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Temporal Learning Analytics for Adaptive Assessment

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Bibliographic Details
Title: Temporal Learning Analytics for Adaptive Assessment
Language: English
Authors: Papamitsiou, Zacharoula, Economides, Anastasios A.
Source: Journal of Learning Analytics. 2014 1(3):165-168.
Availability: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Peer Reviewed: Y
Page Count: 4
Publication Date: 2014
Document Type: Journal Articles
Reports - Research
Education Level: High Schools
Secondary Education
Higher Education
Postsecondary Education
Descriptors: Time Factors (Learning), Predictor Variables, Student Behavior, Academic Achievement, Data Analysis, Adaptive Testing, High School Students, Undergraduate Students, Foreign Countries, Educational Research, Case Studies, Least Squares Statistics
Geographic Terms: Greece
ISSN: 1929-7750
Abstract: Accurate and early predictions of student performance could significantly affect interventions during teaching and assessment, which gradually could lead to improved learning outcomes. In our research, we seek to identify and formalize temporal parameters as predictors of performance ("temporal learning analytics" or TLA) and examine students' temporal behaviour during testing (i.e., in terms of time-spent). The goal is to specify a functional set of parameters that will be embedded in an adaptive assessment system in order to contribute towards the personalization of feedback services. In this paper, we present the motivation and rationale for our work, along with our methodology, initial results, contributions so far, and plans for future work.
Abstractor: As Provided
Number of References: 9
Entry Date: 2017
Accession Number: EJ1126993
Database: ERIC
FullText Text:
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  Data: Temporal Learning Analytics for Adaptive Assessment
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Learning+Analytics%22"><i>Journal of Learning Analytics</i></searchLink>. 2014 1(3):165-168.
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  Data: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
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  Data: <searchLink fieldCode="DE" term="%22Time+Factors+%28Learning%29%22">Time Factors (Learning)</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+Testing%22">Adaptive Testing</searchLink><br /><searchLink fieldCode="DE" term="%22High+School+Students%22">High School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Research%22">Educational Research</searchLink><br /><searchLink fieldCode="DE" term="%22Case+Studies%22">Case Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Least+Squares+Statistics%22">Least Squares Statistics</searchLink>
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  Data: 1929-7750
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  Data: Accurate and early predictions of student performance could significantly affect interventions during teaching and assessment, which gradually could lead to improved learning outcomes. In our research, we seek to identify and formalize temporal parameters as predictors of performance ("temporal learning analytics" or TLA) and examine students' temporal behaviour during testing (i.e., in terms of time-spent). The goal is to specify a functional set of parameters that will be embedded in an adaptive assessment system in order to contribute towards the personalization of feedback services. In this paper, we present the motivation and rationale for our work, along with our methodology, initial results, contributions so far, and plans for future work.
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  Data: 2017
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  Data: EJ1126993
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      – Text: English
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        PageCount: 4
        StartPage: 165
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        Type: general
      – SubjectFull: Predictor Variables
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      – SubjectFull: Student Behavior
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      – SubjectFull: Data Analysis
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      – SubjectFull: Adaptive Testing
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Least Squares Statistics
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