Analytic Hierarchy Process with Rasch Measurement in the Construction of a Composite Metric of Student Online Learning Readiness Scale

Year: 2022       Vol.: 71       No.: 1      

Authors: Joyce DL. Grajo, James Roldan S. Reyes1, Liza N. Comia, Lara Paul A. Ebal, Jared Jorim O. Mendoza, and Mara Sherlin DP. Talento

Abstract:

This paper developed the Online Learning Readiness Composite Scale (OLRCS), a composite measure of student online learning readiness based on five dimensions, namely (1) computer/internet self-efficacy; (2) self-directed learning; (3) learner control; (4) motivation for learning; and (5) online communication self-efficacy. A single metric of online learning readiness has its advantage over its disaggregated dimensions. For one, it allows a summative description of each student which school administrators can use for an effective student targeting toward flexible learning. Rasch Analysis (RA) was performed to come up with an objective measure for each dimension while Analytic Hierarchy Process (AHP) was applied to aggregate the computed Rasch scores of the five dimensions. Three OLRCS have been constructed using weights generated by (1) teacher participants, (2) student participants, and (3) combined student and teacher participants. Results showed that motivation for learning consistently received the highest weight while online communication self-efficacy and computer/internet selfefficacy got low weights among the three OLRCS. Research findings also showed that student participants gave more importance to learner control than self-directed learning, unlike the teacher participants. The difference in the teacher and student perspectives merits detailed attention to optimize the online learning environment and enable individual support. Nevertheless, using cluster analysis, the distribution of students who are ready, undecided, or not ready for online learning is similar to the three constructed OLRCS.

Keywords: multidimensional latent variable; multi-criteria decision analysis; linear aggregation

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