ORCID Profile
0000-0002-4664-3349
Current Organisations
Massachusetts Institute of Technology
,
Nanyang Technological University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: JMIR Publications Inc.
Date: 21-08-2023
DOI: 10.2196/52036
Publisher: JMIR Publications Inc.
Date: 17-05-2023
DOI: 10.2196/41671
Abstract: Digital education has expanded since the COVID-19 pandemic began. A substantial amount of recent data on how students learn has become available for learning analytics (LA). LA denotes the “measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” This scoping review aimed to examine the use of LA in health care professions education and propose a framework for the LA life cycle. We performed a comprehensive literature search of 10 databases: MEDLINE, Embase, Web of Science, ERIC, Cochrane Library, PsycINFO, CINAHL, ICTP, Scopus, and IEEE Explore. In total, 6 reviewers worked in pairs and performed title, abstract, and full-text screening. We resolved disagreements on study selection by consensus and discussion with other reviewers. We included papers if they met the following criteria: papers on health care professions education, papers on digital education, and papers that collected LA data from any type of digital education platform. We retrieved 1238 papers, of which 65 met the inclusion criteria. From those papers, we extracted some typical characteristics of the LA process and proposed a framework for the LA life cycle, including digital education content creation, data collection, data analytics, and the purposes of LA. Assignment materials were the most popular type of digital education content (47/65, 72%), whereas the most commonly collected data types were the number of connections to the learning materials (53/65, 82%). Descriptive statistics was mostly used in data analytics in 89% (58/65) of studies. Finally, among the purposes for LA, understanding learners’ interactions with the digital education platform was cited most often in 86% (56/65) of papers and understanding the relationship between interactions and student performance was cited in 63% (41/65) of papers. Far less common were the purposes of optimizing learning: the provision of at-risk intervention, feedback, and adaptive learning was found in 11, 5, and 3 papers, respectively. We identified gaps for each of the 4 components of the LA life cycle, with the lack of an iterative approach while designing courses for health care professions being the most prevalent. We identified only 1 instance in which the authors used knowledge from a previous course to improve the next course. Only 2 studies reported that LA was used to detect at-risk students during the course’s run, compared with the overwhelming majority of other studies in which data analysis was performed only after the course was completed.
Publisher: JMIR Publications Inc.
Date: 21-08-2023
Abstract: iabetic foot ulcers (DFUs) cause significant morbidity affecting 15% to 25% people living with diabetes Mellitus (DM). DFUs not only impair quality of life but also pose serious risks, including the impact on mortality rates. Diabetes and foot care education has proven to impact health outcomes. New technologies, including mHealth interventions such as health apps bring the potential for more cost-effective and scalable interventions. he study aims to examine the feasibility and usability of a newly developed mHealth app, Well Feet. Well Feet is a diabetes and foot care education app for in iduals living with DM and exposed to the risk of developing DFU. his mixed method approach uses a series of validated questionnaires and focus group discussions and aims at evaluating the feasibility and usability of Well Feet among the study participants. The utilization of both quantitative and qualitative measures enhances the depth and breadth of insights gained from this evaluation. The findings from this study will play a dual role: refining and enhancing the features of the Well Feet app based on user feedback, and shaping the procedural framework for a subsequent controlled trial. atient recruitment began in July 2023, the intervention and data collection will be completed by the end of September 2023. hrough this feasibility study, the Well Feet mHealth education app will undergo a comprehensive quantitative and qualitative evaluation. By focusing on foot care knowledge and skills, the app addresses a critical aspect of DM management. The assessment of usability and acceptance not only provides valuable guidance for potential improvements but also contributes to the knowledge base available to other researchers working in the field of DFU management interventions. Ultimately, this study bridges the gap between cutting-edge technology and the pressing healthcare needs of in iduals with DM, potentially paving the way for more effective and accessible interventions in the future.
Publisher: JMIR Publications Inc.
Date: 04-08-2022
Abstract: igital education has expanded since the COVID-19 pandemic began. A substantial amount of recent data on how students learn has become available for learning analytics (LA). LA denotes the “measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” his scoping review aimed to examine the use of LA in health care professions education and propose a framework for the LA life cycle. e performed a comprehensive literature search of 10 databases: MEDLINE, Embase, Web of Science, ERIC, Cochrane Library, PsycINFO, CINAHL, ICTP, Scopus, and IEEE Explore. In total, 6 reviewers worked in pairs and performed title, abstract, and full-text screening. We resolved disagreements on study selection by consensus and discussion with other reviewers. We included papers if they met the following criteria: papers on health care professions education, papers on digital education, and papers that collected LA data from any type of digital education platform. e retrieved 1238 papers, of which 65 met the inclusion criteria. From those papers, we extracted some typical characteristics of the LA process and proposed a framework for the LA life cycle, including digital education content creation, data collection, data analytics, and the purposes of LA. Assignment materials were the most popular type of digital education content (47/65, 72%), whereas the most commonly collected data types were the number of connections to the learning materials (53/65, 82%). Descriptive statistics was mostly used in data analytics in 89% (58/65) of studies. Finally, among the purposes for LA, understanding learners’ interactions with the digital education platform was cited most often in 86% (56/65) of papers and understanding the relationship between interactions and student performance was cited in 63% (41/65) of papers. Far less common were the purposes of optimizing learning: the provision of at-risk intervention, feedback, and adaptive learning was found in 11, 5, and 3 papers, respectively. e identified gaps for each of the 4 components of the LA life cycle, with the lack of an iterative approach while designing courses for health care professions being the most prevalent. We identified only 1 instance in which the authors used knowledge from a previous course to improve the next course. Only 2 studies reported that LA was used to detect at-risk students during the course’s run, compared with the overwhelming majority of other studies in which data analysis was performed only after the course was completed.
Location: United States of America
No related grants have been discovered for Iva Bojic.