Alex Tze Hiang Sim

Logic-Based Pattern Discovery

In the data mining field, association rules are discovered having domain knowledge specified as a minimum support threshold. The accuracy in setting up this threshold directly influences the number and the quality of association rules discovered. Often, the number of association rules, even though large in number, misses some interesting rules and the rules quality necessitates further analysis. As a result, decision making using these rules could lead to risky actions. We propose a framework to discover domain knowledge report as coherent rules. Coherent rules are discovered based on the properties of propositional logic, and therefore, requires no background knowledge to generate them. From the coherent rules discovered, association rules can be derived objectively and directly without knowing the level of minimum support threshold required. We provide analysis of the rules compare to those discovered via the a priori.

Behavioral intention in social networking sites ethical dilemmas

The expanding use of social networking sites (SNSs) and their flexible functionalities allow individuals to use these sites in unexpected and unethical ways. SNS users face ethical dilemmas each day and they need to decide how to behave in these cases. The aim of this study was to identify the influential factors that have an impact on individuals ethical decision-making and propose a model of the factors that are significant in the ethical decision-making process in the SNS context. This study employed the Theory of Planned Behavior (TPB) and included personal normative beliefs, moral intensity, perceived threat of legal punishment to the main constructs of TPB namely attitude, subjective norms, perceived behavioral control to predict the behavioral intention using a scenario based questionnaire. The moderating effects of several factors, including age, gender, level of income, ego strength, locus of control and religion, were investigated for the probable effect on the proposed model. Derived from a designed procedure, several scenarios were constructed, of which four were selected to be embedded in the survey instrument. With 441 returned questionnaires in hand, the collected data were analyzed using the partial least squares-structural equation modeling technique. The results showed attitude to be the most influential factor, followed by subjective norms, perceived behavioral control, personal normative beliefs, and moral intensity. Level of education, age, and scenario also showed a significant effect on the relation between predictors and behavioral intention. The study was conducted in Malaysia with a multi-cultural context.We model ethical behavioral intention of students in case of SNSs dilemmas.SNS related ethical dilemma scenarios were designed and used in this research.TPB was found a sound theory to predict moral intention to use in SNS context.5 of 6 predictor and 4 of 8 moderators were observed as influential factors.

Exploring MOOC from education and Information Systems perspectives: a short literature review

Massive Open Online Courses (MOOCs) have gained significance as a new paradigm in education. MOOCs are open to any interested person and provide education products for a scalable number of learners who have access to the Internet. In recent years, there has been an increasing interest in MOOCs including their types, theories and challenges. Although this is an important and challenging field for Information Systems researchers, a rich and structural literature review has not yet been conducted in this area. The purpose of this article is to present a short review of recent research into MOOCs. Thirty-two papers were selected for review through four search engines with a search of key terms related to MOOCs. On the basis of this review, a holistic definition on MOOCs is presented and an overview of the relevant theories and issues is provided.

Application of self organizing map for knowledge discovery based in higher education data

This paper focuses on knowledge discovery among attributes of Iran Higher Education Institute using self organizing map (SOM); the key problem with massive volume of data is extracting knowledge and patterns that are hidden in data. Managerial needs to explore this data for the purpose of decision making and strategy making reveals its importance‥ Furthermore it can be useful for researchers that study and research about higher education. Meanwhile planning for higher education has significant impact on developing of one society, successful planning needs to analysis some huge and historical data that is available in higher education institutes. SOM is a particular type of neural network used in clustering and helps discover patterns and relations without advanced knowledge about them. The steps of this approach can be discussed under five headings, which are (i) Data Preparation (ii) Data Loading, (iii) Initializing, (iv) Map training and (v) Interpretation of the results. The target dataset contains data of five universities located in Tehran, Iran affiliated to Medical Ministry of Iran and the most important attributes are program of study, learning style, study mode and degree. Results show that the number of enrolling students for Tehran medical university has decreased for the past 23 years from 1988 to 2005. This study also finds that Tehran University of Medical Science covers the majority of high degrees like MDdisplay(Doctor of Medicine) and PhD. The findings of this study can be used in improving of higher education decision making systems and the results of this study indicate SOM toolbox utility in similar institutes to knowledge discovery in a visualizing way.

