Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recommender system for learning SQL using hints   Today's software industry requires individuals who are proficient in as many
programming languages as possible. Structured query language (SQL), as an
adopted standard, is no exception, as it is the most widely used query language
to retrieve and manipulate data. However, the process of learning SQL turns out
to be challenging. The need for a computer-aided solution to help users learn
SQL and improve their proficiency is vital. In this study, we present a new
approach to help users conceptualize basic building blocks of the language
faster and more efficiently. The adaptive design of the proposed approach aids
users in learning SQL by supporting their own path to the solution and
employing successful previous attempts, while not enforcing the ideal solution
provided by the instructor. Furthermore, we perform an empirical evaluation
with 93 participants and demonstrate that the employment of hints is
successful, being especially beneficial for users with lower prior knowledge.

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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin