Exploring the effects of personality type on perceived satisfaction with web-based learning in continuing professional development

Kanuka, Heather; Nocente, Norma

doi:10.1080/0158791032000127491

Cited by 32 publications

(19 citation statements)

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“…Many educational psychologists have claimed that personality influences the way learners actively involution in their learning processes as well as whether they take responsibility for self-direction and discipline (Kim, Lee, & Ryu, 2013). The relationship between personality and academic performance has been examined in the context of distance and online education (Butler & Pinto-Zipp, 2006;Irani, Telg, Scherler, & Harrington, 2003;Kanuka & Nocente, 2003;Kim & Schniederjans, 2004;Lee & Lee, 2006). Although results have been mixed, but generally support a significant relationship between personality and performance.…”

Section: Introductionmentioning

confidence: 97%

E-learners’ personality identifying using their network behaviors

Ghorbani

Montazer

2015

Computers in Human Behavior

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Section: Introductionmentioning

confidence: 97%

E-learners’ personality identifying using their network behaviors

Ghorbani

Montazer

2015

Computers in Human Behavior

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“…Motivation has been considered a crucial learner characteristic for success in the online learning environment (Kanuka & Nocente, 2003;Lim & Kim, 2002;Piccoli, Ahmad, & Ives, 2001), as it has positive relations with learner satisfaction (Kanuka & Nocente, 2003;Piccoli, Ahmad, & Ives, 2001) and academic achievement (Lim & Kim, 2002). In addition, several existing student readiness instruments in online learning have contained items related to communication competencies (Dray & Miszkiewicz, 2007;McVay, 2001) and distance learners' interactions with their instructors and classmates (Bernard, Brauer, Abrami, & Surkes, 2004).…”

Section: Review Of Existing Student Readiness Instrumentsmentioning

confidence: 99%

An Exploratory Factor Analysis and Reliability Analysis of the Student Online Learning Readiness (SOLR) Instrument

Richardson

2015

OLJ

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The purpose of this study was to develop an effective instrument to measure student readiness in online learning with reliable predictors of online learning success factors such as learning outcomes and learner satisfaction. The validity and reliability of the Student Online Learning Readiness (SOLR) instrument were tested using exploratory factor analysis (EFA) and reliability analysis. Twenty items from three competencies–social competencies, communication competencies, and technical competencies–were designated for the initial instrument based on the Student Online Learning Readiness (SOLR) Model as a new conceptual model. An exploratory factor analysis (EFA) revealed that four factor-structures of the instrument of student readiness in online learning explained 66.69% of the variance in the pattern of relationships among the items. All four factors had high reliabilities (all at or above Cronbach’s Î± > .823). Twenty items remained in the final questionnaire after deleting two items which cross-loaded on multiple factors (social competencies with classmates: five items; social competencies with instructor: five items; communication competencies: four items; and technical competencies: six items). The four-factor structure of the Student Online Learning Readiness (SOLR) instrument has been confirmed through this study. Educators can use the Student Online Learning Readiness (SOLR) instrument in order to gain a better understanding of the level of freshmen college students’ online learning readiness by measuring their social, communication, and technical competencies. In addition, this study examined two factors of social integration in Tinto’s Student Integration Model (SIM) and has introduced the Student Online Learning Readiness (SOLR) conceptual model with the purpose of extending Tinto’s social integration model to online learning environments.

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“…Moore and Kearsley (2005) opined that students in online courses begin discourse with other learners on an individual or group basis and discourse in online learning environments help students to develop skills in knowledge acquisition, which leads to academic success. Arbaugh (2002) Perceived usefulness and perceived ease of use, flexibility of e-Learning, interaction with class participants, student usage, and gender Arbaugh & Duray (2002) Perceived usefulness and perceived ease of use, perceived flexibility Piccoli, Ahmad, & Ives (2001) Maturity, motivation, technology comfort, technology attitudes, computer anxiety, and epistemic beliefs, technology control, technology attitudes, teaching styles, self-efficacy, availability, objectivist and constructivist, quality, reliability, and availability, pace, sequence, control, factual knowledge, procedural knowledge, conceptual knowledge, timing, frequency, and quality Hong (2002) Gender, age, scholastic aptitude, learning style, and initial computer skills, interaction with instructor, interaction with fellow students, course activities, discussion sessions, and time spent on the course Kanuka & Nocente (2003) Motivating aims, cognitive modes, and interpersonal behaviors Stokes (2001) Students' temperaments (guardian, idealist, artisan, and rational) Thurmond, Wambach, Connors, & Frey (2002) Computer skills, courses taken, initial knowledge about eLearning technology, live from the main campus of the institution, age, receive comments promptly on time, offer various assessment methods, time to spend, scheduled discussions, team work, acquaintance with the instructors…”

Section: Online Learning Interaction and Satisfactionmentioning

confidence: 99%

Relationships Between Minority Students Online Learning Experiences and Academic Performance

Kumi–Yeboah

Smith

2016

OLJ

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The study investigated the relationship between minority students' use of technology, social media, the number of online courses, program of study, satisfaction, and academic performance. Participants in the study were a diverse student body regarding age, gender, and educational level, and functioned at both undergraduate and graduate levels. Analysis of variance (ANOVA) and Chi-square tests were used to find the relationship between participants' online learning experiences and academic performance. Results showed that satisfaction and use of social media had no relationship with the academic performance of participants. However, a relationship existed between the use of technology, the number of courses in online, program of study, and academic performance. Categories that emerged from the open-ended questions were flexibility and time convenience, self-confidence, lack of support, self-regulated learning skills, and language and linguistic differences. The authors concluded that varying factors such as cultural, language, personal, and efficacy skills facilitated the academic performance of minority students in an online learning environment. This study reiterates the importance of establishing multicultural presence in an online course and suggests best pedagogical methods for teaching minority students in an online course.

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Exploring the effects of personality type on perceived satisfaction with web-based learning in continuing professional development

Cited by 32 publications

References 0 publications

E-learners’ personality identifying using their network behaviors

E-learners’ personality identifying using their network behaviors

An Exploratory Factor Analysis and Reliability Analysis of the Student Online Learning Readiness (SOLR) Instrument

Relationships Between Minority Students Online Learning Experiences and Academic Performance

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