What makes learners a good fit for hybrid learning? Learning competences as predictors of experience and satisfaction in hybrid learning space

Autor: Hsu-Chen Cheng, Zhimin Pan, Hong-Zheng Sun-Lin, Tzu-Han Lin, Mengyuan Li, Jun Xiao
Rok vydání: 2020
Předmět:
Zdroj: British Journal of Educational Technology. 51:1203-1219
ISSN: 1467-8535
0007-1013
Popis: Compared with fully face-to-face or online learning environments, implementation of hybrid learning spaces is costly given the spaces making all learning options available for learners. Therefore, decisions on investments in hybrid learning are critical for institutions. Satisfaction and experience of learners is one of the important indicators for assessing the cost-effectiveness of learning space implementation; thus, predictions of learners' satisfaction and experience can inform institutions' decision making on learning space investments. Moreover, learning competences are found correlated with learners' satisfaction and experience in general and e-learning settings. Therefore, the present study aimed at exploring predictive learning competences for hybrid learners' experience and satisfaction. A hybrid learning space was built upon a proposed model at Shanghai Open University. 211 students? learning competences and their satisfaction and experience in the hybrid learning space were examined. The results showed that except cognitive engagement competence, most predictive competences were not significantly associated with hybrid learners' satisfaction and experience. The findings indicated that since hybrid learning keeps all options available, to experience satisfying learning, students need not have certain competences but cognitive engagement competence, which is correlated with learners' cognitive ability to figure out the right mix of learning options.
Databáze: OpenAIRE