000			03041nam a22002777a 4500
003			KOHA
005			20221103124341.0
008			221103d2022 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aD 322 _bA34 2022
100	1		_aAdeshola, Ibrahim
245	1	0	_aCOMPUTER SELF-EFFICACY, ACADEMIC SELF-EFFICACY, STUDENT'S ENGAGEMENT, LEARNER'S PERSISTENCE, AND ACADEMIC BENEFITS / _cIBRAHIM ADESHOLA; SUPERVISOR: ASSOC. PROF. DR. MARY AGOYI
264			_c2022
300			_a116 sheets; _c31 cm. _eIncludes CD
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (PhD) - Cyprus International University. Institute of Graduate Studies and Research Management Information Systems Department
504			_aIncludes bibliography (sheets 83-106)
520			_aABSTRACT The unforeseen spread of COVID-19 prompted institutions to make a hasty transition to e-learning, which paved the way for continuous access to educational opportunities. Therefore, the measurement of student engagement in e-learning is necessary if one is to ensure that students are involved in their studies in the same way that they would be in a traditional classroom setting. There is very little information available regarding the actions that teachers can take to help students interact with the e-learning platform while the COVID-19 outbreak is ongoing. This is because the students have a full control over their participation in the platform. In a similar vein, the existing body of research has indicated that one of the difficulties associated with the implementation of e-learning is the fact that a significant number of students multitask during class hours. As a result, on the basis of an in-depth examination of the relevant literature, a systematic model for evaluating the levels of e-learning engagement, learning persistence, and academic benefits experienced by university students was established. This study used the quantitative method of Partial Least Square-Structural Equation Modelling to validate the model empirically. The data for this study came from 274 students who were enrolled in introduction to computer course using learning management systems. For the purpose of measuring e-learning engagement, a total of nine first-order constructs were applied. They collectively accounted 75 percent of the variance in e-learning engagement, whereas learning persistence and academic benefits were each explained by 42 percent and 66 percent of the variance, respectively. Except the relationship between learning persistence and academic benefits, all of the hypotheses that were examined produced positive results. Keywords: Academic Benefits, Academic Self-Efficacy, Computer Efficacy, E-Learning Engagement, Learning Persistence.
650		0	_aLearning _vDissertations, Academic
650		0	_aSelf-efficacy _vDissertations, Academic
700	1		_aAgoyi, Mary _esupervisor
942			_2ddc _cTS
999			_c288991 _d288991