Dataset Information

Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network.

ABSTRACT: The Semantically Annotated Media (SAM) project aims to provide a flexible platform for searching, browsing, and indexing medical learning objects (MLOs) based on a semantic network derived from established classification systems. Primarily, SAM supports the Aachen emedia skills lab, but SAM is ready for indexing distributed content and the Simple Knowledge Organizing System standard provides a means for easily upgrading or even exchanging SAM's semantic network. There is a lack of research addressing the usability of MLO indexes or search portals like SAM and the user behavior with such platforms.The purpose of this study was to assess the usability of SAM by investigating characteristic user behavior of medical students accessing MLOs via SAM.In this study, we chose a mixed-methods approach. Lean usability testing was combined with usability inspection by having the participants complete four typical usage scenarios before filling out a questionnaire. The questionnaire was based on the IsoMetrics usability inventory. Direct user interaction with SAM (mouse clicks and pages accessed) was logged.The study analyzed the typical usage patterns and habits of students using a semantic network for accessing MLOs. Four scenarios capturing characteristics of typical tasks to be solved by using SAM yielded high ratings of usability items and showed good results concerning the consistency of indexing by different users. Long-tail phenomena emerge as they are typical for a collaborative Web 2.0 platform. Suitable but nonetheless rarely used keywords were assigned to MLOs by some users.It is possible to develop a Web-based tool with high usability and acceptance for indexing and retrieval of MLOs. SAM can be applied to indexing multicentered repositories of MLOs collaboratively.

SUBMITTER: Tix N

PROVIDER: S-EPMC5041367 | biostudies-other | 2015 Nov

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network.

Tix Nadine N Gießler Paul P Ohnesorge-Radtke Ursula U Spreckelsen Cord C

JMIR medical education 20151111 2

<h4>Background</h4>The Semantically Annotated Media (SAM) project aims to provide a flexible platform for searching, browsing, and indexing medical learning objects (MLOs) based on a semantic network derived from established classification systems. Primarily, SAM supports the Aachen emedia skills lab, but SAM is ready for indexing distributed content and the Simple Knowledge Organizing System standard provides a means for easily upgrading or even exchanging SAM's semantic network. There is a lac ...[more]

PMID: 27731860

Similar Datasets

Project description:BackgroundDigital learning environments have become very common in the training of medical professionals, and students often use such platforms for exam preparation. Multiple choice questions (MCQs) are a common format in medical exams and are used by students to prepare for said exams.ObjectiveWe aimed to examine whether particular learning activities contributed more strongly than others to users' exam performance.MethodsWe analyzed data from users of an online platform that provides learning materials for medical students in preparation for their final exams. We analyzed whether the number of learning cards viewed and the number of MCQs taken were positively related to learning outcomes. We also examined whether viewing learning cards or answering MCQs was more effective. Finally, we tested whether taking individual notes predicted learning outcomes, and whether taking notes had an effect after controlling for the effects of learning cards and MCQs. Our analyses from the online platform Amboss are based on user activity data, which supplied the number of learning cards studied and test questions answered. We also included the number of notes from each of those 23,633 users who had studied at least 200 learning cards and had answered at least 1000 test exam questions in the 180 days before their state exam. The activity data for this analysis was collected retrospectively, using Amboss archival usage data from April 2014 to April 2017. Learning outcomes were measured using the final state exam scores that were calculated by using the answers voluntarily entered by the participants.ResultsWe found correlations between the number of cards studied (r=.22; P<.001) and the number of test questions that had been answered (r=.23; P<.001) with the percentage of correct answers in the learners' medical exams. The number of test questions answered still yielded a significant effect, even after controlling for the number of learning cards studied using a hierarchical regression analysis (?=.14; P<.001; ?R2=.017; P<.001). We found a negative interaction between the number of learning cards and MCQs, indicating that users with high scores for learning cards and MCQs had the highest exam scores. Those 8040 participants who had taken at least one note had a higher percentage of correct answers (80.94%; SD=7.44) than those who had not taken any notes (78.73%; SD=7.80; t23631=20.95; P<.001). In a stepwise regression, the number of notes the participants had taken predicted the percentage of correct answers over and above the effect of the number of learning cards studied and of the number of test questions entered in step one (?=.06; P<.001; ?R2=.004; P<.001).ConclusionsThese results show that online learning platforms are particularly helpful whenever learners engage in active elaboration in learning material, such as by answering MCQs or taking notes.

