Providing insight into assessment practices in medical school at one Saudi Higher Education Institution: an interpretative phenomenological analysis

It is evident across the globe that assessment has become increasingly central to the whole process of higher education (HE) as an important part of the curriculum and the teaching and learning cycle. There is strong evidence in the literature that assessment for learning (AfL) is key for effective student learning and academic progress in HE context, particularly in the medical context. In response to this international movement towards innovative assessment, there are some recent attempts in the Kingdom of Saudi Arabia (KSA) aiming to reshape and improve the assessment system. While AfL has been widely promulgated by a growing number of educational researchers, little research is available that considers medical lecturers and undergraduates’ experiences and perceptions of such AfL innovations, especially in Saudi learning context. The significant evidence about the influence of assessment on students’ learning drives this research to contribute to the Saudi HE reform. This study aims to investigate the practices of assessment and feedback in order to reshape the process of assessment in productive ways to enhance students’ learning and academic achievements. Through a phenomenological research design, specifically interpretative phenomenological analysis (IPA), this thesis aims to obtain deep understanding of lecturers and undergraduate students’ lived-experiences and perceptions of assessment in a Saudi Applied Medical Sciences School. In looking to generate an insight into their experiences and perceptions, one-to-one semi-structured interviews with 10 lecturers and 5 focus groups with 34 students were conducted in order to explore their experiences of assessment and their perspective of its impact on students’ learning. The lecturers and undergraduate students’ lived-experiences have been contextualized and interpreted using a dual hermeneutics analysis method in which the phenomenon of assessment was co-interpreted by both participants and researcher. The data of this study were qualitatively analysed following IPA steps which enables participants’ cognitive inner worlds to be explored. The findings reveal that there is a lack of clear theoretical underpinning frameworks of assessment practices in Saudi medical context. This is due to the rapid and major changes to move from the traditional to a new assessment culture. In addition, analysis of the responses shows there is a strong relationship between the medical discipline and assessment practices. Based on this relationship, students become more eager to use innovative types of assessment that require them to participate in their own development. Assessment also has a great influence on students’ approaches to learning where students tend to shift between deep approaches to “understanding” and a surface approaches such as “memorizing”, or to a strategic approach involving “a mix of two”, depending on the assessment methods used. As seen throughout the study’s findings, learners shift between different approaches to learning in order to suit the assessment demands of their modules. In order for policy and practice to support the implementation of AfL in the medical context, there is a need to ensure clarity and relevance of AfL to all stakeholders including lecturers and students. In addition, explicit and flexible models of change and reform should be adopted and sufficient support must be offered for a successful implementation

The power of the age standardized incidence rate to discover the gene link between cancer diseases: development of a new epidemiological method to save money, time, and effort for genetic scientists

Background: This study provides an incipient epidemiological rule using the concept of direct method of standardization to determine the genetic link between cancer diseases. Methods: The overall 8 or 10 years age standardized incidence rate (ASIR) for both cancer diseases, for example (A) and (B) should be calculated for all regions of the country. A line chart should be used to display the overall ASIR trend of both diseases (A and B). Pearson’s correlation can be used to determine the strength of the association between the overall ASIRs of both diseases. The overlap or opposite direction of the overall ASIR trend of both diseases (A and B) should be determined and studied for possible associations between cancer diseases. Results: If the trend of the overall 8 or 10 years ASIR of a disease (A) follows that of disease (B) in all regions of the country, then the genes of patients with both diseases (A and B) will be highly homogeneous, and they should be studied in the region with the highest and lowest overall ASIR for both diseases (A and B). In addition, if there is an opposite direction or overlapping trend for both diseases (A and B) in certain regions of the country or among specific groups of people with the same demographic characteristics, then the genes of patients will be investigated for both diseases to identify the potential gene link between cancer diseases. Conclusion: This study revealed that the overall ASIR trends of female breast cancer, prostate cancer, and ovarian cancer are very similar in all regions of Saudi Arabia and England. Our epidemiological evidence helps to save money, time, and effort for testing the potential gene link between cancer diseases

The pattern of Middle East respiratory syndrome coronavirus in Saudi Arabia: a descriptive epidemiological analysis of data from the Saudi Ministry of Health

