Cluster and Time-Series Analyses of Computer-Assisted Pronunciation Training Users: Looking Beyond Scoring Systems to Measure Learning and Engagement

Guardat en:

Dades bibliogràfiques
Publicat a:	International Journal of Computer - Assisted Language Learning and Teaching vol. 4, no. 1 (2014), p. 1
Autor principal:	John-Michael Nix
Publicat:	IGI Global
Matèries:	Individual differences Learning Pronunciation instruction Clustering Computer assisted language learning Time series English as a second language instruction Training Computer assisted instruction > CAI Aggregate data Teaching Pronunciation Variables Scores Multivariate Analysis Copyrights Independent Study Questionnaires Video Technology Influence of Technology Attrition (Research Studies) Language Teachers Computers Measurement Techniques English (Second Language) Electronic Equipment Periodicals Predictor Variables Time Research Design Computer Assisted Instruction Systems Analysis Program Effectiveness Instructional Effectiveness Second Language Learning English Learners Quasiexperimental Design
Accés en línia:	Citation/Abstract Full Text - PDF
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!

Descripció
Resum:	The present study utilized hierarchical agglomerative cluster (HAC) analysis to categorize users of a popular, web-based computer-assisted pronunciation training (CAPT) program into user types using activity log data. Results indicate an optimal grouping of four types: Reluctant, Point-focused, Optimal, and Engaged. Clustering was determined by aggregate data on seven indicator variables of mixed types (e.g., ratio, continuous, and categorical). It was found that measurements of effort: lines recorded and episodic effort served best to distinguish the user types. Subsequent time-series analysis of cluster members showed that groupings exhibited distinct trends in learning behavior which explain performance outcomes. Four waves of data were collected during one semester of EFL instruction wherein CAPT usage partially fulfilled course requirements. This study follows an exploratory, data-driven approach. In addition to the findings above, suggestions for future research into interactions between individual differences variables and CALL platforms are made.
ISSN:	2155-7098 2155-7101
DOI:	10.4018/ijcallt.2014010101
Font:	Advanced Technologies & Aerospace Database