Identifying places of interest for tourists using knowledge discovery techniques

Tourism is one of the most significant industries all across the globe. Information technology plays an indispensable role in promoting the industry. We utilized a web crawler to obtain online tourists demographics and comments. This study adopts a new perspective in analyzing tourists profiles where both quantitative (data mining) and qualitative analysis methods were performed on the data collected from one of the most frequently visited travel websites. Results from both data analysis methods that supported each other were used to build a more definite model. In this study, results show there were different groups of tourist with each group having diverse preferences. For instance, local visitors of Legoland were not satisfied with some drawbacks such as the food price whereas, international visitors praised for their enjoyable experiences spent with families. Findings from this study are useful for tourism organizations in Malaysia. Additionally, the research method using both quantitative (data mining) and qualitative methods in this study is found unique and rare in social studies.

The importance of negative associations and the discovery of association rule pairs

Association Rule Mining (ARM) technique is one of the popular data mining techniques used to discover knowledge from a database. In this paper, we highlight the subsequent drawbacks to association rule mining without considering the absence of items. We propose a novel approach to mine both positive and negative association rules as rule pairs. Our approach ensures that impacts of negative associations are considered so that the drawbacks identified can be avoided. Our initial experiment results show that mining pairs of association rules invoking negative associations are small in number and easy to be appreciated for its implications for decision-making.

Designing a Scenario-Based Questionnaire to Assess Behavioral Intention in Social Networking Sites’ Ethical Dilemmas

Behavior in the field of Information systems is a popular topic of research. Researchers in different contexts try to propose models and theories to show the influential factors that influence individuals’ behavior. In these kinds of researches, the behavior of people in a certain context is investigated and a set of variables as its predictor are being proposed. However, in some cases such as individuals’ behavior in context of ethical dilemmas it is not possible to observe or ask respondents about the behavior directly. In these cases, a scenario-based questionnaire might be very helpful. This paper proposes a method to design proper scenarios for these kinds of questionnaires focusing on Social Networking Sites’ ethical dilemmas.

Understanding the Setting of a MOOC: A Journey into Coursera

Massive Open Online Courses MOOC are becoming increasingly difficult to ignore in online education. Recent developments in MOOC have heightened the need for understanding the MOOC. The issue of an in-depth understanding of the MOOC setting has been a key subject within the field of qualitative research. However, far too little attention has been paid to the MOOC setting. This paper seeks to put the Netnography methodology into context by presenting a description of the Coursera community to better understand this community. It contributes by providing details about Coursera community through our engagement in this community.

Cloud Computing for Teaching Practice: A New Design?

Recently researchers have shown an increased interest in cloud computing technology. It is becoming increasingly difficult to ignore cloud computing technology in education context. However rapid changes in information technology are having a serious effect on teaching framework designs. So far, however, there has been little discussion about cloud computing benefits in domains of teaching frameworks which propels us to study and redesign teaching frameworks considering cloud computing. The purpose of this paper is to review recent research into cloud computing and features which can be improved with this new technology. This paper studied several researches through literature to determine the main impact of cloud computing on planning and preparation and instruction domains as two main domains of teaching framework. Light will be then shed on the impact and potential benefits of cloud computing on teaching framework. The paper closes by proposing to design an evaluation table based on cloud computing artifacts to enhance teaching practice and highlights its offerings for educational institutions.

Knowledge Discovery for Tourism Using Data Mining and Qualitative Analysis: A Case Study at Johor Bahru, Malaysia

This paper aims to propose a new guideline for analyzing tourist profiles as found in www.tripadvisor.com. These have been examined from two different aspects so as to gain conclusive results. Tourist data were “crawled†from tripadvisor.com through a specific web crawler. Mining techniques using a combination of visualization, clustering, and association rules were instrumental in discovering the first set of interesting knowledge. This was followed by a qualitative analysis applied through Nvivo software via coding of the tourists comments in order to define the design of the prospective model. A final set of results was obtained once both results confirmed each other. In this study, results show that there are several types of tourists; with each group having different preferences. For example: male Singaporean visitors to hotels tend to enjoy wine and food in addition to outdoor activities; while local visitors to Legoland are not satisfied with certain aspects, such as the price of food. International tourists, however, consider the affirmative points of Legoland. This research can be very useful for tourist associations and hotel managers in Johor Bahru.