Stress is one of the issues in mental health among the societies. Self-therapy has been an alternative to provide relaxation and to reduce stress that includes the use of guided imagery therapy (GIT). It could also take advantages of technologies that support the sense of presence. In this technology-driven era, Spatial Presence Model (SPM) has been applied in existing studies to develop virtual reality (VR) tools, while Guided Imagery Therapy (GIT) has been used as a treatment tool for potential psychological problems. Currently, no study has utilized both SPM and GIT in supporting the practice of self-therapy to reduce stress. Hence, this study aims to propose a hybridized model for Image-based VR (IBVR) that incorporates SPM and GIT for the purpose of technology-driven self-therapy. The Design science research methodology (DSRM) is used as the basis for conducting the study. The proposed model is expected to benefit the application designers as a reference in developing IBVR tools for self-therapy.

doi: 10.4156/aiss.vol2.issue2.3 Computer programming is one of the most essential skills which each graduate has to acquire. However, there are reports that they are unable to write a program well. Researches indicated there are many factors can affect student programming performance. Thus, the aim of this study is to investigate the significant factors that may influence students programming performance based information from previous student performance using data mining technique. Data mining is a data analysis technique that able to discover hidden knowledge in database. The programming dataset used in this study comprises information on the performance profile of Universiti Utara Malaysia students from 4 different bachelor programs that were Bachelor in Information Technology, Bachelor in Multimedia, Bachelor in Decision Science and Bachelor in Education specializing in IT of the November session year 2004/2005. They were required to enroll introductory programming subject as requirement to graduate. The dataset consisting of 419 records with 70 attributes were pre-processed and then mined using directed association rule mining algorithm namely Apriori. The result indicated that the student who has been exposed to programming prior to entering university and scored well in Mathematics and English subject during secondary Malaysian School Certificate examination were among strong indicators that contributes to good programming grades. This finding can be a guideline to the faculty to plan a teaching and learning program for new registered student