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

I tiakina i:
I whakaputaina i:International Journal of Computer - Assisted Language Learning and Teaching vol. 4, no. 1 (2014), p. 1
Kaituhi matua: John-Michael Nix
I whakaputaina:
IGI Global
Ngā marau:
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
Urunga tuihono:Citation/Abstract
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Whakarāpopotonga: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
Puna:Advanced Technologies & Aerospace Database