Beyond Log Files: Using Multi-Modal Data Streams towards Data-Driven KC Model Improvement [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016).

Autor/inn/en	Liu, Ran; Davenport, Jodi; Stamper, John
Titel	Beyond Log Files: Using Multi-Modal Data Streams towards Data-Driven KC Model Improvement [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016).
Quelle	(2016), (6 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Educational Technology; Data Analysis; Automation; Models; Improvement; Knowledge Representation; High School Students; Chemistry; Intelligent Tutoring Systems; Middle School Students; Fractions; Grade 5; Pennsylvania (Pittsburgh) + Suchen Sie Ihr Suchwort? Unterrichtsmedien; Auswertung; Analogiemodell; Qualitätssteigerung; Wissensrepräsentation; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Chemie; Intelligentes Tutorsystem; Middle school; Middle schools; Mittelschule; Mittelstufenschule; Bruchrechnung; School year 05; 5. Schuljahr; Schuljahr 05
Abstract	The increasing use of educational technologies in classrooms is producing vast amounts of process data that capture rich information about learning as it unfolds. The field of educational data mining has made great progress in using log data to build models that improve instruction and advance the science of learning. Thus far, however, the predictive and explanatory power of such models has often been limited to the actions that educational technologies can log. A major challenge in incorporating more contextually rich data streams into models of learning is collecting and integrating data from different sources and at different grain sizes. We present our methodological advances in automating the integration of log data with additional multi-modal (e.g., audio, screen video, webcam video) data streams. We also demonstrate several examples of how integrating multiple streams of data into the knowledge component (KC) model refinement process improves the predictive fit of student models and yields important pedagogical implications. This work represents an important advancement in facilitating the integration of rich qualitative details of students' learning contexts into the quantitative approaches characteristic of EDM research. [For the full proceedings, see ED592609.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01