Recommender System for Learning SQL Using Hints

Autor/inn/en	Lavbic, Dejan; Matek, Tadej; Zrnec, Aljaž
Titel	Recommender System for Learning SQL Using Hints
Quelle	In: Interactive Learning Environments, 25 (2017) 8, S.1048-1064 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lavbic, Dejan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2016.1244084
Schlagwörter	Evaluation Methods; Information Systems; Intelligent Tutoring Systems; Computer Science Education; Programming; Programming Languages; Computer Software; Problem Solving; Prior Learning; College Students; Foreign Countries; Statistical Analysis; Markov Processes; Slovenia + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Computer science lessons; Informatikunterricht; Programmierung; Problemlösen; Vorkenntnisse; Collegestudent; Ausland; Statistische Analyse; Markowscher Prozess; Slowenien
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01