@book{
 title = {Student performance prediction and optimal course selection: An MDP approach},
 type = {book},
 year = {2018},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 volume = {10729 LNCS},
 id = {c8087882-ff0e-353f-b217-8e1fa594139f},
 created = {2018-02-27T10:31:01.088Z},
 file_attached = {false},
 profile_id = {bbb99b2d-2278-3254-820f-2de6d915ce63},
 last_modified = {2018-02-27T10:31:01.088Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© Springer International Publishing AG 2018. Improving the performance of students is an important challenge for higher education institutions. At most European universities, duration and completion rate of degrees are highly varying and consulting services are offered to increase student achievement. Here, we propose a data analytics approach to determine optimal choices for the courses of the next term. We use machine learning techniques to predict the performance of a student in upcoming courses. These prediction form the transition probabilities of a Markov decision process (MDP) that describes the course of studies of a student. Using this model we plan to explore the effect of different strategies on student performance.},
 bibtype = {book},
 author = {Backenköhler, M. and Wolf, V.},
 doi = {10.1007/978-3-319-74781-1_3}
}

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