International Postgraduate Research Conference (IPRC)
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Item Connectivism for improved learning outcomes in higher education in the digital age – A scoping review(Faculty of Graduate Studies, University of Kelaniya Sri Lanka, 2022) Senadheera, V.; Muthukumarana, C.; Rupasinghe, T.; Ediriweera, D.In the current context, there is a substantial need to update instructional strategies used in higher education, to cater the learning needs of current learners, who are called as digital natives. Connectivism, which is a learning theory that intends to explain how learning occurs in the digital age, can be used to bridge the gap between instructional strategies and how learning occurs in digital natives. The objective of this scoping review was to examine how connectivism has been applied in higher education, describe the findings and to understand the impact of connectivism on the success of student learning in higher education. Nine databases were searched for eligible publications. SCOPUS, EBSCOhost, Emerald, JSTOR, Taylor and Francis, PubMed (MEDLINE), ERIC, ACM and IEEE Xplore were searched using the keyword ‘connectivism’. The search retrieved 1560 records of which 24 articles were selected according to inclusion and exclusion criteria. Included studies which were published 2009 through 2022, were from 18 countries and represented 12 study fields including; language learning, education, medicine, and engineering. In reported studies, either connectivism has been applied alone or it has been combined with another learning theory to design teaching/learning activities. These studies predominantly have used the online mode (54%), followed by blended learning mode (25%) and face to face mode (21%). Among these studies, 67% have been intended to deliver skills such as; writing, teaching and work-based learning, and 33% have been intended to deliver both theory and skills such as; biostatistics, chemistry and pedagogical practices, while no study has been designed to deliver a theory alone. To evaluate the outcomes of the teaching/learning activities, 75% of the studies used qualitative approaches, 12.5% used quantitative approaches and 12.5% used mixed methods. According to the findings, 17% of studies have reported that, connectivist learning environment has exhibited a significant positive impact on the academic performance of students through the promotion of higher order learning activities such as; synthesizing information, creating new knowledge and applying. More importantly, it has resulted in an enhancement of several attributes of learners which are required in the current job market. Accordingly, 17% of studies reported enhanced creative thinking, 21% self-management of learning and 50% enhanced interactions with peers as outcomes of using connectivism to design teaching and learning. Bringing connectivism to higher education is a method to incorporate formal education into the learning needs of the digital age and it has the potential to offer improved learning outcomes for higher education students. These improved outcomes are more pronounced when connectivism is used to deliver skills (deliver functioning knowledge/ put knowledge into action) compared to when it is used to deliver theory (declarative knowledge/content knowledge). Overall, it can be concluded that the successful integration of principles of connectivism in skill related teaching has a positive impact on students’ learning and promotes lifelong learning.Item Evaluating Spatiotemporal Dynamics of Snakebite in Sri Lanka(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Ediriweera, D.; Diggle, P.; Kasturiratne, A.; Pathmeswaran, A.; Gunawardena, N.; Jayamanne, S.; Lalloo, D.; de Silva, J.Snakebite data has shown spatial and temporal variations in many countries and regions. Yet, no study has evaluated spatiotemporal patterns of snakebites across a country in detail. We used data from the National Snakebite Survey (NSS), which sampled 0.8% of the national population (165665 people) living in 1118 clusters representing all the provinces. Explanatory variables of previously published spatial and temporal models for the NSS data were considered as candidate explanatory variables for our spatiotemporal models. Spatial prediction models for snakebite incidence was a geostatistical binomial logistic model and the temporal prediction model was a Poisson log-linear model, which predicted snakebite incidence at the national level. These spatial and temporal models could not explain locally varying temporal patterns in the country. Therefore, we constructed spatiotemporal models at the provincial levels. The NSS was conducted for 11 consecutive months, and different clusters were surveyed in each month. Therefore, the NSS can be considered as a set of 11 repeated cross-sectional surveys at different locations. NSS captured bite events that occurred in the survey month and in the 12 preceding months. Hence, each individual provided information regarding the number of bites experienced in each of 13 months. In the NSS data, the location of each sampled individual was fixed at the cluster centroid and the data contain the month of each recorded bite, if any, over a 13 month period covering the survey month and each of the preceding 12 months. We modelled the data from each cluster as an inhomogenous Poisson process with cluster-level explanatory variables and estimated the model parameters by maximising