A novel IoT architecture for measuring and
visualizing influence of common factors on house dust concentration<i/>

Đambić, Goran; Kučak, Danijel; Ražov, Tomislav

doi:10.31785/ijdte.4.1.1

Cited by 3 publications

References 21 publications

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IoT - Driven Allergy Management & Modelling
Limaye,
Joshi,
Srividya
2024
2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)
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Identifying Critical Factors When Predicting Remedial Mathematics Completion Rates
Mgonja
¹
,
Robles
²

2022
Journal of College Student Retention: Research, Theory & Pr
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Completion of remedial mathematics has been identified as one of the keys to college success. However, completion rates in remedial mathematics have been low and are of much debate across America. This study leverages machine learning techniques in trying to predict and understand completion rates in remedial mathematics. The purpose of this study is to build machine learning models that can predict students that are least likely to complete remedial mathematics and identify which factors are most influential when computing those predictions. The study discovers random forest as the highest performing model. Furthermore, the study reveals that the remedial course a student begins with, credit completion rate, math placement score, and high school G.P.A as the most influential predictors of completion rates. The study also offers future research directions, especially in how to improve the performance of the machine learning models.

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Computer or teacher: Who predicts dropout best?
Eegdeman
¹
,
Cornelisz
²
,
Klaveren
³

et al. 2022
Front. Educ.
3
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IntroductionMachine learning algorithms use data to identify at-risk students early on such that dropout can be prevented. Teachers, on the other hand, may have a perspective on a student’s chance, derived from their observations and previous experience. Are such subjective perspectives of teachers indeed predictive for identifying at-risk students, and can these perspectives help increase the prediction performance of machine learning algorithms? This study puts 9 teachers in an upper secondary vocational education program to the test.MethodsFor each of the 95 freshmen students enrolled in the program, these teachers were asked whether a student would drop out by the end of their freshman year. Teachers answered this question at the beginning of the program and again after the first 10 weeks of the program.ResultsTeachers predicted dropout better than the machine learning algorithms at the start of the program, in particular, because they were able to identify students with a very high likelihood of dropout that could not be identified by the algorithms. However, after the first period, even though prediction accuracy increased over time for both algorithms and teachers, algorithms outperformed the teachers. A ranking, combining the teachers composite and the random forest algorithm, had better sensitivity than each separately, though not better precision.

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A novel IoT architecture for measuring and visualizing influence of common factors on house dust concentration

Cited by 3 publications

References 21 publications

IoT - Driven Allergy Management & Modelling

IoT - Driven Allergy Management & Modelling

Identifying Critical Factors When Predicting Remedial Mathematics Completion Rates

Computer or teacher: Who predicts dropout best?

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