New evaluation vector through the stanford mobile inquiry-based learning environment (Smile) for participatory action research

Abstract

Objectives: This article reviews an evaluation vector model driven from a participatory action research leveraging a collective inquiry system named SMILE (Stanford Mobile Inquiry-based Learning Environment). Methods: SMILE has been implemented in a diverse set of collective inquiry generation and analysis scenarios including community health care-specific professional development sessions and community-based participatory action research projects. In each scenario, participants are given opportunities to construct inquiries around physical and emotional health-related phenomena in their own community. Results: Participants formulated inquiries as well as potential clinical treatments and hypothetical scenarios to address health concerns or clarify misunderstandings or misdiagnoses often found in their community practices. From medical universities to rural village health promotion organizations, all participatory inquiries and potential solutions can be collected and analyzed. The inquiry and solution sets represent an evaluation vector which helps educators better understand community health issues at a much deeper level. Conclusions: SMILE helps collect problems that are most important and central to their community health concerns. The evaluation vector, consisting participatory and collective inquiries and potential solutions, helps the researchers assess the participants’ level of understanding on issues around health concerns and practices while helping the community adequately formulate follow-up action plans. The method used in SMILE requires much further enhancement with machine learning and advanced data visualization.