On the Generation of E-Learning Resources Using Business Process, Natural Language Processing, and Web Services

AbstractIn recent years, there has been increasing interest in the field of natural language processing. Determining which syntactic function is right for a specific word is an important task in this field, being useful for a variety of applications like understanding texts, automatic translation and question-answering applications and even in e-learning systems. In the Romanian language, this is an even harder task because of the complexity of the grammar. The present paper falls within the field of “Natural Language Processing”, but it also blends with other concepts such as “Gamification”, “Social Choice Theory” and “Wisdom of the Crowd”. There are two main purposes for developing the application in this paper:a) For students to have at their disposal some support through which they can deepen their knowledge about the syntactic functions of the parts of speech, a knowledge that they have accumulated during the teaching hours at schoolb) For collecting data about how the students make their choices, how do they know which grammar role is correct for a specific word, these data being primordial for replicating the learning process

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Extracting Business Process Models Using Natural Language Processing (NLP) Techniques

2017 IEEE 19th Conference on Business Informatics (CBI) ◽

10.1109/cbi.2017.41 ◽

2017 ◽

Cited By ~ 7

Author(s):

Konstantinos Sintoris ◽

Kostas Vergidis

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Business Process ◽

Process Models ◽

Business Process Models

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

BMC Medical Education ◽

10.1186/s12909-021-02609-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Aditya Borakati

Keyword(s):

Machine Learning ◽

Cohort Study ◽

Natural Language Processing ◽

Medical Students ◽

Text Mining ◽

Natural Language ◽

Language Processing ◽

Small Scale ◽

E Learning ◽

The Impact

Abstract 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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Evaluation of an International Medical E-Learning Course with Natural Language Processing and Machine Learning

10.21203/rs.3.rs-116209/v1 ◽

2020 ◽

Author(s):

Aditya Borakati

Keyword(s):

Machine Learning ◽

Cohort Study ◽

Natural Language Processing ◽

Medical Students ◽

Text Mining ◽

Natural Language ◽

Language Processing ◽

Small Scale ◽

E Learning ◽

The Impact

Abstract 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: 1,611 collaborators from 24 countries completed the e-learning course; 1,396 (86.7%) were medical students; 1,067 (66.2%) entered feedback. 1,031 (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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