Determining the effectiveness of a massive open online course in data science for health

TY - GEN

T1 - Determining the effectiveness of a massive open online course in data science for health

AU - Alturkistani, A

AU - Car, J

AU - Majeed, A

AU - Brindley, D

AU - Wells, G

AU - Meinert, E

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Massive Open Online Courses (MOOCs) are widely used to deliver specialized education and training in different fields. Determining the effectiveness of these courses is an integral part of delivering comprehensive, high-quality learning. This study is an evaluation of a MOOC offered by Imperial College London in collaboration with Health iQ called, Data Science Essentials: Real World Evidence. The paper analyzes the reported learning outcomes, attitudes and behaviours of students after completing the MOOC. The study used mixed-methods, drawing from a Kirkpatrick evaluation-using data from semi-structured interviews transcribed and analyzed through Braun and Clark's method for thematic coding. 191 learners joined the MOOC. Two participants who completed at least 75related barriers that prevent knowledge application. Networking during and post-MOOC was identified as an area that needs improvement and development in the future. Findings derived from this evaluation support the fact that generally, MOOCs can improve learning and knowledge attainment in practical skills-based knowledge. One implication of this study is to inform factors that engage learners in the design and implementation of MOOC. The findings have shown that factors that affect the learners’ engagement are the availability of lecture videos, self-assessment tools and high networking and communication between learners. In terms of knowledge application, support and availability of the right resources are essential because learners are not able to apply learning in their workplace if the workplace lacked the right resources and support. Developers of MOOCs for continuing professional development should take into consideration work-related barriers when designing their MOOCs.

AB - Massive Open Online Courses (MOOCs) are widely used to deliver specialized education and training in different fields. Determining the effectiveness of these courses is an integral part of delivering comprehensive, high-quality learning. This study is an evaluation of a MOOC offered by Imperial College London in collaboration with Health iQ called, Data Science Essentials: Real World Evidence. The paper analyzes the reported learning outcomes, attitudes and behaviours of students after completing the MOOC. The study used mixed-methods, drawing from a Kirkpatrick evaluation-using data from semi-structured interviews transcribed and analyzed through Braun and Clark's method for thematic coding. 191 learners joined the MOOC. Two participants who completed at least 75related barriers that prevent knowledge application. Networking during and post-MOOC was identified as an area that needs improvement and development in the future. Findings derived from this evaluation support the fact that generally, MOOCs can improve learning and knowledge attainment in practical skills-based knowledge. One implication of this study is to inform factors that engage learners in the design and implementation of MOOC. The findings have shown that factors that affect the learners’ engagement are the availability of lecture videos, self-assessment tools and high networking and communication between learners. In terms of knowledge application, support and availability of the right resources are essential because learners are not able to apply learning in their workplace if the workplace lacked the right resources and support. Developers of MOOCs for continuing professional development should take into consideration work-related barriers when designing their MOOCs.

M3 - Other contribution

SN - 978-989-8533-78-4

PB - IADIS

ER -