the pooled log-likelihood over all. The fitted cluster-level spatiotemporal models were aggregated so as to predict the province-level monthly bite incidence rates in Sri Lanka. Snakebite incidence showed complex spatiotemporal patterns in Sri Lanka. Models fitted for Southern, North Central, Uva and Sabaragamuwa provinces showed both spatial and temporal variation in snakebites. The geographical extent of the high-risk areas (i.e. hotspots) in these provinces dynamically changed over a period of a year. The remaining five models (i.e. Western, Central, North Western, Northern and Eastern) did not show any spatio-temporal interaction, in risk, i.e. the geographical extent of the hotspots persisted throughout the year. Southern, Sabaragamuwa and North Central provinces showed triannual seasonal trends. High snakebite incidences in Southern and Sabaragamuwa provinces were noticed in April followed by December and August to September. Peak incidences in North Central province were seen in November and another two smaller peaks were observed in April and July. Uva province showed a biannual trend with highest incidences in June followed by December. These findings can inform healthcare decision-making at local level, taking account of the seasonal variations in order to prevent and manage snakebites in Sri LankaItem Evaluating Temporal Patterns of Snakebite in Sri Lanka: The Potential for Higher Snakebite Burdens with Climate Change(19th Conference on Postgraduate Research, International Postgraduate Research Conference 2018, Faculty of Graduate Studies,University of Kelaniya, Sri Lanka, 2018) Ediriweera, D.Background: Snakebite is a neglected tropical disease that has been overlooked by healthcare decision makers in many countries. Previous studies have reported seasonal variation in hospital admission rates due to snakebites in endemic countries including Sri Lanka, but seasonal patterns have not been investigated in detail. Methods: A national community-based survey was conducted during the period of August 2012 to June 2013. The survey used a multistage cluster design, sampled 165 665 individuals living in 44 136 households and recorded all recalled snakebite events that had occurred during the preceding year. Log-linear models were fitted to describe the expected number of snakebites occurring in each month, taking into account seasonal trends and weather conditions, and addressing the effects of variation in survey effort during the study and of recall bias amongst survey respondents. Results: Snakebite events showed a clear seasonal variation. Typically, snakebite incidence is highest during November–December followed by March–May and August, but this can vary between years due to variations in relative humidity, which is also a risk factor. Low relative-humidity levels are associated with high snakebite incidence. If current climate-change projections are correct, this could lead to an increase in the annual snakebite burden of 31.3% (95% confidence interval: 10.7–55.7) during the next 25–50 years. Conclusions: Snakebite in Sri Lanka shows seasonal variation. Additionally, more snakebites can be expected during periods of lower-than-expected humidity. Global climate change is likely to increase the incidence of snakebite in Sri LankaItem Envenoming Snakebite Risk Map for Sri Lanka(Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Ediriweera, D.; Kasturiratne, A.; Pathmeswaran, A.; Pathmeswaran, A.; Gunawardena, N.; Jayamanne, S.; Wijayawickrama, B.; Isbister, G.; Giorgi, A.D.E.; Diggle, P.; Lalloo, D.; de Silva, J.Snakebite is a neglected tropical disease. Hospital based statistics often underestimate snakebite incidence because a significant proportion of victims seek traditional treatments. Since geospatial risk assessments of snakebite envenoming are rare, health care resources are distributed based on administrative boundaries rather than on a need analysis. The aim of the study was to develop a snakebite envenoming risk map for Sri Lanka. Epidemiological data was obtained from a community-based island-wide survey. The sample was distributed equally among the nine provinces. 165,665 participants living in 1118 Grama Niladhari divisions were surveyed. Model-based geostatistics was used to determine the geographical distribution of envenoming bite incidence. The Monte Carlo maximum likelihood method was used to obtain parameter estimates and plug-in spatial predictions of risk. A predictive model was developed with natural and social environmental variables to construct an estimated envenoming bite incidence map and a probability contour map (PCM) to demonstrate the spatial variation in the predictive probability that local incidence does or does not exceed national envenoming snakebite incidence (i.e. 151 per 100,000). Envenoming bite incidence had a positive association with elevation up to 195 meters above sea level, with incidence dropping at higher elevations. The incidence of envenoming was higher in the dry zone compared to intermediate and wet climatic zones and decreased with increasing population density. Developed risk maps showed substantial within-country spatial variation in envenoming bites. Conclusion: The risk maps provide useful information for healthcare decision makers to allocate resources to manage snakebite envenoming in Sri Lanka. We used replicable methods which can be adapted to other geographic regions after re-estimating spatial covariance parameters for each region of interest.