Individuals learn in different ways using several learning styles, but lecturers may not always share material and learning experiences that match students’ learning preferences. Mismatches between learning and teaching styles can lead to disappointment with students are taking, and lead to underperformance among them. The aim of this study is to identify the learning styles of the students enrolled in Universiti Malaysia Pahang who were registered in Programming Technique course and to investigate the relationship between students’ learning styles and teachers’ teaching styles. Five lecturers and 251 students were involved in the study as participants and. Data from students were collected using Leonard, Enid’s VAK Learning Style Survey. Meanwhile, the teaching styles of the lecturers were identified using Grasha and Reichmann’s Teaching Style Survey. The findings revealed that majority of the student’s preferred visual learning style. The result also shows that the lecturers’ teaching styles give an impact towards the students’ academic performance. From this study, we can conclude that teaching styles have significant impacts on students’ learning styles and academic performances.

Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.

Educational institutes are adopting Online Social Networks (OSNs) to facilitate learning activities in university campuses. However, before implementation a new technology there is need to identify and measure the factors that influence users' acceptance of the new technology. This will aid to critically predict the success or failure of the new technology that is to be adopted. Therefore, this study aims to identify the factors that influence student’s acceptance of online social networks as learning tool. Besides, a research model is developed based on the identified factors grounded on Technology Acceptance Model (TAM). In addition, Structural Equation Model (SEM) was employed to validate the developed model hypotheses based data collected by employing online survey from 537 students in University Malaysia Pahang (UMP). Furthermore, findings from this study have significant implications and considerable value for higher educational institutes to devise better strategies for adoption of OSNs' as a learning tool.