Using Data Analytics to Monitor Gender Equality in Higher Education Institutions

Gaftandzhieva, Silvia; Doneva, Rositsa; Atanasova, Marieta; Bliznakov, Milen

doi:10.14445/22315381/ijett-v70i12p202

Cited by 2 publications

(2 citation statements)

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“…To help university decision-makers make decisions to increase retention rate and improve student success rate Gaftandzhieva, Doneva and Bliznakov [94] offer a tool for monitoring student success from. The StudAnalyst tool allows programme managers, deans and rector to monitor 42 quantitative indicators divided into 3 groups (Student success during the training, Student success in graduation, Gender gap) and generate reports for each indicator with retrieved values when s/he wants to see the current situation in the faculty/university depending on its user role.…”

Section: Alisan and Serinmentioning

confidence: 99%

Data-driven Decision Making in Higher Education Institutions: State-of-play

Gaftandzhieva¹,

Hussain²,

Hilčenko³

et al. 2023

IJACSA

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The paper highlights the importance of using datadriven decision-making tools in Higher Education Institutions (HEIs) to improve academic performance and support sustainable development. HEIs must utilize data analytics tools, including educational data mining, learning analytics, and business intelligence, to extract insights and knowledge from educational data. These tools can help HEIs' leadership monitor and improve student enrolment campaigns, track student performance, evaluate academic staff, and make data-driven decisions. Although decision support systems have many advantages, they are still underutilized in HEIs, leaving field for further research and implementation. To address this, the authors summarize the benefits of applying data-driven decision approaches in HEIs and review various frameworks and methodologies, such as a course recommendation system and an academic prediction model, to aid educational decision-making. These tools articulate pedagogical theories, frameworks, and educational phenomena to establish mainstay significant components of learning to enable the scheming of superior learning systems. The tools can be utilized by the placement agencies or companies to find out their probable trainees/ recruitees. They can help students in course selection, and educational management in being more efficient and effective.

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Section: Alisan and Serinmentioning

confidence: 99%

Data-driven Decision Making in Higher Education Institutions: State-of-play

Gaftandzhieva¹,

Hussain²,

Hilčenko³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…They summarized the essential place of evidence-well-based methods that promulgate organizations agility and flexibility in the ability to respond to dynamic environmental issues. [19] Gaftandzhieva et al (2023) highlighted covered current data-driven decision-making in universities as well as a holistic gained perspective on the usage of data analytics for strategic planning, getting resource distributed and performance monitoring. [20] Gaol (2021) studied performance management of artificial intelligence systems as one of the strategies to improve human capital competitiveness in Indonesia in order to boost the human resources' performance in any positions and aspects of the economy.…”

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confidence: 99%

Towards a Framework for Performance Management and Machine Learning in a Higher Education Institution

Dr Joel Osei-Asiamah,

Dr. Abhishek Tripathi

2024

jier

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This paper proposes a new structure for management and machine learning in higher education institutions, which is designed to improve the efficiency of an organization and the success of the students at a whole. The framework brings about the enactment of several analytical techniques, like predictive modeling and data-driven decision making, which help to make accurate strategies for planning and providing continuous improvement. Four algorithms in machine learning- Linear Regression, Decision Tree, Random Forest and Multilayer Perceptron- are compared to see if they predict important performance markers for student success, faculty productivity and institutional efficiency. The results illustrate the Multilayer Perceptron algorithm as the best performer, getting MSE of 0.018 and MAE of 0.105, while R2 score of 0.842, showing the superiority of MLP over the others. Validation studies done comparing it with base line models or related models in the field are proof that the suggested model is widely applicable among the higher education spectrum in dealing with the involved issues. The imaginable framework seems to be a prospective tool for stimulating creativity, inclusion, and eminence in academia while adding to the knowledge acquisition and achieving institute objectives.

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Using Data Analytics to Monitor Gender Equality in Higher Education Institutions

Cited by 2 publications

References 0 publications

Data-driven Decision Making in Higher Education Institutions: State-of-play

Data-driven Decision Making in Higher Education Institutions: State-of-play

Towards a Framework for Performance Management and Machine Learning in a Higher Education Institution

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