 to 2019. We analyzed 135 articles retrieved from the Web of Science database and examined prolific countries, collaboration networks, prolific authors, keywords, and the citations the articles received. Through examining keywords, we found that the authors largely focused on students and learning. Through examining prolific authors and countries, we found active publication of corresponding authors from United States, United Kingdom, Canada, and Germany. We found international collaboration among some researchers and institutions, such as strong collaboration network between United States and Canada. We suggest reinforcement in building more widespread international partnership and expanding collaboration network by including diverse institutions. International collaboration and expanded institutional network can improve research by incorporating various perspectives and expertise

Online Learning Communities in the COVID-19 Pandemic: Social Learning Network Analysis of Twitter During the Shutdown

This paper presents a social learning network analysis of Twitter during the 2020 global shutdown due to the COVID-19 pandemic. Research concerning online learning environments is focused on the reproduction of conventional teaching arrangements, whereas social media technologies afford new channels for the dissemination of information and sharing of knowledge and expertise. We examine Twitter feed around the hashtags online learning and online teaching during the global shutdown to examine the spontaneous development of online learning communities. We find relatively small and ephemeral communities on the two topics. Most users make spontaneous contributions to the discussion but do not maintain a presence in the Twitter discourse. Optimizing the social learning network, we find many potential efficiencies to be gained through more proactive efforts to connect knowledge seekers and knowledge disseminators. Considerations and prospects for supporting online informal social learning networks are discussed

Investigating a blended learning context that incorporates two-stage quizzes and peer formative feedback in STEM education

Researchers have expressed concern about the state of STEM education. To improve this situation, new pedagogies, such as blended learning, have been proposed and tested. The last decade has seen an increase in the use of blended learning to support learning; however, the effect of blended learning on learning remains unclear and often mixed. The two studies in this paper draw on data from pre-university science students in the following courses: (1) Electricity and Magnetism (E&M) and (2) Waves, Optics & Modern Physics (Waves). In study 1, the treatment group (blended learning coupled with two-stage quizzes & peer formative feedback) performed significantly higher than the control group (lecture format with online homework & instant feedback) in the standardized final exam. In contrast, in study 2, there was a non-significant main effect of groups, indicating that the treatment group (blended learning with online homework & instant feedback) and the control group (lecture format with online homework & instant feedback) performed similarly in the standardized final exam. The finding of study 1 suggests that the effect of an instructional pedagogical framework embedded in a blended learning context improves performance in STEM education. Whereas the finding of study 2 suggests that a blended learning context without incorporating any instructional framework or support for cognition other than the lecture is comparable to a traditional face-to-face course

The Effects of Testing on Pre-University Science Students’ Academic Outcomes in an Electricity and Magnetism Course

Testing can influence student learning outcomes by influencing their approach to study and to learning. It is important tounderstand the influence of testing on students’ learning outcomes to optimize instruction. We examine the role that testing playedin a science course, to examine the effect of testing on retention and performance on a standardized final exam. This studycompared two sections—experimental condition with testing (N = 35) and comparison condition with homework (N = 39)—of anElectricity and Magnetism course in a pre-university program to explore the role of the testing effect, that is, whether taking a testaids subsequent learning and retention. Results indicated that the students in the experimental group had a higher final examaverage and greatest achievement gains. Our findings corroborate previous research and suggest that the traditional homework-based instructional strategy is a less effective approach for science learning or later retention compared to an instructionalapproach incorporating regular testing. Implications of these findings and the importance of testing in science instruction are alsodiscussed

Assessing the Utility of Deep Learning: Using Learner-System Interaction Data from BioWorld

In recent years, deep learning (LeCun, Bengio, & Hinton, 2015) has drawn interest in many fields. As optimism for deep learning grows, a better understanding of the efficacy of deep learning is imperative, especially in analyzing and making sense of educational data. This study addresses this issue by establishing a benchmark for a common prediction task – student proficiency in diagnosing patient diseases in a system called BioWorld (Lajoie, 2009). To do so, we compared deep learning to existing solutions, including traditional machine learning algorithms that are commonly used in educational data mining. The dataset consists of log interaction data collected from 30 medical students solving 3 different cases. A 10-fold cross-validation method was used to evaluate the predictive accuracy of each model. Interestingly, our results indicate that deep learning does not outperform traditional machine learning algorithms in predicting diagnosis correctness. We discuss the implications in terms of understanding the proper conditions for its use in educational research

