Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automated Personalized Feedback Improves Learning Gains in an
  Intelligent Tutoring System   We investigate how automated, data-driven, personalized feedback in a
large-scale intelligent tutoring system (ITS) improves student learning
outcomes. We propose a machine learning approach to generate personalized
feedback, which takes individual needs of students into account. We utilize
state-of-the-art machine learning and natural language processing techniques to
provide the students with personalized hints, Wikipedia-based explanations, and
mathematical hints. Our model is used in Korbit, a large-scale dialogue-based
ITS with thousands of students launched in 2019, and we demonstrate that the
personalized feedback leads to considerable improvement in student learning
outcomes and in the subjective evaluation of the feedback.

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The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
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The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics