scholarly journals Learning Analytics Applied to Clinical Diagnostic Reasoning Using an NLP-based Virtual Patient Simulator: a Case Study (Preprint)

10.2196/24372 ◽  
2020 ◽  
Author(s):  
Raffaello Furlan ◽  
Mauro Gatti ◽  
Roberto Menè ◽  
Dana Shiffer ◽  
Chiara Marchiori ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Diagnostic Reasoning ◽  
Virtual Patient ◽  
Patient Simulator ◽  
Clinical Diagnostic

Learner Analytics Applied to the Clinical Diagnostic Reasoning with Hepius Cognitive Tutor and Simulator (Preprint)

2020 ◽  
Author(s):  
Raffaello Furlan ◽  
Mauro Gatti ◽  
Roberto Menè ◽  
Dana Shiffer ◽  
Chiara Marchiori ◽  
...  
Keyword(s):  
Language Processing ◽  
Medical Knowledge ◽  
Final Diagnosis ◽  
Relevant Information ◽  
Diagnostic Reasoning ◽  
Diagnostic Process ◽  
Patient Simulator ◽  
Cognitive Tutor ◽  
Clinical Diagnostic ◽  
Maximal Sensitivity

BACKGROUND Virtual Patient Simulator is a tool that may generate a multi-dimensional representation of the student’s medical knowledge by analyzing the recordings of the user’s actions during a clinical simulation. Adequate metrics may provide teachers with valuable learning information. OBJECTIVE To describe the analytic metrics we used to analyze the clinical diagnostic reasoning of medical students obtained by a novel Cognitive Tutor and Simulator named Hepius embedding Natural Language Processing (NLP) techniques. METHODS Two clinical case simulations (Tests) were created to tune our metrics. During each simulation, students’ actions were logged into the program data base for off-line analysis. Twenty-six students, attending the 5th year of the School of Medicine at Humanitas University, underwent Test 1 (April 12th 2019) which simulated a patient suffering from dyspnea. Test 2 (May 21st 2019) dealt with abdominal pain and was attended by 36 students. Overall students’ performance was split into 7 issues: 1) the identification of relevant information in the given clinical scenario (SC); 2) history taking (AN); 3) physical exam (PE); 4) medical tests (MT) ordering; 5) diagnostic hypotheses (HY) setting; 6) binary analysis fulfillment (BA); 7) final diagnosis (RS) setting. Sensitivity (percentage of relevant information found) and precision (percentage of correct actions performed) metrics were computed for each issue and combined into a harmonic (F1), thereby obtaining a single score (1= maximal sensitivity and precision) evaluating the student’s performances. The seven F1-metric scores were further combined to obtained a convenient index assessing the student’s overall performances.The seven metrics were further grouped to reflect the student’s capability to collect (SC, AN, PE and MT) and to analyze (HY, BA and RS) information. A methodological score was computed on the basis of the discordance between the diagnostic pathway followed by the student and a reference one, previously defined by the teacher. RESULTS Mean overall scores were consistent between the two tests (0.6.±0.05 for Test 1 and 0.5±0.05 for Test 2). For each student, overall performance was achieved by a different contribution in collecting and analyzing information. Methodological scores highlighted some discordance between the reference diagnostic pattern previously set by the teachear and the one pursued by the student. CONCLUSIONS Different components of the student’s diagnostic process may be disentangled and quantified by appropriate metrics applied on students’ actions recorded while addressing a virtual case. Such an approach may help teachers in giving students individualized feedbacks aimed at filling up knowledge drawbacks and methodological inconsistencies.

scholarly journals Automated Feedback Is Nice and Human Presence Makes It Better: Teachers’ Perceptions of Feedback by Means of an E-Portfolio Enhanced with Learning Analytics

2021 ◽  
Vol 11 (6) ◽  
pp. 278
Author(s):  
Pihel Hunt ◽  
Äli Leijen ◽  
Marieke van der Schaaf
Keyword(s):  
Learning Analytics ◽  
Electronic Portfolio ◽  
Human Interaction ◽  
Web Based ◽  
Teachers Perceptions ◽  
Use Of Technology ◽  
Group Interviews ◽  
Automated Feedback ◽  
Development Course