Exploring the Role of Testing in Student Outcomes: Evidence from a Mechanics Course

Assessments have become increasingly prevalent in education. While many affordances of assessments are offered in the literature, there is mixed evidence on how assessments affect students’ learning and performance. Moreover, a testing effect has been identified in lab-based studies where more testing is associated with better performance; however, less is known about the effects of testing on performance in situ. The present study employs data from two Mechanics courses to analyze the effects of testing on performance. We compare two sections—experimental condition with testing (N = 36) and control condition with homework (N = 38)—of the Mechanics course, to examine the relative importance of testing. We find a strong effect for regular testing on student mid-term and final exam performance. The findings have broad implications for the growing testing effect literature

Transition to Online Learning During the COVID-19 Pandemic

Background With the new pandemic reality that has beset us, teaching and learning activities have been thrust online. While much research has explored student perceptions of online and distance learning, none has had a social laboratory to study the effects of an enforced transition on student perceptions of online learning. Purpose We surveyed students about their perceptions of online learning before and after the transition to online learning. As student perceptions are influenced by a range of contextual and institutional factors beyond the classroom, we expected that students would be overall sanguine to the project given that access, technology integration, and family and government support during the pandemic shutdown would mitigate the negative consequences. Results Students overall reported positive academic outcomes. However, students reported increased stress and anxiety and difficulties concentrating, suggesting that the obstacles to fully online learning were not only technological and instructional challenges but also social and affective challenges of isolation and social distancing. Conclusion Our analysis shows that the specific context of the pandemic disrupted more than normal teaching and learning activities. Whereas students generally responded positively to the transition, their reluctance to continue learning online and the added stress and workload show the limits of this large scale social experiment. In addition to the technical and pedagogical dimensions, successfully supporting students in online learning environments will require that teachers and educational technologists attend to the social and affective dimensions of online learning as well

Towards emotion awareness tools to support emotion and appraisal regulation in academic contexts

International audienceThis paper studies learners’ emotion awareness in university level academic contexts as a first step to help learners regulate their emotions. Existing emotion awareness tools offer little information on learners’ emotions and their antecedents. This study created an emotion-reporting grid for university students based on the emotions they experienced daily. Students were interviewed based on their self-reported grid. A quantitative descriptive analysis of these retrospective interviews was conducted based on Pekrun’s control-value theory of achievement emotions. Student transcripts were analyzed based on the focus of their emotions (retrospective, activity, or prospective), the causes they attribute to their emotions (agent or external circumstances) and how they appraised the situation in which they experienced the emotions (value and control). We discuss the results with regard to the types of emotion-oriented and appraisal-oriented regulation strategies used in learning contexts and draw implications for the design of emotion awareness tools to support emotion regulation processes

Fearing the Robot Apocalypse: Correlates of AI Anxiety

This study examines the relationship between individuals’ be-liefs about AI (Artificial Intelligence) and levels of anxiety with respect to their technology readiness level. In this cross-sectional study, we surveyed 65 stu-dents at a southwestern US college. Using partial least squares analysis, we found that technology readiness contributors were significantly and positively related to only one AI anxiety factor: socio-technical illiteracy. In contrast, all four links between technology readiness inhibitors and AI anxiety factors were significant with medium effect sizes. Technology readiness inhibitors are posi-tively related to learning, fears of job replacement, socio-technical illiteracy, and particular AI configurations. Thus, we conclude that AI anxiety runs through a spectrum. It is influenced by real, practical consequences of immedi-ate effects of increased automatization but also by popular representations and discussions of the negative consequences of artificial general intelligence and killer robots and addressing technology readiness is unlikely to mitigate effects of AI anxiety