Review of CSEDM Data and Introduction of Two Public CS1 Keystroke Datasets

Autor/inn/en	Edwards, John; Hart, Kaden; Shrestha, Raj
Titel	Review of CSEDM Data and Introduction of Two Public CS1 Keystroke Datasets
Quelle	In: Journal of Educational Data Mining, 15 (2023) 1, S.1-31 (31 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Data Analysis; Computer Science Education; Learning Analytics; Research Methodology; Keyboarding (Data Entry); Barriers; Data Collection; Metadata; Computer Software; Grades (Scholastic); Programming; Undergraduate Students; Majors (Students); College Entrance Examinations; High School Students; Teaching Methods; Student Behavior; Outcomes of Education; Intelligent Tutoring Systems; Physical Characteristics; Computer Security; Plagiarism; Identification; Assignments; State Universities; Utah; ACT Assessment + Suchen Sie Ihr Suchwort? Auswertung; Computer science lessons; Informatikunterricht; Research method; Forschungsmethode; Texterfassung; Data capture; Datensammlung; Metadaten; Notenspiegel; Programmierung; Aufnahmeprüfung; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Teaching method; Lehrmethode; Unterrichtsmethode; Student behaviour; Schülerverhalten; Lernleistung; Schulerfolg; Intelligentes Tutorsystem; Körperliche Erscheinung; Computervirus; Computersicherheit; Plagiat; Identifikation; Identifizierung; Assignment; Auftrag; Zuweisung; Staatliche Universität; Assessment; Eignungsprüfung; Eignungstest; Hochschulzulassung
Abstract	Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as submission, compilation, edit, and keystroke events, with keystroke-level logs being the most fine-grained of commonly used dataset types. However, the lack of open datasets, especially at the keystroke level, is notable. There are several reasons for this failing, with the most prominent being the challenges of deidentification that are peculiar to keystroke log data. In this paper, we present the public release of two fully deidentified keystroke datasets that are the first of their kind in terms of both event and metadata richness. We describe our collection technique and properties of the data along with deidentification techniques that, while not fully relieving researchers of significant effort, at least reduce and streamline manual work in hopes that researchers will release similar datasets in the future. (As Provided).
Anmerkungen	International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01