Using HeuristicsMiner to Analyze Problem-Solving Processes: Exemplary Use Case of a Productive-Failure Study

Autor/inn/en	Hartmann, Christian; Rummel, Nikol; Bannert, Maria
Titel	Using HeuristicsMiner to Analyze Problem-Solving Processes: Exemplary Use Case of a Productive-Failure Study
Quelle	In: Journal of Learning Analytics, 9 (2022) 2, S.66-86 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Hartmann, Christian) ORCID (Rummel, Nikol) ORCID (Bannert, Maria)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Heuristics; Problem Solving; Computer Software; Comparative Analysis; Sample Size; Classroom Environment; Failure; Teaching Methods; Learning Processes; Secondary School Students; Foreign Countries; Mathematics Skills; Prior Learning; Mathematics Instruction; Prerequisites; Germany + Suchen Sie Ihr Suchwort? Heuristik; Problemlösen; Klassenklima; Unterrichtsklima; Teaching method; Lehrmethode; Unterrichtsmethode; Learning process; Lernprozess; Sekundarschüler; Ausland; Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Vorkenntnisse; Mathematics lessons; Mathematikunterricht; Voraussetzung; Deutschland
Abstract	This paper presents a fine-grained process analysis of 22 students in a classroom-based learning setting. The students engaged (and failed) in problem-solving attempts prior to instruction (i.e., the Productive-Failure approach). We used the HeuristicsMiner algorithm to analyze the data of a quasi-experimental study. The applied algorithm allowed us to investigate temporally structured think-aloud data, to outline productive and unproductive problem-solving strategies. Our analyses and findings demonstrated that HeuristicsMiner enables researchers to effectively mine problem-solving processes and sequences, even for smaller sample sizes, which cannot be done with traditional coding-and-counting strategies. The limitations of the algorithm, as well as further implications for educational research and practice, are also discussed. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01