Students’ Perspectives on Satisfaction with Distance Education in Ghana: A Cluster Analysis

Agyapong, Kingsley

doi:10.21083/ajote.v10i1.6287

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…In their study investigating the social dimensions of distance education in high schools, Lidegran et al (2021) identified three clusters: urban upper-middle-class, immigrant working-class, and rural working-class. Agyapong (2021) divided students into three clusters, which were highly, moderately, and least satisfied based on preferences (price, quality, packaging, and social bonding) that influenced their distance education program choices. Students in the highly satisfied cluster were driven by the education program's service quality and competitive fee structure.…”

Section: Innoeduca International Journal Of Technology and Educationa...mentioning

confidence: 99%

Examination of student satisfaction with e-courses by clustering analysis

Deveci Topal,

Kolburan Geçer

2023

IJTEI

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Student satisfaction is one of the foremost factors for e-courses to continue efficiently and achieve their goals. The present research aims to analyze the clustering tendencies of e-course students’ satisfaction with e-courses according to the variables of “gender, class, computer ownership, computer and internet literacy, duration of internet use, the connection type to the internet, and the tools utilized by the students in the learning management system” through clustering algorithms, one of the data mining techniques. The survey model was employed. A 35-item satisfaction scale devised for students taking e-courses was used for data collection. A total of 522 students taking online courses at a university participated in the study. Descriptive statistics and cluster analysis were employed to analyze and interpret the data. As a result of the analyses through K-Means cluster analysis, four different clusters were obtained. Those clusters were defined in accordance with the variables they included as follows: Cluster 1 (the group with the highest rate of computer ownership and the highest computer literacy); Cluster 2 (the group with the lowest rate of computer ownership and the lowest computer literacy); Cluster 3 (students have low computer literacy and low duration of Internet use), and Cluster 4 (the group with the highest rate of females, first graders, and those who use the Internet at “good” and “very good” levels) . According to the research results, the mean student satisfaction levels regarding e-courses were determined to be low in general. As per the clustering analysis, it was observed that the students in Cluster 1 and Cluster 4 had the highest mean values of satisfaction with e-courses and a “good” level of satisfaction compared to the other groups, while Cluster 2 had the lowest mean value. Cluster 3, on the other hand, was the one with a “medium” level of satisfaction.

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Section: Innoeduca International Journal Of Technology and Educationa...mentioning

confidence: 99%

Examination of student satisfaction with e-courses by clustering analysis

Deveci Topal,

Kolburan Geçer

2023

IJTEI

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“…Their possessing different characteristics necessitates the need for segmenting them into groups so that while evaluating their perception regarding a certain issue, their needs can be understood and proper corrective action can be taken. Thus, it has been used by different scholars in distance education to analyse distance learners (Saxena et al, 2004), to explore the factors of their satisfaction with a distance education course (Agyapong, 2021;Ozyurt, 2014) to determine their dropout rate etc. (Desjardins et al, 2021).…”

Section: Cluster Analysismentioning

confidence: 99%

Segmenting Distance Learners on the Basis of their Career Awareness and Perceived Employability: A Study of PG Economics Students of University of Jammu

Choudhary

2022

SER

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This research explored the factors affecting awareness regarding graduate career prospects for distance learners of Economics, studying at the Directorate of Distance Education, University of Jammu, India. Distance learners completed a questionnaire about students' career intentions and motivation for undertaking the subject. Exploratory Factor Analysis (EFA) and Cluster Analysis were used to cluster these students into groups "based upon their perceptions and awareness" of graduate careers. The distance learners were aware of the career opportunities available to MA Economics graduates. Their awareness emerged as the most significant factor. However, they also show 'Perceived Poor job prospects'. Two clusters emerged: 'highly motivated learners' and 'moderately motivated learners'. The item 'Those studying for academics and competitive exams simultaneously don't achieve anything in life' emerged as the most influential in forming the clusters. The findings highlight an urgent need for early counselling for these students. Their competitive inclination can be seen from their response to the question 'Why distance mode was chosen', as 47% responded that it was due to their preparing for the competitive exams simultaneously. Their job preferences for the banking sector (26.3%), education sector (22.5%) or acceptance of any job (16.5%) show that their pursuit of a Master's degree in Economics was not an influencer in their career orientation. The learners have not realized their potential yet, due to which they are willing to take up any job, thus underutilizing their special skills learned after completing the Master's degree in Economics.

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Students’ Perspectives on Satisfaction with Distance Education in Ghana: A Cluster Analysis

Cited by 2 publications

References 42 publications

Examination of student satisfaction with e-courses by clustering analysis

Examination of student satisfaction with e-courses by clustering analysis

Segmenting Distance Learners on the Basis of their Career Awareness and Perceived Employability: A Study of PG Economics Students of University of Jammu

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