6533b7d9fe1ef96bd126cae3

RESEARCH PRODUCT

Forecast of Study Success in the STEM Disciplines Based Solely on Academic Records

Stefan KramerLukas Pensel

subject

Relational database020204 information systems0202 electrical engineering electronic engineering information engineeringMathematics education020201 artificial intelligence & image processing02 engineering and technologySocioeconomic statusEducational data mining

description

We present an approach to the forecast of the study success in selected STEM disciplines (computer science, mathematics, physics, and meteorology), solely based on the academic record of a student so far, without access to demographic or socioeconomic data. The purpose of the analysis is to improve student counseling, which may be essential for finishing a study program in one of the above mentioned fields. Technically, we show the successful use of propositionalization on relational data from educational data mining, based on standard aggregates and basic LSTM-trained aggregates.

yearjournalcountryeditionlanguage
2020-01-01
https://doi.org/10.1007/978-3-030-43823-4_51