Abstract-Accepting the emergence of data mining technology by students is crucial to the successful implementation of the technology in education institutions. Although previous studies have empirically show the result of acceptance or adoption of data mining technology in numerous fields, however, they are focused at organisational-level. Hence, there is a need to explain what are the determinants could influence the acceptance of data mining technology at individual-level since they are the most affected by the technology. Therefore, this study adapts selected constructs in the Technology Acceptance Model 3 (TAM3) to conceptualise the research problem, namely in terms of perceived usefulness, perceived ease of use, relevance for analysing, anxiety of educational data mining technology, self-efficacy and facilitating conditions. To examine the model, this study surveyed 158 students from four public Institutions of Higher Learning in Malaysia. Pearson product-moment correlation coefficient is utilised to analyses the relationship between the constructs. The findings have revealed that most of the constructs have a high level of correlation.Index Terms-Acceptance, data mining, educational data mining, institutions of higher learning (IHLs), technology acceptance model 3 (TAM3). I. INTRODUCTIONData mining (DM) technology has become new paradigms to enhance the scope, quality, efficiency and achievements of educational system [1]. In particular, users increasingly benefit from the technology that provides special data interpretation and processing techniques [2]. With such capabilities, DM is now considered one of promising technologies for education [3] which commonly known as educational data mining (EDM). Earlier educational system faced a number of challenges; lack of significant knowledge for evaluating, planning, monitoring and marketing [4]. Hence, EDM has gained significant interest as suitable and realistic next-generation computer-based educational systems (CBES), which offers a potential solution to these challenges [5].In spite of the rapidly growing popularity of DM in the educational environment, most of studies have emphasize more on the technical aspect of DM application Manuscript received October 10, 2014; revised January 13, 2015. Muslihah Wook is with the Faculty of Defence Science and Technology, National Defence University of Malaysia, Sungai Besi Camp, 57000 Kuala Lumpur, Malaysia (e-mail: muslihah@upnm.edu.my).Zawiyah M. Yusof and Mohd Zakree Ahmad Nazri are with Faculty of Information Science and Technology, National University of Malaysia, 43600 Bangi, Selangor, Malaysia (e-mail: zmy@ftsm.ukm.my; mzan@ftsm.ukm.my).algorithms-ignoring the users' perception of the technology [6]- [8]. Moreover, there has been a lack of study analysing how users use the technology [9]. Therefore, it is important to identify user behaviour prior to implement the EDM technology, as this could help minimising underutilisation or eventual abandonment. II. EDUCATIONAL DATA MINING (EDM)EDM has been defined as "t...

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The popularity of big data analytics (BDA) has boosted the interest of organisations into exploiting their large scale data. This technology can become a strategic stimulation for organisations to achieve competitive advantage and sustainable growth. Previous BDA research, however, has focused more on introducing more traits, known as Vs for big data traits, while ignoring the quality of data when examining the application of BDA. Therefore, this study aims to explore the effect of big data traits and data quality dimensions on BDA application. This study has formulated 10 hypotheses that comprised of the relationships of big data traits, accuracy, believability, completeness, timeliness, ease of operation, and BDA application constructs. This study conducted a survey using a questionnaire as a data collection instrument. Then, the partial least squares structural equation modelling technique was used to analyse the hypothesised relationships between the constructs. The findings revealed that big data traits can significantly affect all constructs for data quality dimensions and that the ease of operation construct has a significant effect on BDA application. This study contributes to the literature by bringing new insights to the field of BDA and may serve as a guideline for future researchers and practitioners when studying BDA application.

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