Using Learning Analytics in the Amazonas: Understanding Students' Behaviour in Introductory Programming

Autor/inn/en	Pereira, Filipe D.; Oliveira, Elaine H. T.; Oliveira, David B. F.; Cristea, Alexandra I.; Carvalho, Leandro S. G.; Fonseca, Samuel C.; Toda, Armando; Isotani, Seiji
Titel	Using Learning Analytics in the Amazonas: Understanding Students' Behaviour in Introductory Programming
Quelle	In: British Journal of Educational Technology, 51 (2020) 4, S.955-972 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.12953
Schlagwörter	Introductory Courses; Programming; Outcomes of Education; Student Behavior; Computer Science Education; Data Analysis; Grading; Computer Software; Assignments; Interaction; Evaluation Methods; Keyboarding (Data Entry); Grades (Scholastic); Concept Formation; Student Improvement; Teaching Methods; Learning Processes; Intervention; Foreign Countries; South America + Suchen Sie Ihr Suchwort? Einführungskurs; Programmierung; Lernleistung; Schulerfolg; Student behaviour; Schülerverhalten; Computer science lessons; Informatikunterricht; Auswertung; Notengebung; Schulnote; Assignment; Auftrag; Zuweisung; Interaktion; Texterfassung; Notenspiegel; Concept learning; Begriffsbildung; Teaching method; Lehrmethode; Unterrichtsmethode; Learning process; Lernprozess; Ausland; Südamerika
Abstract	Tools for automatic grading programming assignments, also known as Online Judges, have been widely used to support computer science (CS) courses. Nevertheless, few studies have used these tools to acquire and analyse interaction data to better understand the students' performance and behaviours, often due to data availability or inadequate granularity. To address this problem, we propose an Online Judge called CodeBench, which allows for fine-grained data collection of student interactions, at the level of, eg, keystrokes, number of submissions, and grades. We deployed CodeBench for 3 years (2016-18) and collected data from 2058 students from 16 introductory computer science (CS1) courses, on which we have carried out fine-grained learning analytics, towards early detection of effective/ineffective behaviours regarding learning CS concepts. Results extract clear behavioural classes of CS1 students, significantly differentiated both semantically and statistically, enabling us to better explain how student behaviours during programming have influenced learning outcomes. Finally, we also identify behaviours that can guide novice students to improve their learning performance, which can be used for interventions. We believe this work is a step forward towards enhancing Online Judges and helping teachers and students improve their CS1 teaching/learning practices. (As Provided).
Anmerkungen	Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01