Research Article
Scott A. Courtney, Mary E. S. Miller, Michael J. Gisondo
CONT ED TECHNOLOGY, Volume 14, Issue 4, Article No: ep387
ABSTRACT
The coronavirus pandemic impacted all aspects of society, causing countries and local communities to close workplaces, move schools to remote instruction, limit in-person contact, cancel public gatherings, and restrict travel. Attempts to mitigate COVID-19 through remote instruction provided unique opportunities for researchers to examine the resources teachers utilize to drive their practices. We examine the impacts of the pandemic on grades 6-12 mathematics teachers and math interventionists, with particular attention to teachers’ integration of digital resources. Using purposive sampling, we surveyed 50 participants—across urban, suburban, and rural districts—throughout the United States. The descriptive survey focused on six aspects of teachers’ practices with digital resources. Results indicate that challenges encountered and lessons learned included a lack of student engagement and motivation, increased distractions, and varied access to technology. Integration of technology did not positively impact students’ mathematical proficiency across all teachers. Common resources used across planning of lessons, implementation of instruction, and assessment included the Google platform, Desmos, and GeoGebra. Where appropriate, we situate our results within the larger context of recent international research. These findings support teacher practices that constantly attempt to optimize students’ mathematics and social emotional learning, regardless of the environment or situation.
Keywords: digital technology, remote instruction, teachers’ mathematics practices, students’ social emotional learning
Research Article
Suthanit Wetcho, Jaitip Na-Songkhla
CONT ED TECHNOLOGY, Volume 14, Issue 2, Article No: ep359
ABSTRACT
In the era of a workforce driven by automation and artificial intelligence, social and emotional skills are becoming increasingly relevant to online learning environments. Since social-emotional learning may be defined as a vital component of the learning process in professional instructional design practices, online learners not only need to develop the ability to apply their knowledge, attitudes, and skills but also to understand and manage their emotions. In which setting and achieving positive goals through social interaction, sharing feelings, and developing empathy for others can help with the process. This paper outlines the possibility of using emotion recognition, and social sharing of emotion techniques to support the regulation of emotion in pre-service teacher education. This study aimed to investigate pre-service teachers’ emotion recognition tools acquired by emotion tracker and physiological signals based on their perceptions (without a concrete experience and knowledge). Moreover, the predictive ability was examined along with the relationships between emotion recognition, social sharing of emotion, and emotion regulation. Finally, we investigated emotion adjustment techniques that can be adapted into mobile computer-supported collaborative learning (mCSCL). In this study, 183 pre-service teachers from three different teacher-education institutions in Thailand, were voluntarily participated based on convenience sampling. The results of a self-report via online survey revealed that most pre-service teachers own at least one of the mobile technologies e.g., smartphones, tablets, or laptops. However, there is an increasing number of additional gadgets and wearable devices like EarPods and smartwatches. At the current time, it is nearly impossible to use of the IoT and other wearable devices. According to their subjective impressions in which corresponded to emotion recognition in the scientific literature, Heart rate (HR) and Heart rate variability (HRV) have recognized the most possibilities for emotion detection among physiological signals. Regarding regression analysis, the two-predictor models of emotion recognition and the social sharing of emotion were also able to account for 31% of the variance in emotion regulation, p<.001, R2=.31, and 95% CI [.70, .77]. In addition, the mCSCL applications and the importance of these variables in different collaboration levels are also discussed.
Keywords: social emotional learning, emotion recognition, social sharing of emotion, emotion regulation, mCSCL