While there is now extensive research on feedback in the context of higher education, including pre-service teacher education, little has been reported regarding the use of feedback from teachers to other teachers. Moreover, literature on the potential advantages that the use of technology, for example electronic portfolios and learning analytics, has in improving feedback in the in-service workplace practices, is also sparse. Therefore, the aim of this exploratory case study was to explore how in-service teachers perceived the peer feedback they received and provided through a web-based electronic portfolio during a professional development course carried out in their workplace. Questionnaire and interview data were collected from 38 teachers who received feedback through a learning analytics enhanced electronic portfolio and from 23 teachers who received feedback only by the electronic portfolio. Additionally, one individual and four focus group interviews were conducted with 15 teachers who were the feedback providers. Several common topics were identified in the interviews with the feedback receivers and providers, involving the benefits and challenges of human interaction and the flexibility of the feedback process that the electronic portfolio offered. The results also revealed better feedback experience within the group of teachers who received extra feedback by means of learning analytics. It is concluded that although an electronic portfolio provides a useful tool in terms of flexibility in the provision and receipt of feedback, the need for human interaction was acknowledged.

scholarly journals Understanding Learner Behaviour in Online Courses through Learning Analytics

2021 ◽  
pp. 381-390
Author(s):  
D. Thammi Raju ◽  
G. R. K. Murthy ◽  
S. B. Khade ◽  
B. Padmaja ◽  
B. S. Yashavanth ◽  
...  
Keyword(s):  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Learning Experience ◽  
Training Needs ◽  
Online Course ◽  
Quality Of Education ◽  
Time Data

Building an effective online course requires an understanding of learning analytics. The study assumes significance in the COVID 19 pandemic situation as there is a sudden surge in online courses. Analysis of the online course using the data generated from the Moodle Learning Management System (LMS), Google Forms and Google Analytics was carried out to understand the tenants of an effective online course. About 515 learners participated in the initial pre-training needs & expectations’ survey and 472 learners gave feedback at the end, apart from the real-time data generated from LMS and Google Analytics during the course period. This case study analysed online learning behaviour and the supporting learning environment and suggest critical factors to be at the centre stage in the design and development of online courses; leads to the improved online learning experience and thus the quality of education. User needs, quality of resources and effectiveness of online courses are equally important in taking further online courses.

The Marginal Syllabus

2018 ◽  
pp. 17-58 ◽  
Author(s):  
Jeremiah H. Kalir ◽  
Francisco Perez
Keyword(s):  
Discourse Analysis ◽  
Data Collection ◽  
Learning Analytics ◽  
Meaning Making ◽  
Educational Equity ◽  
Political Agency ◽  
Design Experiment ◽  
Social Design ◽  
Web Annotation

This case study examines educator learning as mediated by open web annotation among sociopolitical texts and contexts. The chapter introduces annotation practices and conceptualizes intertextuality to describe how open web annotation creates dialogic spaces which gather together people and texts, coordinates meaning-making, and encourages political agency. This perspective on texts-as-contexts is used to present and analyze educator participation in the Marginal Syllabus, a social design experiment that leverages open web annotation to foster conversation about educational equity. One conversation from the Marginal Syllabus is analyzed using mixed method approaches to data collection, analysis, and the presentation of findings. Learning analytics and discourse analysis detail how open web annotation mediated educator participation among sociopolitical texts and contexts of professional relevance. The chapter concludes by discussing open web annotation as a means of coordinating educator participation in public conversations about sociopolitical issues related to educational equity.

scholarly journals The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study

2020 ◽  
Vol 45 ◽  
pp. 100725 ◽  
Author(s):  
Christothea Herodotou ◽  
Bart Rienties ◽  
Martin Hlosta ◽  
Avinash Boroowa ◽  
Chrysoula Mangafa ◽  
...  
Keyword(s):  
Distance Learning ◽  
Learning Analytics ◽  
Longitudinal Case Study ◽  
Predictive Learning

scholarly journals Aligning learning design and learning analytics through instructor involvement: a MOOC case study

2019 ◽  
Vol 27 (5-6) ◽  
pp. 685-698 ◽  
Author(s):  
Erkan Er ◽  
Eduardo Gómez-Sánchez ◽  
Yannis Dimitriadis ◽  
Miguel L. Bote-Lorenzo ◽  
Juan I. Asensio-Pérez ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Learning Design
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