Automatic Classification of Online Discussions and Other Learning Traces to Detect Cognitive Presence.

Cognitive presence is a core construct of the Community of Inquiry (CoI) framework. It is considered crucial for deep and meaningful online-based learning. CoI-based real-time dashboards visualizing students' cognitive presence may help instructors to monitor and support students' learning progress. Such real-time classifiers are often based on the linguistic analysis of the content of posts made by students. It is unclear whether these classifiers could be improved by considering other learning traces, such as files attached to students' posts. We aimed to develop a German-language cognitive presence classifier that includes linguistic analysis using the Linguistic Inquiry and Word Count (LIWC) tool and other learning traces based on 1,521 manually coded meaningful units from an online-based university course. As learning traces, we included not only the linguistic features from the LIWC tool, but also features such as attaching files to a post, tagging, or using terms from the course glossary. We used the k-nearest neighbor method, a random forest model, and a multilayer perceptron as classifiers. The results showed an accuracy of up to 82% and a Cohen's κ of 0.76 for the cognitive presence classifier for German posts. Including learning traces did not improve the predictive ability. In conclusion, we developed an automatic classifier for German-language courses based on a linguistic analysis of students' posts. This classifier is a step toward a teacher dashboard. Our work also provides the first fully CoI-coded German dataset for future research on cognitive presence.

A Linked Open Data-Based Terminology to Describe Libre/Free and Open-source Software: Incremental Development Study.

BACKGROUND: There is a variety of libre/free and open-source software (LIFOSS) products for medicine and health care. To support health care and IT professionals select an appropriate software product for given tasks, several comparison studies and web platforms, such as Medfloss.org, are available. However, due to the lack of a uniform terminology for health informatics, ambiguous or imprecise terms are used to describe the functionalities of LIFOSS. This makes comparisons of LIFOSS difficult and may lead to inappropriate software selection decisions. Using Linked Open Data (LOD) promises to address these challenges. OBJECTIVE: We describe LIFOSS systematically with the help of the underlying Health Information Technology Ontology (HITO). We publish HITO and HITO-based software product descriptions using LOD to obtain the following benefits: (1) linking and reusing existing terminologies and (2) using Semantic Web tools for viewing and querying the LIFOSS data on the World Wide Web. METHODS: HITO was incrementally developed and implemented. First, classes for the description of software products in health IT evaluation studies were identified. Second, requirements for describing LIFOSS were elicited by interviewing domain experts. Third, to describe domain-specific functionalities of software products, existing catalogues of features and enterprise functions were analyzed and integrated into the HITO knowledge base. As a proof of concept, HITO was used to describe 25 LIFOSS products. RESULTS: HITO provides a defined set of classes and their relationships to describe LIFOSS in medicine and health care. With the help of linked or integrated catalogues for languages, programming languages, licenses, features, and enterprise functions, the functionalities of LIFOSS can be precisely described and compared. We publish HITO and the LIFOSS descriptions as LOD; they can be queried and viewed using different Semantic Web tools, such as Resource Description Framework (RDF) browsers, SPARQL Protocol and RDF Query Language (SPARQL) queries, and faceted searches. The advantages of providing HITO as LOD are demonstrated by practical examples. CONCLUSIONS: HITO is a building block to achieving unambiguous communication among health IT professionals and researchers. Providing LIFOSS product information as LOD enables barrier-free and easy access to data that are often hidden in user manuals of software products or are not available at all. Efforts to establish a unique terminology of medical and health informatics should be further supported and continued.

Medical informatics and digital health multilingual ontology (MIMO): A tool to improve international collaborations.

BACKGROUND: Even if English is the leading language for international communication, it is essential to keep in mind that research runs at the local level by local teams generally communicating in their local/national language, especially in Europe among European projects. OBJECTIVE: Therefore, the European Federation for Medical Informatics - Working Group on Health Informatics for Inter-regional Cooperation" has one objective: To develop a multilingual ontology focusing on Health Informatics and Digital Health as a collaboration tool that improves international and, in particular, European collaborations. RESULTS: We have developed the Medical Informatics and Digital Health Multilingual Ontology (MIMO). Hosted on the Health Terminology/Ontology Portal (HeTOP), MIMO contains around 1,000 concepts, 460 MeSH Descriptors, 220 MeSH Concepts, and more than 300 newly created concepts. MIMO is continuously updated to comprise as recent as possible concepts and their translations in more than 30 languages. Moreover, the MIMO's development team constantly improves MIMO content and supporting information. Thus, during workshop discussions and one-on-one exchanges, the MIMO team has collected domain experts' opinions about the community's interests and suggestions for future enhancements. Moreover, MIMO will be integrated to support the annotation and categorization of research products into the HosmartAI European project involving more than 20 countries around Europe and worldwide. CONCLUSION: MIMO is hosted by HeTOP (Health Terminology/Ontology Portal), which integrates 100 terminologies and ontologies in 55 languages. MIMO is freely available online. MIMO is portable to other knowledge platforms as part of MIMO's main aims to facilitate communication between medical librarians, translators, and researchers as well as to support students' self-learning.

