Educational data mining acceptance among undergraduate students

Wook, Muslihah; Yusof, Zawiyah Mohammad; Nazri, Mohd Zakree Ahmad

doi:10.1007/s10639-016-9485-x

Cited by 30 publications

(28 citation statements)

References 59 publications

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“…All but one of these countries has a Western cultural tradition (as defined by Huntington, 1991): United Kingdom, Canada, United States, Australia, New Zealand, Germany, France, Austria, and Netherlands. Two studies set in Malaysia were identified (Wook et al, 2015(Wook et al, , 2017. The papers described the views of students at Australian, German and Austrian, German and French, and Malaysian higher education institutions and the perspectives of staff from the United Kingdom, United States, Australia, and New Zealand, and internationally.…”

Section: Resultsmentioning

confidence: 99%

Student and Staff Perspectives on the Use of Big Data in the Tertiary Education Sector: A Scoping Review and Reflection on the Ethical Issues

Braunack-Mayer¹,

Street

Tooher³

et al. 2020

Review of Educational Research

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While universities routinely use student data to monitor and predict student performance, there has been limited engagement with student and staff views, social and ethical issues, policy development, and ethical guidance. We reviewed peer-reviewed and grey-literature articles of 2007 to 2018 describing the perspectives of staff and students in tertiary education on the use of student-generated data in data analytics, including learning analytics. We used an ethics framework to categorize the findings. There was considerable variation but generally low awareness and understanding amongst students and staff about the nature and extent of data collection, data analytics, and use of predictive analytics. Staff and students identified potential benefits but also expressed concerns about misinterpretation of data, constant surveillance, poor transparency, inadequate support, and potential to impede active learning. This review supports the contention that consideration of ethical issues has failed to keep pace with the development of predictive analytics in the tertiary sector.

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Section: Resultsmentioning

confidence: 99%

Student and Staff Perspectives on the Use of Big Data in the Tertiary Education Sector: A Scoping Review and Reflection on the Ethical Issues

Braunack-Mayer¹,

Street

Tooher³

et al. 2020

Review of Educational Research

View full text Add to dashboard Cite

show abstract

“…(Wook, Yusof and Nazri 2016). While the analysis presented in this article was aggregated and anonymised, live integration of the analytics into the Learning Management System (LMS) would require some level of disaggregation to ensure that students in need of support are identified for targeted interventions.…”

Section: Key Considerations For the Implementation Of Results From Edmentioning

confidence: 99%

“…Wook, Yusof and Nazri (2016) found that relevance of the analysis, self-efficacy of individual users, conducive environmental conditions, perceived usefulness, perceived ease of use, optimism about learning analytics and discomfort experienced about learning analytics influenced the acceptance of EDM among 211 undergraduate students at six public universities in Malaysia. In summary, their results indicate that students who are optimistic toward technological advancements, and who are selfefficacious, are more likely to use EDM when the usefulness and usability of the analytics are made readily apparent.…”

Section: Legal and Ethical Considerations In Learning Analyticsmentioning

confidence: 99%

“…In summary, their results indicate that students who are optimistic toward technological advancements, and who are selfefficacious, are more likely to use EDM when the usefulness and usability of the analytics are made readily apparent. Furthermore, they state that the relevance of the analysis, paired with conditions that facilitate access to and the application of EDM, further enhances the likelihood of EDM uptake among the sample (Wook, Yusof and Nazri 2016). The focus of this study is on addressing the principle of increasing the transparency of the data model by making explicit the underlying accuracy and performance of the algorithms utilised to make predictions as to student success.…”

Section: Legal and Ethical Considerations In Learning Analyticsmentioning

confidence: 99%

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A comparison of the utility of data mining algorithms in an open distance learning context

Fynn

Adamiak

2018

SAJHE

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The use of data mining within the higher education context has, increasingly, been gaining traction.A parallel examination of the accuracy, robustness and utility of the algorithms applied to data mining is argued as a necessary step toward entrenching the use of EDM. This article provides a comparative analysis of various classification algorithms within an Open Distance Learning institution in South Africa. The study compares the performance of the ZeroR, OneR, Naïve Bayes, IBk, Simple Logistic Regression and the J48 in classifying students within a cohort over an eightyear time span. The initial results appear to show that, given the data quality and structure of the institution under study, the J48 most consistently performed with the highest levels of accuracy.

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“…Questionnaire is an effective data collection tool or mechanism for researchers when conducting a survey [20]. The data collected must be up-to-date, uniform and adhere to prescribed sampling procedures [11].…”

Section: A Research Designmentioning

confidence: 99%

Instrument Development for Measuring the Acceptance of UC&C: A Content Validity Study

Alias

Mukhtar

Jenal

2019

IJACSA

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Studies on the acceptance of Unified Communications and Collaboration (UC&C) tools such as instant messaging and video conferencing have been around for some time. Adoption and acceptance of UC&C tools and services has boosted productivity and improved communications by integrating voice, video and data into one platform. UC&C also allows collaboration by enabling users to interact with each other in different media. However, their acceptance rate by individual in developing nations has been low. It is hypothesized that the factors that contribute to acceptance are developed based on two underlying theories. The first is that of diffusion of innovation and the other is that of service dominant logic. Items is constructed based on eight constructs which are relative advantage, compatibility, ease of use, trialability, observability, improved service, value co-creation capacity, and coordination efficiency. In order to validate the items, content validity ratios are calculated on a set of questionnaire. The ratios will determine which items should be included or removed from the questionnaire. The paper concludes with a discussion on the implications of the findings from the experts' evaluation and also from the content validity ratios. The new items are used in designing the survey instrument to measure the acceptance of UC&C.

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Educational data mining acceptance among undergraduate students

Cited by 30 publications

References 59 publications

Student and Staff Perspectives on the Use of Big Data in the Tertiary Education Sector: A Scoping Review and Reflection on the Ethical Issues

Student and Staff Perspectives on the Use of Big Data in the Tertiary Education Sector: A Scoping Review and Reflection on the Ethical Issues

A comparison of the utility of data mining algorithms in an open distance learning context

Instrument Development for Measuring the Acceptance of UC&C: A Content Validity Study

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