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
Research Article
Ahmet Coymak
CONT ED TECHNOLOGY, Volume 10, Issue 1, pp. 94-105
ABSTRACT
There is a growing interest in the use of computer-based learning environments to enhance learning in higher education, but the implications of this on the improvement of metacognition in higher education have yet to be adequately explored. In the current study, 175 students who enrolled in an introductory psychology course were randomly assigned to experimental and control groups (computer-assisted vs. self-managed). In the computer-assisted condition, besides the lecture delivered four hours per week and for three months in total, students took a set of assignments based on evidence-based teaching in a computer-based environment. The control group, however, took these assignments as in-and-out class activities without using a computer environment. An assessment based on Bloom’s taxonomy was utilized to obtain metacognitive and competency scores for both groups. The results of the study showed that the students who completed the structured learning assignments as computer-based, self-learning environment showed better metacognition performance than those in the self-managed group, who did not engage with the online platform, even though there was no significant difference between the groups regarding competence on the course’s learning objectives. The current experiment offers an empirical validation for why instructors should use technology as a self-regulatory tool that enhances students’ metacognitive performance and competence of learning outcomes.
Keywords: Metacognition, University teaching, Self-regulation in learning, Computer-based learning
Research Article
Junko Yamamoto, Simeon Ananou
CONT ED TECHNOLOGY, Volume 6, Issue 1, pp. 1-18
ABSTRACT
Even though technology has brought great benefits to current society, there are also indications that the manner in which people use technology has undermined their humanity in some respects. In this article the authors frame human nature in terms of four dimensions: cognition, social interaction, emotion, and ethics. We argue that while basic human nature remains constant, the four dimensions are molded to some extent by interaction with our environment, particularly through the use of technology. As a powerful factor, we need to use technology in a way that human cognition, social interaction, emotion, and ethics are supported and not seriously disturbed by digital devices. Education can be a means of helping learners use technology in a positive way, minimizing its negative potential. This paper shows how people’s choice and level of interaction with technology can allow them to live in harmony with technology. It also points to possible directions to teach healthy co-existence with technology. The suggestions include applying decision-making theory, increasing self-awareness, teaching academic honesty, and responding to problems with technology addiction through impulse control training and other programs
Keywords: Digital age, Humanity, Cognition, Social interaction, Ethics, Digital citizenship
Research Article
Alaba O. Agbatogun, Peter A. Ajelabi, Lawunmi M. Oyewusi
CONT ED TECHNOLOGY, Volume 1, Issue 4, pp. 335-347
ABSTRACT
This study was designed to investigate the relative and combined contributions of cognition and emotion on Nigerian undergraduate students’ level of computer frustration in online environments. A total of 1972 (Male=987, Female=985) students randomly selected from the two state-owned universities in Ogun State of Nigeria participated in the study. The data for the study were collected through the use of Students’ Cognition Scale (SCS), Students’ Emotion Scale (SES) and Students’ Computer Frustration Scale (SCFS). Data analysis involved the use of mean and standard deviation as descriptive statistics as well as Pearson Product Moment Correlation and regression analysis as inferential statistics. The research findings revealed that students encountered various frustrating experiences during e-registration, when a combination of the predictor variables (cognition and emotion) significantly accounted for 2.5% to the variance of the students’ level of frustration during e-registration. Meanwhile, cognition was found as the potent contributor of students’ frustration during e-registration. The results of this study further indicated that there was a statistically significant difference in the level of computer frustration among students of different universities. Recommendations were made according to the findings of the study.
Keywords: Cognition, Emotion, Computers frustration, Online registration