Akademik Veri Yönetim Sistemi
Online Teaching and Learning in Higher Education, Pedro Isaias,Demetrios G.,Dirk Ifenthaler, Editör, Springer, London/Berlin , Berlin, ss.117-131, 2020
An important advantage of e-learning environments is the numerical observation of the learning behaviors of learners. The use of e-learning environments by learners creates a learner data (log data). From these learner data, the navigation patterns obtained by using educational data mining have a very important in learning and teaching design. Studies have shown that learners’ learning behaviors in online learning environments may vary according to the characteristics of learners. Studies on the differentiation of the navigation patterns according to the psycho-educational characteristics of the learners provide very strong inputs to the design of the learning environment appropriate to the characteristics of the learners, which is named as adaptive learning environments. According to these inputs, learning environment designs can be developed according to the individual characteristics of the learners. Online learners’ readiness (OLR) for e-learning is an important psycho-educational structure. The aim of this study is to investigate learners’ navigations in the e-learning environment according to the level of readiness for e-learning. Self-directed learning, learner control, motivation sub-dimensions were used in this study as online readiness sub-dimensions. The consecutive analysis was used to reveal the model of human behavior and communication patterns. For this purpose, lag sequential analysis was used when learners’ system interactions were analyzed sequentially. According to the results of the analysis, it has been found that the sequential navigation patterns of the learners differ according to the OLR structure. The findings of this research are expected to provide important information and suggestions to online learning environment designers.