Purpose: This study describes the epidemiology of Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia. Patients and methods: Epidemiological analysis was performed on data from all MERS-CoV cases recorded by the Saudi Ministry of Health between June 6, 2013 and May 14, 2014. The frequency of cases and deaths was calculated and adjusted by month, sex, age group, and region. The average monthly temperature and humidity of infected regions throughout the year was also calculated. Results: A total of 425 cases were recorded over the study period. The highest number of cases and deaths occurred between April and May 2014. Disease occurrence among men (260 cases [62%]) was higher than in women (162 cases [38%]), and the case fatality rate was higher for men (52%) than for women (23%). In addition, those in the 45–59 years and ≥60 years age groups were most likely to be infected, and the case fatality rate for these people was higher than for other groups. The highest number of cases and deaths were reported in Riyadh (169 cases; 43 deaths), followed by Jeddah (156 cases; 36 deaths) and the Eastern Region (24 cases; 22 deaths). The highest case fatality rate was in the Eastern Region (92%), followed by Medinah (36%) and Najran (33%). MERS-CoV infection actively causes disease in environments with low relative humidity (<20%) and high temperature (15°C–35°C). Conclusion: MERS-CoV is considered an epidemic in Saudi Arabia. The frequency of cases and deaths is higher among men than women, and those above 45 years of age are most affected. Low relative humidity and high temperature can enhance the spread of this disease in the entire population. Further analytical studies are required to determine the source and mode of infection in Saudi Arabia

Incidence rate of ovarian cancer cases in Saudi Arabia: an observational descriptive epidemiological analysis of data from Saudi Cancer Registry 2001–2008

Purpose This study provides descriptive epidemiological data, such as the percentage of cases diagnosed, crude incidence rate (CIR), and age-standardized incidence rate (ASIR) of ovarian cancer in Saudi Arabia from 2001–2008. Patients and methods A retrospective descriptive epidemiological analysis of all ovarian cancer cases recorded in the Saudi Cancer Registry (SCR) from January 2001–December 2008 was performed. The data were analyzed using descriptive statistics, analysis of variance tests, Poisson regression, and simple linear modeling. Results A total of 991 ovarian cancer cases were recorded in the SCR from January 2001–December 2008. The region of Riyadh had the highest overall ASIR at 3.3 cases per 100,000 women, followed by the Jouf and Asir regions at 3.13 and 2.96 cases per 100,000 women. However, Hail and Jazan had the lowest rates at 1.4 and 0.6 cases per 100,000 women, respectively. Compared to Jazan, the incidence rate ratio for the number of ovarian cancer cases was significantly higher (P<0.001) in the Makkah region at 6.4 (95% confidence interval [CI]: 4.13–9.83), followed by Riyadh at 6.3 (95% CI: 4.10–9.82), and the eastern region of Saudi Arabia at 4.52 (95% CI: 2.93–6.98). The predicted annual CIR and ASIR for ovarian cancer in Saudi Arabia could be defined by the equations 0.9 + (0.07× years) and 1.71 + (0.09× years), respectively. Conclusion We observed a slight increase in the CIRs and ASIRs for ovarian cancer in Saudi Arabia from 2001–2008. Riyadh, Jouf, and Asir had the highest overall ASIR, while Jazan and Hail had the lowest rates. Makkah, Riyadh, and the eastern region of Saudi Arabia had the highest incidence rate ratio for the number of ovarian cancer cases. Further analytical studies are required to determine the potential risk factors of ovarian cancer among Saudi women

Cloud-Based Retrieval Information System Using Concept for Multi-Format Data

The need of effective and efficient method to retrieving non-Web-enabled and Web-enabled information entities is essential, due to the fact of inaccuracy of the existing search engines that still use traditional term-based indexing for text documents and annotation text for images, audio and video files. Previous works showed that incorporating the knowledge in the form of concepts into an information retrieval system may increase the effectiveness of the retrieving method. Unfortunately, most of the works that implemented the concept-based information retrieval system still focused on one information format. This paper proposes a multi-format (text, image, video and, audio) concept-based information retrieval method for Cloud environment. The proposed method is implemented in a laboratory-scale heterogeneous cloud environment using Eucalyptus middleware.  755 multi-format information is experimented and the performance of the proposed method is measured

Computation offloading in mobile edge computing: an optimal stopping theory approach

