Understanding Topic Models in Context: A Mixed-Methods Approach to the Meaningful Analysis of Large Document Collections

Eickhoff, Matthias; Wieneke, Runhild

doi:10.24251/hicss.2018.113

Cited by 21 publications

(24 citation statements)

References 26 publications

(32 reference statements)

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“…Prior studies of e-learning in medicine have thus far have been on a small scale, with only a minority having greater than 100 participants; with inclusion of over 1000 responses, this study has been able to apply machine learning techniques to this larger natural language dataset. Through these machine learning algorithms it is demonstrated a method for reproducibility and objectivity in the analysis of free text data [ 64 , 65 ]. This study is limited however, in not collecting data on demographic characteristics of feedback respondents.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of an international medical E-learning course with natural language processing and machine learning

Borakati

2021

BMC Med Educ

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Background In the context of the ongoing pandemic, e-learning has become essential to maintain existing medical educational programmes. Evaluation of such courses has thus far been on a small scale at single institutions. Further, systematic appraisal of the large volume of qualitative feedback generated by massive online e-learning courses manually is time consuming. This study aimed to evaluate the impact of an e-learning course targeting medical students collaborating in an international cohort study, with semi-automated analysis of feedback using text mining and machine learning methods. Method This study was based on a multi-centre cohort study exploring gastrointestinal recovery following elective colorectal surgery. Collaborators were invited to complete a series of e-learning modules on key aspects of the study and complete a feedback questionnaire on the modules. Quantitative data were analysed using simple descriptive statistics. Qualitative data were analysed using text mining with most frequent words, sentiment analysis with the AFINN-111 and syuzhet lexicons and topic modelling using the Latent Dirichlet Allocation (LDA). Results One thousand six hundred and eleventh collaborators from 24 countries completed the e-learning course; 1396 (86.7%) were medical students; 1067 (66.2%) entered feedback. 1031 (96.6%) rated the quality of the course a 4/5 or higher (mean 4.56; SD 0.58). The mean sentiment score using the AFINN was + 1.54/5 (5: most positive; SD 1.19) and + 0.287/1 (1: most positive; SD 0.390) using syuzhet. LDA generated topics consolidated into the themes: (1) ease of use, (2) conciseness and (3) interactivity. Conclusions E-learning can have high user satisfaction for training investigators of clinical studies and medical students. Natural language processing may be beneficial in analysis of large scale educational courses.

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Section: Discussionmentioning

confidence: 99%

Evaluation of an international medical E-learning course with natural language processing and machine learning

Borakati

2021

BMC Med Educ

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Section: Methodsmentioning

confidence: 99%

Instructional Designers’ Use of Informal Learning: How Can We All Support Each Other in Times of Crisis?

Abramenka‐Lachheb¹,

Lachheb²,

Leung³

et al. 2021

JAID

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The purpose of this study was to investigate instructional designers' needs during a rapid transition to remote learning due to COVID-related shutdowns of campuses, schools, and organizations. For the purpose of this study, we chose a large Facebook group for instructional designers as a medium of informal learning. Following a mixed-method study design, we answered the following research questions: (RQ1) What needs did instructional designers express and report in an informal learning environment during the In what way did an informal learning environment facilitate peer-to-peer support for instructional designers? The findings of this study highlighted diverse expressed needs, ranging from educational technology needs to COVID-19 specific and general pedagogical needs. We found that peer-to-peer support between instructional designers was facilitated in an informal learning environment through an exchange of ideas and advice that were prompted by questions/requests for support. The study begins to document the needs of instructional designers during the COVID-19 crisis in instructional design technology (IDT) literature. The online environment we studied seems to provide numerous options for informal learning activities for instructional design professionals.

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“…To answer the research questions, we specifically followed a nested mixed-method design (Eickhoff & Wieneke, 2018;Hesse-Biber, 2010). The key characteristic of this type of mixed-method research is to employ at least one additional method within another primary method (Eickhoff & Wieneke, 2018). That is, first, we used a quantitative approach to collect a large textual data set-995 posts published in the Instructional Designer Facebook group.…”

Section: Methodsmentioning

confidence: 99%

Untitled

2021

JAID

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This journal will provide a peer-reviewed format for the publication of scholarly articles in the field of applied instructional design. The journal recognizes the role of the practitioner in the work environment and realizes that outside constraints may limit the data collection and analysis process in applied settings. The limitations of real-world instructional design of the practitioner can still provide valuable knowledge for the field. Sponsoring OrganizationJAID is a publication of the Association for Educational Communications and Technology (AECT).

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Understanding Topic Models in Context: A Mixed-Methods Approach to the Meaningful Analysis of Large Document Collections

Abstract: In

Cited by 21 publications

References 26 publications

Evaluation of an international medical E-learning course with natural language processing and machine learning

Evaluation of an international medical E-learning course with natural language processing and machine learning

Instructional Designers’ Use of Informal Learning: How Can We All Support Each Other in Times of Crisis?

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