This study was aimed to identify pupils’ internet usage, gaming experience, learning problems, educational values and change in behaviour using Kahoot in biology. A mixed method approach with embedded design was used. The study used purposive sampling with ten form 4 biology students, four biology teachers and four parents as participants in the qualitative part of the study while random sampling was used for 250 students who were respondents for the survey. The instruments used to gather qualitative data were interview protocols, field notes and document analysis while questionnaire was used to gather quantitative data. All instruments were checked and validated by five experts with percentage agreements of more than 90%. Reliability was carried out through member checking, peer checking, expert consultation, and pilot study with Cronbach’s alpha value of 0.752. There were 18 interviews, ten document analysis, four observations and a survey. Data analysis for interview .....

2655 hits

This study investigated the usage of Digital Games (DG) in learning Biology among Form Four students in Malaysia. It is aimed at identifying general overview of the students’ interest in using DG in learning of Biology. It also explored the effectiveness as well as the problems faced by students’ while using DG in the learning process. This study was conducted in a sub-urban secondary school in Kinta District, Perak. Six Form 4, consisted of four males and two females students participated in this study. A qualitative research design was utilized to answer the research questions. Individual indepth interviews and also laboratory observations were carried out to collect the data needed for this study. The data was analysed using coding. ‘Verbatim Transcripts’ were used to describe the data. Analysis of the data reveals that DG arouses students’ interest as well as motivates them in learning Biology. In addition, the results also indicate that students gain benefits .....

535 hits

Affordable housing especially in a city environment is recognized as one of citizen needs among the mid-dle-income groups. This research paper intended to explore the possibilities to use data mining algorithms: Linear Regression, Random Forest and Gradient Boosting algorithms for predicting and analyzing the housing affordability price for middle-income earners in Petaling Jaya district, Malaysia. The dataset from Malaysia House Index by Petaling Jaya district used to evaluate based on the proposed algorithms. The dataset extracted based on residential property sub-sectors with three attributes. Based on the prediction models, as results, the Gradient Boosting algorithm shows higher accuracy of 74% for predicting affordable housing price in Petaling Jaya district, Malaysia compared to other prediction techniques. Keywords: Affordable housing price; Linear regression; Random forest and gradient boosting..

517 hits