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Regression analyses identified learner attributes and instructional variables predictive of student satisfaction.
A correlational research design examined the relationships among 5 learner attributes and 3 instructional variables and student satisfaction.
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Learner attribute predictors included: (1) previous technology courses, (2) technology competence, (3) between-class technology usage, (4) age, and (5) remote-site group size.
The analyses explore (1) potential differences of students' perceptions between Internet-based and face-to-face learning environments and (2) potential differences in the three aspects in relation to learners' attributes and the use of the Internet and enrollment in online courses.
Further, the questionnaire administered in class #3 had inquiries concerning mainly the learner's attributes; hence, our analysis based on the answers could not reveal factors that have (or do not have) a learning effect.
To consider learning material attributes in learner profiles, learners' ratings were used.
In comparison, for example, teachers may show two different chemical reactions of combustion on the screen together and have learners compare their attributes.
Instrumental conflict is referred to when the learner does not attribute an educational function to a digital learning object [ 34].
Learners must determine these attributes independently.
Learners must determine these attributes independently .
Other research studies used iSAT visualization to trace transition patterns across multiple attributes of learners.
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CEO of Professional Science Editing for Scientists @ prosciediting.com