Studies have shown that many Malaysian non-technical students have low motivation in learning ICT course due to a number of reasons, such as a lack of learning practice and effective learning applications. In view of such a problem, the researchers carried out a quasi-experimental study to examine the impact of a novel mobile augmented reality learning application (MARLA) on students' motivation in learning a topic of a university ICT course. The research was based on the pretest-posttest control group design, and the study sample consisted of 120 non-technical undergraduates majoring in social science, with a mean age of 19.5 years. They were divided into an experimental group and a control group. The dependent variable was students' motivation in learning, and the independent variables were learning method and gender. The experimental group used MARLA on their mobile devices to learn one of the topics of the ICT Competency course, namely Computer System; whereas the control group used a similar application on their desktop computers. The Intrinsic Motivation Inventory (IMI) was the research instrument used to measure students' motivation before and after learning sessions, which spanned 6 hours. Utilizing the SPSS (version 21), an analysis of covariance was performed, showing there was a main effect attributed to gender only, with male and female students attaining mean scores of 4.24 and 3.90 respectively for the motivation construct. This finding showed male students were more motivated than their opposite counterparts. In contrast, no such main effect attributed to learning method was observed, as evidenced from the mean scores of 4.08 and 4.07 of the experimental group and control group respectively for the measured construct, suggesting both methods were both equally effective. Additionally, there was an interaction effect between gender and learning method, with male students attaining different levels of motivation based on learning method. Arguably, such a mobile learning tool can be used to help non-technical undergraduates learn with greater motivation, but its success will rely on proper planning and implementation by considering students' demographic background.

Over recent years, computer science (CS) teachers and instructor have faced several challenges in assisting students to strengthen their understanding of programming. To make matters worse, existing assessment methods could be more effective in assessing students' programming skills and knowledge, thus entailing a review of issues surrounding the instructions of programming courses. Against this backdrop, the authors accomplished a systematic review of the current literature to identify several socio-cognitive factors that can help develop a learner model for learning programming. Specifically, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique was utilized to identify and select pertinent articles from three primary online databases: Scopus, Web of Science, and Eric. Initially, 401 relevant papers were identified and retrieved, which were further reduced to only 24 articles based on specific selection criteria. As revealed, several demographic factors (such as gender, age, ethnicity, and SES) and socio-cognitive factors (motivation, attitude, and interest) have been shown to wield significant impacts on student learning of programming. The authors' findings of the systematic literature review helped synthesize the learner model's essential elements that must be carefully considered and used. Arguably, the use of such a new learner model can oblige guide instructors to teach programming more efficaciously by crystallizing several students' socio-cognitive backgrounds, which collectively have a significant impact on student learning of programming courses or subjects at the primary, secondary, and tertiary levels of education, especially in the Malaysian educational context.

Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications.

Digitalization technology has emerged as a critical tool for promoting and sustaining rural entrepreneurship by providing greater access to information and new business opportunities. It can enhance the competitiveness of rural entrepreneurs by enabling them to leverage e-commerce platforms, social media, and other digital tools to access the global market. However, the uneven distribution of digital infrastructure and the lack of digital literacy in rural areas can create significant barriers to digital adoption and pose challenges to promoting sustainable rural entrepreneurship. Hence, this paper aims to structurally review related studies from 2 databases (Scopus and EconBiz) by utilizing Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A total of 30 papers were eligible for evaluation. The findings were discussed based on three themes: the role, challenges and opportunities, and impact of digitalization technology on rural entrepreneurship. The adoption of digitalization technology among rural entrepreneurs has the potential to drive economic growth, create employment opportunities, and foster sustainable development in rural areas.

This study developed and used a mobile Augmented Reality (AR) reading kit to help preschool students recognize alphabets and read simple words more effectively. This study was based on the quantitative approach involving an experimental methodology that used a one-group pretest-posttest design. In this study, the variables of interest to be measured were students’ reading skills, motivation, and self-learning. The learning treatment involved a series of reading sessions using the novel learning application that spanned three weeks, with each session lasting for two hours. The sample of this study comprised 60 preschool students, whose ages ranged from four to six, who were selected from three different preschools. The effectiveness of the novel-reading kit was evaluated in terms of students’ learning performance, learning motivation in reading, and self-learning. The data for the former were gathered from pre-testing and post-testing. At the same time, a survey was administered to the students to elicit their opinions and feedback on the last two factors. Furthermore, all descriptive and inferential statistical procedures have been selected to analyze the data. Specifically, a one-way analysis of variance (ANOVA) was applied to analyze the data, which demonstrated significant differences in the students’ reading skills, motivation, and self-learning before and after the learning interventions. These findings showed the students could recognize alphabets more accurately, read simple words more intelligently, become more motivated to read and be highly engaged in self-learning using the mobile AR reading kit.