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Leveraging progress to improve proficiency: Analyzing the relationship between usage statistics and educational goals in a mobile learning environment to develop an educational “scorecard” for Frontline Health Workers


Tuesday, November 3, 2015

Lindsey Leslie, MSPH, Center for Communication Programs, Johns Hopkins University, Baltimore, MD
Michael Bailey, MA, Center for Communication Programs, Johns Hopkins University, Baltimore, MD
Alex Little
Lisa Cobb, MPH, Center for Communication Programs, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
Moji Odeku, Center for Communication Programs, Johns Hopkins University, Baltimore, MD
Background: There is evidence to support integrating learning analytics into distance education systems. Developing a “scorecard” tool can increase awareness, engagement and create a personalized educational experience for students using the system. However, there is limited research on which variables are most relevant when developing a similar tool for mLearning applications used by Frontline Health Workers (FLHW). With the necessary modifications, the proposed scorecard tool has the capacity to reflect progress and predict the performance of each individual FLHW, resulting in a tailored learning experience. Using the Nigerian Urban Reproductive Health Initiative (NURHI) and OppiaMobile as a case study, we seek to describe which usage statistics collected by the application should be represented in a scorecard to more accurately assess student progress, engagement and achievement. Further, we aim to recommend additional measures necessary to apply predictive analytics for tailoring content to individual student needs.

Methods: A literature review and descriptive analytics of usage statistics from NURHI users was conducted to capture key variables and identify additional data that could conceivably be collected. This data was strategically incorporated onto the scorecard tool.

Results: Variables associated with engagement and educational attainment among FLHWs were identified. A variety of visual representations have been created for testing with users to determine the optimal set of variables to collect and how best to represent them.

Conclusions: Following an iterative design and development process with FHWs, this tool will present new opportunities for self-awareness and accountability, contributing to a more personalized learning experience for FLHWs.  This is a first step towards the ultimate objective: the data selection, collection and representation process in order to more confidently tailor content to student needs.  If successful, this intervention may also serve as a resource to organizations interested in creating similar features for their distance education programs for FHWs.

Learning Areas:

Communication and informatics
Planning of health education strategies, interventions, and programs

Learning Objectives:
Define "learning analytics" Articulate how the principles of learning analytics could be applied to distance education on a mobile phone (mLearning) Describe the methodology used to identify potential variables when developing a scorecard tool.

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have worked on multiple mLearning systems with open source platforms in India and Nigeria. My professional interests include behavior change communication and strategic use of new media to engage communities.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

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