scholarly journals Research on Higher Education Evaluation and Decision-Making Based on Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liu Feng
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Curriculum Design ◽  
Educational Data Mining ◽  
Design Model ◽  
Education Evaluation ◽  
Resource Evaluation ◽  
Higher Education Curriculum ◽  
Universal Applicability

Educational data mining is concerned with developing methods to explore the data from educational environments which provides insights that help in understanding the learning process and improving the educational outcomes. The evaluation and decision-making methods of higher education resources ignore the number of specific basic systems of resource evaluation and decision-making, resulting in the low accuracy of evaluation and decision-making. Therefore, a research on higher education evaluation and decision-making based on data mining is proposed. We analyze the application of big data in the field of higher education and design its optimal curriculum design model. We calculate the phased teaching task objectives of higher education curriculum, form its curriculum teaching guidance according to the influence degree between learners’ learning progress and learners’ thinking limitations, and obtain the learning effect produced by the optimal selection of curriculum teaching content. Then the probability of learners completing the structured teaching goal is calculated, so as to establish the optimal curriculum design model of higher education. Finally, we obtain the quantitative values of different experiences, extract the main influencing factors of resource evaluation and decision-making, and carry out higher education resource evaluation and decision-making analysis on this basis. The experimental results show that the research method improves the flexibility and universal applicability of higher education evaluation and decision-making, achieving an evaluation accuracy of above 90% and with below 7% error rate.

Knowledge Discovery for Large Databases in Education Institutes

2017 ◽  
pp. 27-110
Author(s):  
Robab Saadatdoost ◽  
Alex Tze Hiang Sim ◽  
Hosein Jafarkarimi ◽  
Jee Mei Hee
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Knowledge Discovery ◽  
Learning Style ◽  
Science Research ◽  
Data Set ◽  
Use Of Data ◽  
Education Data ◽  
Decision Making System

This project presents the patterns and relations between attributes of Iran Higher Education data gained from the use of data mining techniques to discover knowledge and use them in decision making system of IHE. Large dataset of IHE is difficult to analysis and display, since they are significant for decision making in IHE. This study utilized the famous data mining software, Weka and SOM to mine and visualize IHE data. In order to discover worthwhile patterns, we used clustering techniques and visualized the results. The selected dataset includes data of five medical university of Tehran as a small data set and Ministry of Science - Research and Technology's universities as a larger data set. Knowledge discovery and visualization are necessary for analyzing of these datasets. Our analysis reveals some knowledge in higher education aspect related to program of study, degree in each program, learning style, study mode and other IHE attributes. This study helps to IHE to discover knowledge in a visualize way; our results can be focused more by experts in higher education field to assess and evaluate more.

Turning the Titanic: inertia and the drivers of climate change education

2014 ◽  
Vol 6 (1) ◽  
pp. 44-62 ◽  
Author(s):  
Shireen J. Fahey ◽  
John R. Labadie ◽  
Noel Meyers
Keyword(s):  
Climate Change ◽  
Higher Education ◽  
Curriculum Design ◽  
Internal Resistance ◽  
National Standards ◽  
Content Type ◽  
Higher Education Curriculum ◽  
National Education ◽  
Global Issues ◽  
Internal Inertia

Purpose – The aim of this paper is to present the challenges external drivers and internal inertia faced by curriculum designers and implementers at institutions of higher education. The challenges to academics from competing factors are presented: internal resistance to changing existing curricula vs the necessity to continuously evolve programmes to reflect a dynamic, uncertain future. The necessity to prepare future leaders to face global issues such as climate change, dictates changing curricula to reflect changing personal, environmental and societal needs. Design/methodology/approach – This paper uses the case study method to examine two models of climate change curriculum design and renewal. One model, from an Australian university, is based upon national education standards and the second is a non-standards-based curriculum design, developed and delivered by a partnership of four North American universities. Findings – The key findings from this study are that the highest level of participation by internal-to-the-programme academics and administrators is required. Programme quality, delivery and content alignment may be compromised with either stand-alone course delivery and learning outcomes, or if courses are developed independently of others in the programme. National educational standards can be effective tools to guide course and programme management, monitoring, review and updating. Practical implications – The paper includes implications for postgraduate level curricula design, implementation and programme evaluation. Originality/value – The paper is the first to compare, contrast and critique a national standards-based, higher education curriculum and a non-standards-based curriculum.

scholarly journals Educational Data Mining, Student Academic Performance Prediction, Prediction Methods, Algorithms and Tools: An Overview of Reviews

2021 ◽  
Author(s):  
Chaka Chaka
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Overview Of Reviews ◽  
Student Demographics ◽  
Student Academic Performance ◽  
Online Databases ◽  
Subject Areas ◽  
Input Variables

This overview study set out to compare and synthesise the findings of review studies conducted on predicting student academic performance (SAP) in higher education using educational data mining (EDM) methods, EDM algorithms and EDM tools from 2013 to June 2020. It conducted multiple searches for suitable and relevant peer-reviewed articles on two online search engines, on nine online databases, and on two online academic social networks. It, then, selected 26 eligible articles from 2,050 articles. Some of the findings of this overview study are worth mentioning. First, only 2 studies explicitly stated their precise sample sizes with maths and science as the two most mentioned subject areas. Second, 16 review studies had purposes related to either EDM techniques, EDM methods, EDM models, or EDM algorithms employed to predict SAP and student success in the higher education sector. Third, there are six commonly used typologies of input variables reported by 26 review studies, of which student demographics was the most commonly utilised variable for predicting SAP. Fourth and last, seven common EDM algorithms employed for predicting SAP were identified, of which Decision Tree emerged both as the most used algorithm and as the algorithm with the highest prediction accuracy rate for predicting SAP.

