How to design for persistence and retention in MOOCs?

Design of educational interventions is typically carried out following a design cycle involving phases of investigation, conceptualization, prototyping, implementation, execution and evaluation. This cycle can be applied at different levels of granularity e.g. learning activity, module, course or programme. In this paper we consider an aspect of learner behavior that can be critical to the success of many MOOCs i.e. their persistence to study, and the related theme of learner retention. We reflect on the impact that consideration of these can have on design decisions at different stages in the design cycle with the aim of en-hancing MOOC design in relation to learner persistence and retention, with particular attention to the European context

Ten simple rules for organizing a bioinformatics training course in low- And middle-income countries

© 2021 Moore et al.Bioinformatics training is required at every stage of a scientist’s research career. Continual bioinformatics training allows exposure to an ever-changing and growing repertoire of techniques and databases, and so biologists, computational scientists, and healthcare practitioners are all seeking learning opportunities in the use of computational resources and tools designed for data storage, retrieval, and analysis. There are abundant opportunities for accessing bioinformatics training for scientists in high-income countries (HICs), with well-equipped facilities and participants and trainers requiring minimal travel and financial costs alongside a range of general advice for developing short bioinformatics training courses [1–3]. However, regionally targeted bioinformatics training in low- and middle-income countries (LMICs) often requires more extensive local and external support, organization, and travel. Due to the limited expertise in bioinformatics in LMICs in general, most bioinformatics training requires a fair amount of collaboration with experts beyond the local community, country, or region. A common model of training, used as the basis of this article, includes a local host collaborating with local, regional, and international experts gathering to train local or regional participants. Recently, there has been a growth of capacity strengthening initiatives in LMICs, such as the Pan African Bioinformatics Network for Human Heredity and Health in Africa (H3ABioNet) Initiative [4–6], the Capacity Building for Bioinformatics in Latin America (CABANA) Project [7], the Asia Pacific BioInformatics Network (APBioNet) [8], and the Wellcome Connecting Science Courses and Conferences program [9]. One of the important strands of these initiatives is a drive to organize and deliver valuable bioinformatics training, but organizing and delivering short bioinformatics training workshops in an LMIC present a unique set of challenges. This paper attempts to build upon the sage advice for organizing bioinformatics workshops with specific guidance for organizing and delivering them in LMICs. It describes the processes to follow in organizing courses taking into consideration the low-resource setting. We should also note that LMICs are not a monolithic group and that setting, context, temporality, and specific location matters. LMICs are a complex regional grouping [10] and should be treated as such; however, we will present some common lessons that we hope will help organizers and trainers of bioinformatics training events in LMICs to navigate the often different, challenging, and rewarding experience.The authors who contributed to this manuscript are funded as follows: BM receives salary support from Wellcome Trust grants [WT108749/Z/15/Z, WT108749/Z/15/A], PC, VR, NM, AG’s salaries are funded in whole, or in part, by the NIH Common Fund H3ABioNet grant [U24HG006941], MC, SLFV, AR, PG, PCL’s salaries were partly funded by the UKRI-BBSRC ‘Capacity building for bioinformatics in Latin America’ (CABANA) grant, on behalf of the Global Challenges Research Fund [BB/P027849/1], JDLR is funded by ISCiii AES [ref. PI18/00591] at the CSIC/USAL (Spain) and by CYTED, RIABIO (Red Iberoamericana 521RT0118), AM’s salary is funded by [WT206194/Z/17/Z], GO is funded by the CABANA grant and SM is funded by the EMBL-EBI

Introducing mobile technologies to strengthen the national continuing medical education program in Vietnam

BACKGROUND: In 2009, the Government of the Republic of Vietnam adopted legislation requiring all clinicians to complete continuing medical education (CME) credits in order to maintain licensure. Several CME in-person and distance-based courses have been developed and as of 2015, a national distance-based electronic learning (eLearning) network was being established. However, the uptake of CME courses remained low despite high clinician demand. Vietnam’s high mobile phone ownership rate of 1.4 mobile subscriptions per person presents an opportunity to leverage this for CME. This study investigated how mobile technologies could strengthen delivery of distance-based CME courses and improve national CME program administration. METHODS: A literature and policy review was conducted. Qualitative methods were employed to collect and analyze key informant interviews of 52 global and Vietnamese experts, including selected policy makers. Interviews were supplemented by six focus group discussions with Vietnamese physicians, nurses, midwives and physician assistants. Transcripts were analyzed using an inductive coding methodology. A framework was developed to organize and present results for government consumption. RESULTS: Globally, examples and supporting evidence related to mobile technologies for CME were limited. Experts reported three main use cases for using mobile technology for CME in Vietnam: 1) delivery of CME courses (N=34; 65%); 2) registration and tracking of CME credits (n=28; 54%); and 3) sending alerts and reminders on CME opportunities (n=23; 44%). The national CME policy environment in Vietnam was supportive of introducing mobile technologies within the eLearning network. However, there was a widespread lack of awareness and capacity to design and deliver distance-based CME courses. Mobile phone ownership was high and health workers reported interest in acquiring CME credits via mobile. Financing options to develop and implement distance-based CME courses were limited. CONCLUSION: Despite the paucity of evidence related to mobile technologies for learning, there is potential to innovate and strengthen the evidence base using these technologies for CME in Vietnam. Introducing mobile technologies within the national eLearning network would improve clinicians’ access to CME, particularly in rural areas, and can strengthen national CME program administration. Key recommendations were developed to provide the government with concrete steps for national level adoption

Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study.

Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses. [Abstract copyright: © 2023. The Author(s).