Both sides of the story: comparing student-level data on reading performance from administrative registers to application generated data from a reading app
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Both sides of the story : comparing student-level data on reading performance from administrative registers to application generated data from a reading app. / Sortkær, Bent; Smith, Emil; Reimer, David; Oehmcke, Stefan; Andersen, Ida Gran.
I: EPJ Data Science, Bind 10, Nr. 1, 44, 2021.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Both sides of the story
T2 - comparing student-level data on reading performance from administrative registers to application generated data from a reading app
AU - Sortkær, Bent
AU - Smith, Emil
AU - Reimer, David
AU - Oehmcke, Stefan
AU - Andersen, Ida Gran
PY - 2021
Y1 - 2021
N2 - The use of various learning apps in school settings is growing and thus producing an increasing amount of usage generated data. However, this usage generated data has only to a very little extend been used for monitoring and promoting learning progress. We test if application usage generated data from a reading app holds potential for measuring reading ability, reading speed progress and for pointing out features in a school setting that promotes learning. We analyze new data from three different sources: (1) Usage generated data from a widely used reading app, (2) Data from a national reading ability test, and (3) Register data on student background and family characteristics. First, we find that reading app generated data to some degree tells the same story about reading ability as does the formal national reading ability test. Second, we find that the reading app data has the potential to monitor reading speed progress. Finally, we tested several models including machine learning models. Two of these were able to identify variables associated with reading speed progress with some degree of success and to point at certain conditions that promotes reading speed progress. We discuss the results and avenues for further research are presented.
AB - The use of various learning apps in school settings is growing and thus producing an increasing amount of usage generated data. However, this usage generated data has only to a very little extend been used for monitoring and promoting learning progress. We test if application usage generated data from a reading app holds potential for measuring reading ability, reading speed progress and for pointing out features in a school setting that promotes learning. We analyze new data from three different sources: (1) Usage generated data from a widely used reading app, (2) Data from a national reading ability test, and (3) Register data on student background and family characteristics. First, we find that reading app generated data to some degree tells the same story about reading ability as does the formal national reading ability test. Second, we find that the reading app data has the potential to monitor reading speed progress. Finally, we tested several models including machine learning models. Two of these were able to identify variables associated with reading speed progress with some degree of success and to point at certain conditions that promotes reading speed progress. We discuss the results and avenues for further research are presented.
KW - Administrative data
KW - Application generated data
KW - Learning analytics
KW - Machine learning
KW - Reading speed
KW - Reading speed progress
KW - Reading test
UR - http://www.scopus.com/inward/record.url?scp=85112805564&partnerID=8YFLogxK
U2 - 10.1140/epjds/s13688-021-00300-y
DO - 10.1140/epjds/s13688-021-00300-y
M3 - Journal article
C2 - 34426779
AN - SCOPUS:85112805564
VL - 10
JO - EPJ Data Science
JF - EPJ Data Science
SN - 2193-1127
IS - 1
M1 - 44
ER -
ID: 278043158