An Analysis of User-Generated Comments on the Development of Social Mobile Learning

In this study, the authors used a mixed-method approach to analyze user-generated comments on social mobile learning from three leading news sites that report the latest development in higher education. Koole’s mobile learning model was used to code comments made by the public on the three news sites. Results showed that social mobile learning has gained an increasing public engagement in the past four years. Responders’ discussion in the comments primarily focused on four themes of social mobile learning: technology adoption, effective design, faculty training, and student training. In the end, the authors discussed the implications for developers and educators and concluded with recommendations for future research in social mobile learning using user-generated comments

Pandemic Pedagogy in Online Hands-on Learning for IT/IS Courses

Due to the coronavirus disease of 2019 (COVID-19) pandemic, many educational institutions have transitioned to online instruction. As a result, instructors need to investigate online small group learning opportunities to bond with their students who feel isolated from their peers due to social-distancing guidelines. In this paper, we discuss three key issues in online hands-on learning: 1) interactions, 2) equity and inclusive participation, and 3) students’ readiness for hands-on or higher-level cognitive learning. We reflect on our teaching experience during the COVID-19 pandemic and offer suggestions to help instructors plan and implement online small group hands-on learning

Teaching Tip: Promoting Inclusive Online Learning for Students with Disabilities in Information Systems Courses

It is important to ensure educational equity for students with disabilities in online courses as more courses are taught online during the pandemic. This conceptual paper describes the key barriers for instructors to implement inclusive online classes, particularly technology courses, during the COVID-19 pandemic and suggests specific strategies to mitigate the negative impact on students with disabilities. We hope that this paper will motivate more Information Technology/Information Systems (IT/IS) instructors to implement more inclusive and accessible online courses to keep all students, including students with disabilities, motivated and engaged in online environments throughout the pandemic

Deep learning for automatic microscopy image analysis

Microscopy imaging techniques allow for the creation of detailed images of cells (or nuclei) and have been widely employed for cell studies in biological research and disease diagnosis in clinic practices.Microscopy image analysis (MIA), with tasks of cell detection, cell classification, and cell counting, etc., can assist with the quantitative analysis of cells and provide useful information for a cellular-level understanding of biological activities and pathology. Manual MIA is tedious, time-consuming, prone to subject errors, and are not feasible for the high-throughput cell analysis process. Thus, automatic MIA methods can facilitate all kinds of biological studies and clinical tasks. Conventional feature engineering-based methods use handcrafted features to address MIA problems, but their performances are generally limited since the handcrafted features can lack feature diversity as well as relevancy to specific tasks. In recent years, deep learning, especially convolutional neuronal networks (CNNs), have shown promising performances on MIA tasks, due to their strong ability to automatically learn task-specific features directly from images in an end-to-end learning manner. However, there still remains a large gap between deep learning algorithms shown to be successful on retrospective datasets and those translated to clinical and biological practice. The major challenges for the application of deep learning into practical MIA problems include: (1) MIA tasks themselves are challenging due to limited image quality, the ambiguous appearance of inter-class nuclei, occluded cells, low cell specificity, and imaging artifacts; (2) training a learning algorithm is very challenging due to the potential gradient vanishing issue and the limited availability of annotated images. In this thesis, we investigate and propose deep learning methods for three challenging MIA tasks: cell counting, multi-class nuclei segmentation, and 3D phase-to-fluorescent image translation. We demonstrate the effectiveness of the proposed methods by intensively evaluating them on practical MIA problems. The proposed methods show superior performances compared to competitive state-of-the-art methods. Experimental results demonstrated that the proposed methods hold great promise to be applied in practical biomedical applications

A Literature Review of Quantum Education in K-12 Level

Quantum computing is an emerging technology paradigm of computing and has the potential to solve computational problems intractable using today’s classical computers or digital technology. Quantum computing is expected to be disruptive for many industries. The power of quantum computing technologies is based on the fundamentals of quantum mechanics, such as quantum superposition, quantum entanglement, or the no-cloning theorem. To build a highly trained and skilled quantum workforce that meets future industry needs, there is a need to introduce quantum concepts early on in K-12 schools since the learning of quantum is a lengthy process. As fundamental quantum concepts derive from physics, students usually start to learn physics in secondary schools. Since the resources and curriculum design for quantum education in K-12 level is rare, we conducted a literature review with a focus on quantum computing education in K-12 level and filled the research gap

Gamma-tocotrienol stimulates the proliferation, differentiation, and mineralization in osteoblastic MC3T3-E1 cells

Gamma-tocotrienol, a major component of tocotrienol-rich fraction of palm oil, has been suggested to exhibit bone protective effects in vivo. However, the effects of γ-tocotrienol on osteoblast cells are still unclear. In this study, the effects of γ-tocotrienol on the proliferation, differentiation, and mineralization in osteoblastic MC3T3-E1 cells were investigated. Our results showed that γ-tocotrienol (2–8 μmol/L) significantly improved the cell proliferation (), but it did not affect cell cycle progression. γ-Tocotrienol significantly increased alkaline phosphatase (ALP) activity (), secretion levels of osteocalcin (OC) and osteonectin (ON), and mRNA levels of collagen type I (Col I) of MC3T3-E1 cells. Meanwhile, we found that γ-tocotrienol is promoted in differentiation MC3T3-E1 cells by upregulation of the expression of Runx2 protein. Moreover, the number of bone nodules increased over 2.5-fold in cells treated with γ-tocotrienol (2–8 μmol/L) for 24 d compared to control group. These results indicated that γ-tocotrienol at low dose levels, especially 4 μmol/L, could markedly enhance the osteoblastic function by increasing the proliferation, differentiation, and mineralization of osteoblastic MC3T3-E1 cells. Moreover, our data also indicated that Runx2 protein may be involved in these effects. Further studies are needed to determine the potential of γ-tocotrienol as an antiosteoporotic agent