Predicting achievement in distance language learning: a structural equation model

ABSTRACT

Getting increasingly common in many settings in higher education, distance language courses have become a vigorous area of research which requires sustained focus. Although the relevant literature has documented well various aspects of distance language learning, the factors affecting academic achievement have remained under-researched. This study investigates the predictive power of the sense of community, perceived learning and learner satisfaction as the possible factors having an influence on academic achievement. Data for this study come from a series of surveys administered to a total of 156 EFL learners and participants’ final grades. Structural Equation Model (SEM) was used to analyse the data. Results indicated acceptable values of the goodness of fit indices for structural model (TLI =.96; CFI =.98; NFI =.95; RMSEA =.06 and SRMR =.04). Analysis of path coefficients revealed that two components of sense of community (i.e. teaching presence and cognitive presence), perceived learning and satisfaction are the significant predictors of academic achievement. The research findings bear implications for the design of distance language courses.

KEYWORDS:

• Academic achievement
• distance language learning
• EFL learners
• structural equation model

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Notes on contributors

Aysel Şahin Kızıl

Dr. Aysel Şahin Kızıl works as an Associate Professor at İzmir Bakırçay University. She holds an M.A degree in Applied Linguistics and a PhD in English Language Teaching. She has taught ICT in language education, linguistics and applied linguistics at the undergraduate level, and theories of second language acquisition and applied linguistics at the graduate level. Her current research interests include the use of ICT tools in language education and corpus linguistics.