A key concern for most institutions and instructors is whether students are satisfied with their learning experience. However, relatively few studies have unpacked what the key drivers for learner satisfaction are in blended and online courses. Using logistical regression modelling, learner satisfaction data of 62,986 learners in 401 undergraduate blended and online modules was analyzed. The data included over 200 potential explanatory variables based on learner and module learning design characteristics. Findings indicate that learning design has a strong and significant impact on overall satisfaction for both new and continuing learners. Learners who are more satisfied with the quality of teaching materials, assessment strategies, and workload are more satisfied with the overall learning experience. Furthermore, long‐term goals of learners (i.e., qualifications and relevance of modules with learners’ professional careers) are important predictors of learner satisfaction. Individual learner characteristics are mostly insignificant, indicating that despite a wide diversity of learners studying at the Open University, UK, the underlying learning experiences are similar. Future research should focus on how learning design changes can enhance the learning experiences of students.
A vast body of research has indicated the importance of distinguishing new versus continuing students' learning experiences in blended and online environments. Continuing learners may have developed learning and coping mechanisms for "surviving" in such learning environments, while new learners might still need to adjust their learning approaches to the new learning context. In this large scale replication study, we investigated whether and how the learning satisfaction experiences of 16670 new versus 99976 continuing students were different. Using logistical regression modelling of learner satisfaction scores of 422 undergraduate blended and online modules (including 232 learner and module learning design variables), our findings indicated that new learners indeed differed subtly in their learning and teaching experiences across two consecutive academic years. The minor differences in key drivers between the 2014 and 2015 cohorts also indicate that institutions need to continuously monitor and act upon changing learning needs.
Most distance learning institutions collect vast amounts of learner and learning data. Making sense of this "Big Data" can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, in 2012 the Open University UK (OU) instigated a Data Wrangling initiative. This provided every Faculty with a dedicated academic with expertise data analysis and whose task is to provide strategic, pedagogical, and sense-making advice to staff and senior management. Given substantial changes within the OU over the last 18 months (e.g., new Faculty structure, real-time dashboards, two large-scale adoptions of predictive analytics approaches, increased reliance on analytics), this embedded case-study provides an in-depth review of lessons learned of 5 years of data wrangling. Using semistructured interviews with key stakeholders (10 senior managers/associate deans) and ten Data Wranglers (DWs), a clear mismatch was identified in terms of resources, expertise, and skills that can effectively address key needs from Faculties. Furthermore, inconsistencies in terms of reporting and responding to bespoke requests were noted by stakeholders. Given the essential role of DW for the OU, a new DW structure is proposed to ensure effective provision of in-depth, evidence-based data analyses, pedagogical insight, and actionable advice for Faculties. We will elaborate on the design of the new structure, its strengths and potential weaknesses, and affordances to be adopted by other institutions.
During 2016, Safety4Sisters North West, a small women’s organisation based in Manchester, UK, worked with 61 women who had insecure immigration status and had experienced gender-based violence. This article examines the themes that arose from the project’s group work highlighting the realities of vulnerable migrant women living at society’s margins, resisting and navigating both patriarchal and immigration control within the context of austerity.
Lung cancer is responsible for ~160,000 deaths/year in the USA. Activation of signaling pathways downstream of receptor tyrosine kinase is common in non‐small cell lung cancer (NSCLC). Lung epithelium specific expression of mutationally activated KRASG12D or BRAFV600E leads to tumorigenesis. Unlike KRASG12D, BRAFV600E expression leads to benign lung adenomas that do not progress. One explanation might be the lack of PI3′K signaling in BRAFV600E–driven tumors. We generated mice carrying Cre‐activated alleles of BRaf, Pik3ca, or both. Intranasal instillation of Ad‐Cre to initiate PIK3CAH1047R expression failed to elicit lung tumors while expression of BRAFV600E resulted in abundant benign lung tumors. However, Ad‐Cre mediated expression of both BRAFV600E and PIK3CAH1047R resulted in increased number and size of lung tumors compared to control BRafCA littermates. Pharmacological inhibition of AKT prevented the increased tumor burden observed in BRafCA; Pik3caHR mice, indicating that cooperation is AKT dependent. We treated compound KRasLSL; Pik3caHR mice with Ad‐Cre, and these mice succumbed to lung tumorigenesis significantly more rapidly than control KRasLSL littermates. These data suggest that expression of PIK3CAH1047R alone is insufficient to promote lung tumorigenesis, but cooperates in dramatic fashion with either mutationally activated BRAFV600E or KRASG12D.
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