Predicting Student Performance in a Collaborative Learning Environment [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol
Institution	International Educational Data Mining Society
Titel	Predicting Student Performance in a Collaborative Learning Environment [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (7 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Educational Environment; Predictive Measurement; Predictor Variables; Cooperative Learning; Regression (Statistics); Models; Problem Solving; Data Collection; Academic Records; Data Processing; Progress Monitoring; Goodness of Fit; Fractions; Grade 4; Grade 5; Prediction; Predictive Validity; Achievement Gains; Learning Strategies; Learning Processes + Suchen Sie Ihr Suchwort? Lernumgebung; Pädagogische Umwelt; Schulumwelt; Prädiktor; Kooperatives Lernen; Regression; Regressionsanalyse; Analogiemodell; Problemlösen; Data capture; Datensammlung; College; Colleges; University; Universities; Publication; Hochschule; Fachhochschule; Universität; Akademieschrift; Publikation; Datenverarbeitung; Bruchrechnung; School year 04; 4. Schuljahr; Schuljahr 04; School year 05; 5. Schuljahr; Schuljahr 05; Vorhersage; Achievement gain; Leistungssteigerung; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Learning process; Lernprozess
Abstract	Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression model for modeling individual learning, often used in conjunction with knowledge component models and tutor log data. The extended model predicts performance of students solving problems collaboratively with an ITS. Specifically, we address the open questions: Does adding collaborative features to a standard AFM provide a better fit than the standard AFM? Also, does the impact of these features change based on the nature of the knowledge (conceptual v. procedural) that is being acquired? In our extended AFM models, we include a variable indicating if students are working individually or in pairs. Also, for students working collaboratively, we model both the influence on learning of being helped by a partner and helping a partner. For each model, we analyzed conceptual and procedural datasets separately. We found that both collaborative features (being helped and helping) improve the model fit. In addition, the impact of these features differs between the collaborative and procedural datasets, suggesting collaboration may affect procedural and collaborative learning differently. By adding collaborative learning features into an existing regression model for individual learning over a series of skill opportunities, we gain a better understanding of the impact that working with a partner has on student learning, when working with a step-based collaborative ITS. This work also provides an improved model to better predict when students have reached mastery while collaborating. [For complete proceedings, see ED560503.] (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