A Study of Application of Data Mining and Analytics in Education Domain

Karkhanis, Sahil P.; Dumbre, Shweta S.

doi:10.5120/21393-4436

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…Based on analysis of students' study results, demographics and social data, at-risk students were identified for providing individual counselling Karkhanis and Dumbre (2015) Bridgewater College Tutors were provided with detailed information to discuss with students on their progress against targets and suggested actions Sclater et al (2016) Open Universities Australia…”

Section: Albany Technical Collegementioning

confidence: 99%

Learning analytics in higher education: an analysis of case studies

Wong

2017

AAOUJ

111

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Purpose-The purpose of this paper is to present a systematic review of the mounting research work on learning analytics. Design/methodology/approach-This study collects and summarizes information on the use of learning analytics. It identifies how learning analytics has been used in the higher education sector, and the expected benefits for higher education institutions. Empirical research and case studies on learning analytics were collected, and the details of the studies were categorized, including their objectives, approaches, and major outcomes. Findings-The results show the benefits of learning analytics, which help institutions to utilize available data effectively in decision making. Learning analytics can facilitate evaluation of the effectiveness of pedagogies and instructional designs for improvement, and help to monitor closely students' learning and persistence, predict students' performance, detect undesirable learning behaviours and emotional states, and identify students at risk, for taking prompt follow-up action and providing proper assistance to students. It can also provide students with insightful data about their learning characteristics and patterns, which can make their learning experiences more personal and engaging, and promote their reflection and improvement. Originality/value-Despite being increasingly adopted in higher education, the existing literature on learning analytics has focussed mainly on conventional face-to-face institutions, and has yet to adequately address the context of open and distance education. The findings of this study enable educational organizations and academics, especially those in open and distance institutions, to keep abreast of this emerging field and have a foundation for further exploration of this area.

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Section: Albany Technical Collegementioning

confidence: 99%

Learning analytics in higher education: an analysis of case studies

Wong

2017

AAOUJ

111

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“…The program is authorized for use in predicting the future academic performance of students in an institution, in which it is destined to be used [31]. The staff is trained to apply the program during the entry of students' previous performances.…”

Section: Final Implementationmentioning

confidence: 99%

Enhancing Program Management with Predictive Analytics Algorithms (PAAs)

Lainjo¹

2021

Artificial Intelligence

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The increase in the amount of big data and the emergence of analytics technologies has created the opportunity for applying algorithm development techniques using machine learning (ML) languages to predict future events. To conduct inclusive analyses of contemporary literature of existing relevant narratives with a focus on program management themes, including state-of-the art methodologies on current plausible predictive analytics models. The methodology used is the review and applications of relevant programming platforms available. Program management requires the utilization of the existing ML languages in understanding future events. Enabling decision makers to make strategic - goals, objectives, and missions. The use of PAAs has gained thematic significance in automotive industries, energy sector, financial organizations, industrial operations, medical services, governments, and more. PAAs are important in promoting the management of future events such as workflow or operational activities in a way that institutions can schedule their activities in order to optimize performance. It enables organizations to use existing big data to predict future performance and mitigate risks. The improvements in information technology and data analytics procedures have resulted in the ability of businesses to make effective use of historical data in making predictions. This enables evidence-based planning, mitigating risks, and optimizing production.

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“…Association used two or more items that usually have the same type dataset used to identify the patterns. Apriori which is association algorithm is used to find relations between two or more entities in certain data environment such as mathematics and database [72,73]. Association using the support and confidence parameter to allow the association rule can be recognized.…”

Section: Unsupervised Data Miningmentioning

confidence: 99%

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

Tarmizi¹,

Mutalib²,

Hamid³

et al. 2019

IJMECS

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Student attrition among undergraduate students is among the most concerned issues in higher educational institutions in Malaysia and abroad. This problem arises when these students unable to complete their studies within the stipulated period when there are majoring in the Science, Technology, Engineering, and Mathematics (STEM) fields. Research findings highlight numerous factors contribute to the student attrition. These findings also suggest that the factors differ from one case to another case. Effects of student attrition not only for the student itself but also to the institutions and community. It is challenging to classify the factors based on general assumptions. Moreover, increasing students' information makes the problem more complicated. This student information can provide a useful database for analytical analysis. Methods such as big data analytics and data mining techniques can be deployed to gain insights and pattern that related to student attrition problem. The objective of this paper (i) review the student attrition in higher education (HE) and the contributing factors; and (ii) review the existing computational model to analyze and predict student attrition in HE.

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A Study of Application of Data Mining and Analytics in Education Domain

Cited by 12 publications

References 14 publications

Learning analytics in higher education: an analysis of case studies

Learning analytics in higher education: an analysis of case studies

Enhancing Program Management with Predictive Analytics Algorithms (PAAs)

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

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