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Mining Individual Learning Topics in Course Reviews Based on Author Topic Model

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Title: Mining Individual Learning Topics in Course Reviews Based on Author Topic Model
Language: English
Authors: Liu, Sanya, Ni, Cheng, Liu, Zhi, Peng, Xian, Cheng, Hercy N. H.
Source: International Journal of Distance Education Technologies. Jul-Sep 2017 15(3):1-14.
Availability: IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: http://www.igi-global.com/journals
Peer Reviewed: Y
Page Count: 14
Publication Date: 2017
Document Type: Journal Articles
Reports - Research
Descriptors: Online Courses, Student Records, Learning Strategies, Cognitive Style, Data Analysis, Data Collection, Student Interests, Units of Study, Models, Electronic Learning, Information Utilization
DOI: 10.4018/IJDET.2017070101
ISSN: 1539-3100
Abstract: Nowadays, Massive Open Online Courses (MOOCs) have obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data in education. To mine useful information from these data, we need to use educational data mining and learning analysis technique to study the learning feelings and discussed topics among learners. This paper aims to mine and analyze topic information hidden in the unstructured reviews data in MOOCs; a novel author topic model based on an unsupervised learning idea is proposed to extract learning topics for each learner. According to the experimental results, we will analyze and focus on interests of learners, which facilitates further personalized course recommendation and improves the quality of online courses.
Abstractor: As Provided
Number of References: 25
Entry Date: 2017
Accession Number: EJ1145219
Database: ERIC
FullText Text:
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  Data: Mining Individual Learning Topics in Course Reviews Based on Author Topic Model
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  Data: English
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Sanya%22">Liu, Sanya</searchLink><br /><searchLink fieldCode="AR" term="%22Ni%2C+Cheng%22">Ni, Cheng</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zhi%22">Liu, Zhi</searchLink><br /><searchLink fieldCode="AR" term="%22Peng%2C+Xian%22">Peng, Xian</searchLink><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Hercy+N%2E+H%2E%22">Cheng, Hercy N. H.</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Distance+Education+Technologies%22"><i>International Journal of Distance Education Technologies</i></searchLink>. Jul-Sep 2017 15(3):1-14.
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  Data: IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: http://www.igi-global.com/journals
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  Data: Y
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  Data: 14
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Online+Courses%22">Online Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Records%22">Student Records</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Strategies%22">Learning Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Style%22">Cognitive Style</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Collection%22">Data Collection</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Interests%22">Student Interests</searchLink><br /><searchLink fieldCode="DE" term="%22Units+of+Study%22">Units of Study</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Utilization%22">Information Utilization</searchLink>
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  Data: 10.4018/IJDET.2017070101
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  Data: 1539-3100
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Nowadays, Massive Open Online Courses (MOOCs) have obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data in education. To mine useful information from these data, we need to use educational data mining and learning analysis technique to study the learning feelings and discussed topics among learners. This paper aims to mine and analyze topic information hidden in the unstructured reviews data in MOOCs; a novel author topic model based on an unsupervised learning idea is proposed to extract learning topics for each learner. According to the experimental results, we will analyze and focus on interests of learners, which facilitates further personalized course recommendation and improves the quality of online courses.
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  Data: As Provided
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        Value: 10.4018/IJDET.2017070101
    Languages:
      – Text: English
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      – SubjectFull: Student Records
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      – SubjectFull: Learning Strategies
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      – SubjectFull: Cognitive Style
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      – SubjectFull: Data Analysis
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      – SubjectFull: Data Collection
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      – SubjectFull: Student Interests
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      – SubjectFull: Models
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      – SubjectFull: Electronic Learning
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      – SubjectFull: Information Utilization
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      – TitleFull: Mining Individual Learning Topics in Course Reviews Based on Author Topic Model
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            NameFull: Cheng, Hercy N. H.
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