Project description:IntroductionRecent conceptualizations of self-regulated learning acknowledge the importance of co-regulation, i.e., students' interactions with others in their networks to support self-regulation. Using a social network approach, the aim of this study is to explore relationships between characteristics of medical students' co-regulatory networks, perceived learning opportunities, and self-regulated learning.MethodsThe authors surveyed 403 undergraduate medical students during their clinical clerkships (response rate 65.5%). Using multiple regression analysis, structural equation modelling techniques, and analysis of variance, the authors explored relationships between co-regulatory network characteristics (network size, network diversity, and interaction frequency), students' perceptions of learning opportunities in the workplace setting, and self-reported self-regulated learning.ResultsAcross all clerkships, data showed positive relationships between tie strength and self-regulated learning (β = 0.095, p < 0.05) and between network size and tie strength (β = 0.530, p < 0.001), and a negative relationship between network diversity and tie strength (β = -0.474, p < 0.001). Students' perceptions of learning opportunities showed positive relationships with both self-regulated learning (β = 0.295, p < 0.001) and co-regulatory network size (β = 0.134, p < 0.01). Characteristics of clerkship contexts influenced both co-regulatory network characteristics (size and tie strength) and relationships between network characteristics, self-regulated learning, and students' perceptions of learning opportunities.DiscussionThe present study reinforces the importance of co-regulatory networks for medical students' self-regulated learning during clinical clerkships. Findings imply that supporting development of strong networks aimed at frequent co-regulatory interactions may enhance medical students' self-regulated learning in challenging clinical learning environments. Social network approaches offer promising ways of further understanding and conceptualising self- and co-regulated learning in clinical workplaces.

Project description:Chat Generative Pretrained Transformer (ChatGPT) is a large language model developed by OpenAI that has gained widespread interest. It has been cited for its potential impact on health care and its beneficial role in medical education. However, there is limited investigation into its use among medical students. In this study, we evaluated the frequency of ChatGPT use, motivations for use, and preference for ChatGPT over existing resources among medical students in the United States. Data was collected from an original survey consisting of 14 questions assessing the frequency and usage of ChatGPT in various contexts within medical education. The survey was distributed via email lists, group messaging applications, and classroom lectures to medical students across the United States. Responses were collected between August and October 2023. One hundred thirty-one participants completed the survey and were included in the analysis. Of the total, 48.9% respondents responded that they have used ChatGPT in medical studies. Among ChatGPT users, 43.7% of respondents report using ChatGPT weekly, several times per week, or daily. ChatGPT is most used for writing, revising, editing, and summarizing purposes. 37.5% and 41.3% of respondents reported using ChatGPT more than 25% of the working time for these tasks respectively. Among respondents who have not used ChatGPT, more than 50% of respondents reported they were extremely unlikely or unlikely to use ChatGPT across all surveyed scenarios. ChatGPT users report they are more likely to use ChatGPT over directly asking professors or attendings (45.3%), textbooks (42.2%), and lectures (31.7%), and least likely to be used over popular flashcard application Anki (11.1%) and medical education videos (9.5%). ChatGPT is an increasingly popular resource among medical students, with many preferring ChatGPT over other traditional resources such as professors, textbooks, and lectures. Its impact on medical education will only continue to grow as its capabilities improve.

Project description:Biomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature.The aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE.Our approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity. Once the most similar documents for a given input document are retrieved, we rank their MeSH terms to choose the most suitable set for the input document. To do this, we define a scoring function that takes into account the frequency of the term into the set of retrieved documents and the similarity between the input document and each retrieved document. In addition, we implement guidelines proposed by human curators to annotate MEDLINE articles; in particular, the heuristic that says if 3 MeSH terms are proposed to classify an article and they share the same ancestor, they should be replaced by this ancestor. The representation of the MeSH thesaurus as a graph database allows us to employ graph search algorithms to quickly and easily capture hierarchical relationships such as the lowest common ancestor between terms.Our experiments show promising results with an F1 of 69% on the test dataset.To the best of our knowledge, this is the first work that combines search and graph database technologies for the task of biomedical semantic indexing. Due to its horizontal scalability, ElasticSearch becomes a real solution to index large collections of documents (such as the bibliographic database MEDLINE). Moreover, the use of graph search algorithms for accessing MeSH information could provide a support tool for cataloging MEDLINE abstracts in real time.

Dataset Information

Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network.

Publications

Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets