Journal Articles

With the fast expansion of online learning in higher education, institutions have increasingly offered and mandated faculty professional development (PD) programs focused on online instruction. However, the extent to which these PD programs indeed lead to improved students' online course performance remains largely unknown. This paper used a rigorous quasi-experimental approach to estimate the impact of a PD program on student online course performance at a large community college using a dataset that includes more than 370,000 online course enrollments taught by close to 900 instructors. The analyses yielded robust, nonsignificant estimates for the PD program on both online course persistence and course grades. Further qualitative analysis of the courses taught by PD participants indicated that instructors' integration of elements covered by the PD training into their subsequent teaching was fairly limited, highlighting the need for ongoing support to help instructors incorporate recommended practices into instruction.

Educators and researchers have analyzed various image data acquired from teaching and learning, such as images of learning materials, classroom dynamics, students’ drawings, etc. However, this approach is labour-intensive and time-consuming, limiting its scalability and efficiency. The recent development in the Visual Question Answering (VQA) technique has streamlined this process by allowing users to posing questions about the images and receive accurate and automatic answers, both in natural language, thereby enhancing efficiency and reducing the time required for analysis. State-of-the-art Vision Language Models (VLMs) such as GPT-4V(ision) have extended the applications of VQA to a wide range of educational purposes. This report employs GPT-4V as an example to demonstrate the potential of VLM in enabling and advancing VQA for education. Specifically, we demonstrated that GPT-4V enables VQA for educational scholars without requiring technical expertise, thereby reducing accessibility barriers for general users. In addition, we contend that GPT-4V spotlights the transformative potential of VQA for educational research, representing a milestone accomplishment for visual data analysis in education.

The Technology Acceptance Model (TAM) is a concise and efficient predictive model used to explain the acceptance of m-learning technology. However, several studies have shown that TAM cannot fully explain the acceptance of m-learning among Generation Z. This study aims to formulate TAM as a model of m-learning acceptance for Generation Z. TAM developed based on self-efficacy and enjoyment is expected to explain the behavior of Generation Z in accepting m-learning. This study uses a survey approach, utilizing PLS-SEM as an analysis tool and primary data collected through questionnaires. Participants in this study were 563 students who used m-learning (on class application) at the Muhammadiyah University of Purwokerto, Indonesia. The results contribute to the formulation of a successful m-learning implementation model for Generation Z. These results provide empirical support indicating that selfefficacy and perceived enjoyment cause them to use m-learning now and in the future. Generation Z, who grew up in the digital era, has a high level of proficiency in using technology. Self-efficacy increases user optimism. They are confident in their ability to complete tasks and solve problems when using m-learning. Enjoyment can increase the belief that m-learning is user-friendly and useful. The results of this study support the theory of self-efficacy which states that user beliefs serve as the best predictors of their behavior in using technology in mobile learning.

Electromyography (EMG) is increasingly used in stroke assessment research, with studies showing that EMG co-contraction (EMG-CC) of upper limb muscles can differentiate stroke patients from healthy individuals and correlates with clinical scales assessing motor function. This suggests that EMG-CC has potential for both assessing motor impairments and monitoring recovery in stroke patients. However, systematic reviews on EMG-CC’s effectiveness in stroke assessment are lacking. To address this, the present study aims to synthesize recent evidence on EMG-CC’s use in evaluating stroke-induced muscle abnormality. Eighteen studies including a total of 308 stroke patients and 155 healthy controls were included. Fifteen out of Eighteen included studies used the EMG-CC to successfully differentiate abnormal muscle co-contraction performance of the affected upper limb, even in comparison to the unaffected side in static tasks (isometric maximal voluntary contractions) and dynamic tasks (movement-oriented or goal-oriented). The EMG-CC shows promise as a convenient and effective tool for evaluating the extent of abnormal muscle coactivation in the upper limbs of post-stroke patients with spasticity as well as assessing the effectiveness of rehabilitation interventions. Further research is needed to validate these findings and establish standardized protocols for EMG-CC’s use in stroke assessment.

Cyberbullying victimization and mental health symptoms are major concerns for children and adolescents worldwide. Despite the increasing number of longitudinal studies of cyberbullying and mental health among this demographic, the robustness of the causal associations between cyberbullying victimization and the magnitude of mental health symptoms remains unclear. This meta-analysis investigated the longitudinal impact of cyberbullying victimization on mental health symptoms among children and adolescents. A systematic search identified primary studies published in English between January 2010 and June 2021, yielding a sample of 27 studies encompassing 13,497 children and adolescents aged 8 to 19 years old. The longitudinal association between cyberbullying victimization and mental health symptoms among children and adolescents was found to be weakly positive and consistent across time and age. Three significant moderators were identified: the effect of cyberbullying victimization on mental health was larger among older children, groups with a higher proportion of males, and in more recent publications. No evidence of publication bias was detected. This study adds to the existing body of research by providing a new perspective on the long-term effects of cyberbullying victimization on the mental health of children and adolescents’ mental health. Furthermore, it underscores the necessity of developing effective cyberbullying prevention programs, interventions, and legal regulations to comprehensively address this issue.