In recent years, new mobile devices and applications with different functionalities and uses, such as drones, Autonomous Vehicles (AV) and highly advanced smartphones have emerged. Such devices are now able to launch applications such as augmented and virtual reality, intensive contextual data processing, intelligent vehicle control, traffic management, data mining and interactive applications. Although these mobile nodes have the computing and communication capabilities to run such applications, they remain unable to efficiently handle them mainly due to the significant processing required over relatively short timescales. Additionally, they consume a considerable amount of battery power. Such limitations have motivated the idea of computation offloading where computing tasks are sent to the Cloud instead of executing it locally at the mobile node. The technical concept of this idea is referred to as Mobile Cloud Computing (MCC). However, using the Cloud for computational task offloading of mobile applications introduces a significant latency and adds additional load to the radio and backhaul of the mobile networks. To cope with these challenges, the Cloud’s resources are being deployed near to the users at the Edge of the network in places such as mobile networks at the Base Station (BS), or indoor locations such as Wi-Fi and 3G/4G access points. This architecture is referred to as Mobile Edge Computing or Multi-access Edge Computing (MEC). Computation offloading in such a setting faces the challenge of deciding which time and server to offload computational tasks to. This dissertation aims at designing time-optimised task offloading decision-making algorithms in MEC environments. This will be done to find the optimal time for task offloading. The random variables that can influence the expected processing time at the MEC server are investigated using various probability distributions and representations. In the context being assessed, while the mobile node is sequentially roaming (connecting) through a set of MEC servers, it has to locally and autonomously decide which server should be used for offloading in order to perform the computing task. To deal with this sequential problem, the considered offloading decision-making is modelled as an optimal stopping time problem adopting the principles of Optimal Stopping Theory (OST). Three assessment approaches including simulation approach, real data sets and an actual implementation in real devices, are used to evaluate the performance of the models. The results indicate that OST-based offloading strategies can play an important role in optimising the task offloading decision. In particular, in the simulation approach, the average processing time achieved by the proposed models are higher than the Optimal by only 10%. In the real data set, the models are still near optimal with only 25% difference compared to the Optimal while in the real implementation, the models, most of the time, select the Optimal node for processing the task. Furthermore, the presented algorithms are lightweight, local and can hence be implemented on mobile nodes (for instance, vehicles or smart phones)

Time-Optimized Task Offloading Decision Making in Mobile Edge Computing

Mobile Edge Computing application domains such as vehicular networks, unmanned aerial vehicles, data analytics tasks at the edge and augmented reality have recently emerged. Under such domains, while mobile nodes are moving and have certain tasks to be offloaded to Edge Servers, choosing an appropriate time and an ideally suited server to guarantee the quality of service can be challenging. We tackle the offloading decision making problem by adopting the principles of Optimal Stopping Theory to minimize the execution delay in a sequential decision manner. A performance evaluation is provided by using real data sets compared with the optimal solution. The results show that our approach significantly minimizes the execution delay for task execution and the results are very close to the optimal solution

On the Optimality of Task Offloading in Mobile Edge Computing Environments

Mobile Edge Computing (MEC) has emerged as new computing paradigm to improve the QoS of users' applications. A challenge in MEC is computation (task/data) offloading, whose goal is to enhance the mobile devices' capabilities to face the requirements of new applications. Computation offloading faces the challenges of where and when to offload data to perform computing (analytics) tasks. In this paper, we tackle this problem by adopting the principles of Optimal Stopping Theory contributing with two time-optimized sequential decision making models. A performance evaluation is provided using real world data sets compared with baseline deterministic and stochastic models. The results show that our approach optimizes such decision in single user and competitive users scenarios

Delay-tolerant sequential decision making for task offloading in mobile edge computing environments

In recent years, there has been a significant increase in the use of mobile devices and their applications. Meanwhile, cloud computing has been considered as the latest generation of computing infrastructure. There has also been a transformation in cloud computing ideas and their implementation so as to meet the demand for the latest applications. mobile edge computing (MEC) is a computing paradigm that provides cloud services near to the users at the edge of the network. Given the movement of mobile nodes between different MEC servers, the main aim would be the connection to the best server and at the right time in terms of the load of the server in order to optimize the quality of service (QoS) of the mobile nodes. We tackle the offloading decision making problem by adopting the principles of optimal stopping theory (OST) to minimize the execution delay in a sequential decision manner. A performance evaluation is provided using real world data sets with baseline deterministic and stochastic offloading models. The results show that our approach significantly minimizes the execution delay for task execution and the results are closer to the optimal solution than other offloading methods