scholarly journals Emerging values and implications for consumer decision-making: China’s study abroad students

2020 ◽  
Author(s):  
◽  
Robert Hayward
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Middle Class ◽  
Study Abroad ◽  
Curriculum Design ◽  
Cultural Awareness ◽  
Chinese Culture ◽  
Decision Making Process ◽  
Cultural Shift ◽  
Research Instrument

From the initial catalyst of the cultural awareness trip the researcher was a part of and the subsequent observations made during further business trips to China questions arose around the validity of the established culture literature in contemporary China and how Chinese culture impacts on the decision of where to study abroad. The overarching aim of this research programme is to develop and test a conceptual framework that could help better understand the decision making process of Chinese students applying to study at a university in the United Kingdom. The intension is to identify differences and similarities in decision making in relation to the established cultural norms and if there are significant subcultures geographically across China. A digital card sort was deployed that consisted of 75 variables, from which participants were asked to firstly identify which variables were part of their decision making process. Those that were part of the process were then ordered into three levels of significance – contributed to, were important and were essential. The results having a confidence level of 95%, the following variables are considered as essential:  I wanted to study overseas.  I want an international career.  I wanted to study in English (language).  I wanted to advance / boost my career prospects.  I can achieve a world-recognised qualification.  By studying overseas, I will be able to make my own decisions. Further analysis and discussion determined that:  A middle class exists in China, but is based on social capital.  A cultural shift has been detected in the younger generation moving towards a more individualistic view of life.  There are differences between genders in the decision making process.  There are differences in exposure to international trade and global brands across China and this influences which variables are considered to be more significant within the decision making process.  There is a need for a differentiated marketing message to be developed by organisations for optimal market penetration. The thesis therefore makes several contributions to both knowledge and to practice. Contributions to knowledge include:  Recognising the premise on which the Chinese middle class is formed.  Demonstrating a cultural shift in the millennial generation, moving towards a more individualist view of life.  Identifying gender differences in the decision making process.  Identifying how geographic location influences the significance of different decision making variables.  Creation of a research instrument that enables cultural values to recognised in the decision making process. Contributions to practice include:  The deeper understanding of the concept of middle class in China will assist organisations in their strategic marketing planning activities, as well as informing them on the focus of targeting communication processes.  By having a new understanding of how millennial Chinese are moving towards a more individualistic life style, when compared to previous Chinese generations organisations will be able to develop products and services that are more aligned to this market segment.  Higher education institutions will be better informed regarding curriculum design and the importance of including cultural experience within the overall student experience package. Further research projects have been identified that will enhance the findings from this thesis and make further contributions to knowledge and practice:  To extend the data collection from a mainly business base to encompass more subject disciplines such as computing, engineering, medicine.  To adapt the context of the decision from higher education to other major purchases such as housing and travel.  The research instrument can be repeated to establish a multi-generational perspective of Chinese decision making, degrees of power within the family context and further explore differences in gender.  A more complete geographical picture could be developed, not just of China, but to include more collectivist societies around the world including Japan and India.

scholarly journals DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW

2020 ◽  
Vol 5 (2) ◽  
pp. 166-177 ◽  
Author(s):  
Pragati Sharma ◽  
Dr. Sanjiv Sharma
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Educational Institutions ◽  
Data Set ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
New Knowledge ◽  
Hidden Patterns ◽  
Educational Field

Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining can be used for decision making in higher educational institutions. This paper is based on literature review of different data mining techniques along with certain algorithms like classification, clustering etc. This paper represents the effectiveness of mining techniques with educational data set for higher education institutions.

Machine Learning in Higher Education

2020 ◽  
pp. 27-46
Author(s):  
Garima Jaiswal ◽  
Arun Sharma ◽  
Reeti Sarup
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Data Mining ◽  
At Risk ◽  
Educational Data Mining ◽  
Training Data ◽  
Pedagogical Practices ◽  
Dropping Out ◽  
Support Vector ◽  
Class Labels

Machine learning aims to give computers the ability to automatically learn from data. It can enable computers to make intelligent decisions by recognizing complex patterns from data. Through data mining, humongous amounts of data can be explored and analyzed to extract useful information and find interesting patterns. Classification, a supervised learning technique, can be beneficial in predicting class labels for test data by referring the already labeled classes from available training data set. In this chapter, educational data mining techniques are applied over a student dataset to analyze the multifarious factors causing alarmingly high number of dropouts. This work focuses on predicting students at risk of dropping out using five classification algorithms, namely, K-NN, naive Bayes, decision tree, random forest, and support vector machine. This can assist in improving pedagogical practices in order to enhance the performance of students predicted at risk of dropping out, thus reducing the dropout rates in higher education.

Comparison of Different Classification Techniques for Educational Data

2017 ◽  
Vol 9 (1) ◽  
pp. 54-67 ◽  
Author(s):  
Kavita Pabreja
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Educational Data Mining ◽  
Real Data ◽  
Classification Techniques ◽  
Great Acceptance ◽  
Education Field

Data mining has been used extensively in various domains of application for prediction or classification. Data mining improves the productivity of its analysts tremendously by transforming their voluminous, unmanageable and prone to ignorable information into usable pieces of knowledge and has witnessed a great acceptance in scientific, bioinformatics and business domains. However, for education field there is still a lot to be done, especially there is plentiful research to be done as far as Indian Universities are concerned. Educational Data Mining is a promising discipline, concerned with developing techniques for exploring the unique types of educational data and using those techniques to better understand students' strengths and weaknesses. In this paper, the educational database of students undergoing higher education has been mined and various classification techniques have been compared so as to investigate the students' placement in software organizations, using real data from the students of a Delhi state university's affiliates.

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