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Paassen, Benjamin; McBroom, Jessica; Jeffries, Bryn; Koprinska, Irena; Yacef, Kalina – Journal of Educational Data Mining, 2021
Educational data mining involves the application of data mining techniques to student activity. However, in the context of computer programming, many data mining techniques can not be applied because they require vector-shaped input, whereas computer programs have the form of syntax trees. In this paper, we present ast2vec, a neural network that…
Descriptors: Data Analysis, Programming Languages, Networks, Novices
Berens, Johannes; Schneider, Kerstin; Gortz, Simon; Oster, Simon; Burghoff, Julian – Journal of Educational Data Mining, 2019
To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and private university to predict student dropout as a basis for a targeted…
Descriptors: Risk Management, At Risk Students, Dropout Prevention, College Students
Pelaez, Kevin – Journal of Educational Data Mining, 2019
Higher education institutions often examine performance discrepancies of specific subgroups, such as students from underrepresented minority and first-generation backgrounds. An increase in educational technology and computational power has promoted research interest in using data mining tools to help identify groups of students who are…
Descriptors: At Risk Students, College Students, Identification, Multivariate Analysis