The purpose of this study was to develop an effective instrument to measure student readiness in online learning with reliable predictors of online learning success factors such as learning outcomes and learner satisfaction. The validity and reliability of the Student Online Learning Readiness (SOLR) instrument were tested using exploratory factor analysis (EFA) and reliability analysis. Twenty items from three competencies–social competencies, communication competencies, and technical competencies–were designated for the initial instrument based on the Student Online Learning Readiness (SOLR) Model as a new conceptual model. An exploratory factor analysis (EFA) revealed that four factor-structures of the instrument of student readiness in online learning explained 66.69% of the variance in the pattern of relationships among the items. All four factors had high reliabilities (all at or above Cronbach’s α > .823). Twenty items remained in the final questionnaire after deleting two items which cross-loaded on multiple factors (social competencies with classmates: five items; social competencies with instructor: five items; communication competencies: four items; and technical competencies: six items). The four-factor structure of the Student Online Learning Readiness (SOLR) instrument has been confirmed through this study. Educators can use the Student Online Learning Readiness (SOLR) instrument in order to gain a better understanding of the level of freshmen college students’ online learning readiness by measuring their social, communication, and technical competencies. In addition, this study examined two factors of social integration in Tinto’s Student Integration Model (SIM) and has introduced the Student Online Learning Readiness (SOLR) conceptual model with the purpose of extending Tinto’s social integration model to online learning environments.

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This study examines the construct validity of the Student Online Learning Readiness (SOLR) instrument. The SOLR instrument consists of 20 items to evaluate social competencies, communication competencies, and technical competencies in online learning. A large Midwestern university was selected to test the construct validity of the SOLR instrument. A total of 347 undergraduate students participated in this study. Confirmatory factor modeling approach was used to assess the construct validity of the SOLR instrument for this study. As a result of Confirmatory Factor Analysis (CFA), the hypothesized model of 20-item structure of the SOLR instrument was verified as a good fit for the data (χ2 (164, N=347)=1959.94, p<.001, IFI=.81, CFI=.81, GFI=.55, RMSEA=.016).

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The aim of this study is to suggest more meaningful components for learning analytics in order to help learners improving their learning achievement continuously through an educational technology approach. Multiple linear regression analysis is conducted to determine which factors influence student's academic achievement. 84 undergraduate students in a women's university in South Korea participated in this study. The sixpredictor model was able to account for 33.5% of the variance in final grade, F (6, 77) = 6.457, p < .001, R 2 = .335. Total studying time in LMS, interaction with peers, regularity of learning interval in LMS, and number of downloads were determined to be significant factors for students' academic achievement in online learning environment. These four controllable variables not only predict learning outcomes significantly but also can be changed if learners put more effort to improve their academic performance. The results provide a rationale for the treatment for student time management effort.