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Researchers have long theorized that characteristics of education systems impact both perceived and experienced corruption in public schools. However, due to insufficient cross-national survey data with measures on corruption in education and unassembled yet publicly available institutional data, there are few empirical tests of this theory. This article provides the rare direct test of the relationship between corruption in European public schools and three education system factors: government expenditure on education, education staff compensation, and teacher workload (pupil–teacher ratio). With a newly constructed harmonized data set for European countries, and controlling for national economic factors and individual characteristics, results of multilevel analyses suggest partial support for the theory that specific institutional characteristics of education systems impact public school corruption. The theorized institutional factors have different effects that depend on whether we examine bribe-giving experience or corruption perception. Results show that bribe-giving experience in public schools of Europe is weakly yet significantly related to education staff compensation. For corruption perception, low levels of government expenditure on education and a lopsided pupil–teacher ratio (too few teachers per student) increase the probability that people view corruption as prevalent.
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Toward an Automatic Speech Classifier for the Teacher
Our system classifies audio from microphones worn by the teacher in order to determine (1) whether the teacher is addressing the whole class or talking to individuals or groups of students. In the latter case, it determines (2) whether the teacher is giving formative feedback, giving corrective feedback, chatting socially, or addressing administrative or workflow concerns. This paper reports the initial accuracy of this system against human coding of middle school math classroom behavior. We also compared audio collected through professional hardware versus more accessible alternatives.
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- Award ID(s):
- 1840051
- PAR ID:
- 10185621
- Date Published:
- Journal Name:
- Artificial Intelligence in Education, AIED 2020
- Page Range / eLocation ID:
- 279-284
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
