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Abstract

The web-based learning environment provides access to education for those who are unable to be physically present in a classroom. In situations where comprehensive learner analysis is cost prohibitive, fiscally prudent guidelines for learner analysis that include learner interest and the cultural attribute of language may be feasible alternatives to omitting learner analysis altogether as an online instructional design consideration. Community colleges routinely collect student data during the college admission process, such as the COMPASS reading score, which may be useful in predicting student success in web-based courses.

Therefore, learner characteristics such as the COMPASS reading score, learner interest in course topic, and interest in web-based learning were examined to determine their utility as predictors of achievement in an online introductory health care course. Learner interests were measured using the Course Interest Scale and Web Interest Scale developed in 2008 by Nummenmaa and Nummenmaa. Simple and multiple regression analyses were utilized to determine potential associations. The results demonstrated that the COMPASS reading score positively predicted achievement and was statistically significant, F(1, 17) = 8.05, p = .011 when considered solely, when combined with course interest, F(2, 16) = 4.42, p = .030, and when combined with web interest, F(2, 16) = 3.79, p = .045. These findings indicated that the COMPASS reading score and other data routinely collected on community college students may be useful as predictors of success in online courses and may be effective for guiding student learning format design selections. Using familiar measures such as the COMPASS test score to predict achievement in web-based courses may promote learning outcomes, course completion rates, and graduation rates in community colleges.

Publication Date

11-14-2014

Disciplines

Instructional Media Design | Online and Distance Education

Learner Interest, Reading Comprehension and Achievement in Web-Based Learning

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