Recommender system for learning SQL using hints

TL;DR

This paper introduces an adaptive hint-based recommender system to facilitate SQL learning, supporting personalized learning paths and demonstrating effectiveness especially for users with less prior knowledge.

Contribution

It presents a novel adaptive approach that supports personalized SQL learning by providing hints without enforcing a fixed solution, validated through empirical evaluation.

Findings

Hints significantly aid users with low prior knowledge.

The system improves learning efficiency and understanding.

Empirical results show positive user outcomes.

Abstract

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.