Academic Analytics and Data Mining in Higher Education

Baepler, Paul; Murdoch, Cynthia James

doi:10.20429/ijsotl.2010.040217

Cited by 194 publications

(113 citation statements)

References 12 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

The Acceptance of Educational Data Mining Technology among Students in Public Institutions of Higher Learning in Malaysia

Wook¹,

Yusof²,

Nazri³

2015

IJFCC

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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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Section: Introductionmentioning

confidence: 99%

The Acceptance of Educational Data Mining Technology among Students in Public Institutions of Higher Learning in Malaysia

Wook¹,

Yusof²,

Nazri³

2015

IJFCC

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“…In 2014, the Ministry of Education, Ministry of Finance and so on five ministries and commissions appear the related document, integrates the education informationization the national education special direction content [1]. The national medium and long-term education reformed and develops in the plan summary to manifest the technical function, by the technical actuation reform, has been clear about the education informationization importance.…”

Section: The Importance Of New Technology and Teacher Education Fmentioning

confidence: 99%

Research and practice of new technology and teacher education fusion mode under the future education space station

Kong¹,

Tao²

2015

Proceedings of the 2015 International Conference on Social Science and Higher Education

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Abstract-This paper analyzed the definition of the integration of new technology and teacher education in the future education space station, and discusses the new technology and the depth of the integration of the teacher education connotation. And then from the perspective of process division dimension fusion of new technology and teacher education, build the future educational space station "new technology and teacher education integration mode is established. Finally, the scientific evaluation model sustained track record and evaluate the quality of the sample data.

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“…As such, the use and implementation of data governance for learning analytics in the context of higher education requires a shared understanding of the goals and purposes of learning analytics across 1 The term has roots in different areas, viz., business intelligence (with its own roots in data warehousing, customer relationship management, and web intelligence), educational data mining (including but not limited to the mining of the learning and content management systems' user and usability data), and recommender systems. For an overview of the history of the emergence of the field of learning analytics, see, for example, Romero & Ventura, 2006;Baepler & Murdoch, 2010;Ferguson, 2012;Chatti, Dyckhoff, Schroeder, & Thüs, 2012. different layers of the governance forms and structures. The diffusion of such shared understanding of these goals and functions may require modifying and adapting some aspects of the various governance models that exist at the educational institution (e.g., institutional governance, IT governance, etc.)…”

Section: Defining Learning Analytics and Governancementioning

confidence: 99%

Critical Factors In Data Governance For Learning Analytics

Elouazizi

2014

Learning Analytics

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This paper identifies some of the main challenges of data governance modelling in the context of learning analytics for higher education institutions, and discusses the critical factors for designing data governance models for learning analytics. It identifies three fundamental common challenges that cut across any learning analytics data governance model, viz., the ownership of the learning analytics data sets, the interpretation of the data, and the decision making based on the data. It also proposes a set of high-level requirements necessary for modelling data governance for learning analytics.

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Academic Analytics and Data Mining in Higher Education

Cited by 194 publications

References 12 publications

The Acceptance of Educational Data Mining Technology among Students in Public Institutions of Higher Learning in Malaysia

The Acceptance of Educational Data Mining Technology among Students in Public Institutions of Higher Learning in Malaysia

Research and practice of new technology and teacher education fusion mode under the future education space station

Critical Factors In Data Governance For Learning Analytics

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