Ankora, Carlos

2 Publications in AERD

Author Publications

Examining students' academic motivation for studying programming languages

By Ankora, Carlos

Background: The degree to which Computer Science (CS) and Information Communication Technology (ICT) students are motivated to learn greatly impacts their study habits, academic achievement in school and ultimately their job prospects. In recent times, skills in programming languages have become vital in searching for employment. Objective: This study sought to examine the academic motivation of students to study programming languages and their pursuit of job prospects in the software development field. Methods: The study adopted the Academic Motivation Scale (AMS) to measure students' motivation. Data was collected from 244 CS and ICT students at different levels of study at Ho Technical University (HTU) who responded to an online questionnaire. Exploratory factor analysis (EFA), reliability analysis, independent samples t-test and confirmatory factor analysis (CFA) were conducted using IBM statistical package for social sciences (SPSS) and IBM analysis of moment structure (AMOS), respectively. Results and Conclusions: The results showed no significant difference in students' motivation to study programming languages in the university between those who had prior programming languages and those who had none. Also, the results showed no significant difference in the motivation of students to study programming languages between those with and without working experience. The results showed a significant difference between those who want to and do not want to pursue a career in software development and their study motivation of programming languages. Takeaway: Course instructors should emphasize connections between course goals and future employment opportunities. © 2023 John Wiley & Sons Ltd.

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Predicting students’ continuance use of learning management system at a technical university using machine learning algorithms

By Kuadey, Noble Arden

Purpose: This study aims to investigate factors that could predict the continued usage of e-learning systems, such as the learning management systems (LMS) at a Technical University in Ghana using machine learning algorithms. Design/methodology/approach: The proposed model for this study adopted a unified theory of acceptance and use of technology as a base model and incorporated the following constructs: availability of resources (AR), computer self-efficacy (CSE), perceived enjoyment (PE) and continuance intention to use (CIU). The study used an online questionnaire to collect data from 280 students of a Technical University in Ghana. The partial least square-structural equation model (PLS-SEM) method was used to determine the measurement model’s reliability and validity. Machine learning algorithms were used to determine the relationships among the constructs in the proposed research model. Findings: The findings from the study confirmed that AR, CSE, PE, performance expectancy, effort expectancy and social influence predicted students’ continuance intention to use the LMS. In addition, CIU and facilitating conditions predicted the continuance use of the LMS. Originality/value: The use of machine learning algorithms in e-learning systems literature has been rarely used. Thus, this study contributes to the literature on the continuance use of e-learning systems using machine learning algorithms. Furthermore, this study contributes to the literature on the continuance use of e-learning systems in developing countries, especially in a Ghanaian higher education context. © 2022, Emerald Publishing Limited.

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