Use of Natural Language Processing for Precise Retrieval of Key Elements of Health IT Evaluation Studies.

Having precise information about health IT evaluation studies is important for evidence-based decisions in medical informatics. In a former feasibility study, we used a faceted search based on ontological modeling of key elements of studies to retrieve precisely described health IT evaluation studies. However, extracting the key elements manually for the modeling of the ontology was time and resource-intensive. We now aimed at applying natural language processing to substitute manual data extraction by automatic data extraction. Four methods (Named Entity Recognition, Bag-of-Words, Term-Frequency-Inverse-Document-Frequency, and Latent Dirichlet Allocation Topic Modeling were applied to 24 health IT evaluation studies. We evaluated which of these methods was best suited for extracting key elements of each study. As gold standard, we used results from manual extraction. As a result, Named Entity Recognition is promising but needs to be adapted to the existing study context. After the adaption, key elements of studies could be collected in a more feasible, time- and resource-saving way.

Implementing National Electronic Health Records in Nursing Homes in Tyrol: A Nursing Management Perspective.

BACKGROUND: Health information technologies as electronic health records (EHR) have the potential to improve the quality and efficiency of health care. Implementing national EHR in nursing homes in Tyrol is a required task within the next years. OBJECTIVES: To explore and analyze the current situation of information exchange in nursing homes in Tyrol as well as expected potentials and challenges of an ELGA implementation from a nursing management perspective. METHODS: Semi-structured interviews with nine nursing managers of nursing homes in Tyrol were conducted and analyzed using qualitative content analysis. RESULTS: The explorative interviews offered a glance at the actual information exchange with other institutions and at the perception of nursing managers regarding potentials (e.g. decreasing organizational effort) and challenges (e.g. incompleteness of available patient information) of the nationally mandated implementation of ELGA in the nursing homes in Tyrol. CONCLUSION: The interviews with the nine nursing managers of nursing homes in Tyrol revealed some hopes (e.g. the reduction of double documentation) that ELGA will probably not be able to fulfill.

Towards Precise Descriptions of Medical Free/Libre and Open Source Software.

The web portal Medfloss.org lists over 360 medical free/libre and open source software (MEDFLOSS) projects. These projects are described with the help of a self-developed nomenclature. Due to inconsistencies, the nomenclature shall be replaced by HITO, the Health IT Ontology. HITO is developed iteratively based on different use cases. This paper aims to describe methods and results of the second HITO use case in which HITO is extended to improve the description, retrieval and comparisons of MEDFLOSS projects on Medfloss.org. We use a mixed-methods approach to add concepts and relationships to describe MEDFLOSS precisely. The resulting HITO version stresses functional descriptions based on features and supported enterprise functions, rather than just describing technical characteristics. However, describing a larger number of MEDFLOSS projects requires the commitment of the community.

An Ontology for Describing Health IT Interventions: Methodological Considerations.

Health informatics as a young, interdisciplinary discipline lacks a unified terminology in some areas. This is especially true when trying to properly describe health informatics interventions developed and deployed to improve quality and efficiency of patient care. We aim at developing a health IT ontology which allows systematically describing health IT interventions. To achieve this, we combine a deductive and an inductive approach. First results are promising and may later be extended by a folksonomy.

Impact of Students' Presence and Course Participation on Learning Outcome in Co-Operative Online-based Courses.

Socio-constructive instructional designs for online-based learning focus on interaction and communication of students to allow in-depth learning. The objective of this study is to analyze whether increased interaction of students in online-based learning settings may contribute to better outcome. We developed indicators for presence, participation, and interactivity of students. We extracted log data from the learning management system for 31 students in 10 online courses (n=123 course attendances). We correlated indicators to final grades and also applied a decision tree based machine learning approach. We found only weak to moderate correlations between the indicators and final grades, but acceptable results concerning prediction of students' success based on the indicators. Our results support the theory that student presence and participation in online-based courses is related to learning outcome.