Social Networks among Students, Peer TAs, and Instructors and Their Impacts on Student Learning in the Blended Environment: A Model Development and Testing

Due to its flexibility and effectiveness, blended learning has become popular in higher education. Previous studies have discussed and presented various methods and cases that one can use and leverage in blended courses. Other studies have described and examined the technology and/or systems that support blended learning. However, no research has examined student learning from the social network perspective. Compared with traditional face-to-face instruction, blended learning incorporates a great portion of online activities. Thus, blended learning typically features fewer interactions among students, teaching assistants (if any), and instructors. Therefore, we need to examine whether and how (if any) social networks among students, peer teaching assistants, and instructors could influence student learning in the blended environment. To do so, we developed and tested a research model with a large sample size of 699 students who took a blended class. The results indicated that all three types of networks (including student-student networks, student-peer TA networks, and student-instructor networks) significantly influenced both social presence and interaction, which, in turn, had significant impacts on learning climate and perceived academic performance

Adoption of Social Media Search Systems: An IS Success Model Perspective

The social media search system aims at providing an organized and integrated access and search support to a massive amount of unstructured, multilingual, user-generated content in an effective and efficient manner. Previous research on social media analytics mainly focuses on developing and applying advanced analysis methods and/or tools to make sense of the large amount of user-generated data over the Internet. Relatively little effort has been put to specifically examine the social media search system. In this study, we utilize and apply the DeLone and McLean IS Success Model to examine this type of systems. To do it, a lab experiment was conducted, and the results showed that all causal relationships, except for satisfaction to social benefit, specified in the DeLone and McLean IS Success Model hold in the context of the large-scale, social media search system. Specifically, we found that information quality and system quality associated with the system could significantly influence both users’ intention to use and satisfaction toward it, both of which, in turn, had significant impacts on users’ perceived individual benefit and social benefit. In addition, satisfaction could significantly influence intention to use the system. Available at: https://aisel.aisnet.org/pajais/vol10/iss2/4

Examining Student Satisfaction and Gender Differences in Technology-Supported, Blended Learning

Recently, blended learning has become popular in higher education. In this study, we aim to investigate influential factors that could impact student learning in this young and relatively immature environment. Factors from three perspectives – students themselves, instructors, and institutional support – were examined. Specifically, these factors are students’ computer self-efficacy, instructor characteristics, and facilitating conditions. A research model was developed to systematically assess their impacts on students’ perceived accomplishment, perceived enjoyment, and satisfaction toward the blended class. We also explored the gender differences by testing the research model on the two genders respectively. Interestingly, we found that for female students all three factors had significant impacts on their perceived accomplishment and perceived enjoyment, which in turn significantly impacted their learning satisfaction; however, for male students, no significant impact was found from computer self-efficacy to either perceived accomplishment or perceived enjoyment (the other two factors were significant)

Myeloid malignancies: mutations, models and management

<p>Abstract</p> <p>Myeloid malignant diseases comprise chronic (including myelodysplastic syndromes, myeloproliferative neoplasms and chronic myelomonocytic leukemia) and acute (acute myeloid leukemia) stages. They are clonal diseases arising in hematopoietic stem or progenitor cells. Mutations responsible for these diseases occur in several genes whose encoded proteins belong principally to five classes: signaling pathways proteins (e.g. CBL, FLT3, JAK2, RAS), transcription factors (e.g. CEBPA, ETV6, RUNX1), epigenetic regulators (e.g. ASXL1, DNMT3A, EZH2, IDH1, IDH2, SUZ12, TET2, UTX), tumor suppressors (e.g. TP53), and components of the spliceosome (e.g. SF3B1, SRSF2). Large-scale sequencing efforts will soon lead to the establishment of a comprehensive repertoire of these mutations, allowing for a better definition and classification of myeloid malignancies, the identification of new prognostic markers and therapeutic targets, and the development of novel therapies. Given the importance of epigenetic deregulation in myeloid diseases, the use of drugs targeting epigenetic regulators appears as a most promising therapeutic approach.</p