Discovering Performance Evaluation Features of faculty Members using Data Mining Techniques to Support Decision Making

Amani, Mahdi; Salama, Shaimaa

doi:10.5120/ijca2019919417

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Lecturer career development is part of human resource management in educational institutions. Human resource management in educational institutions might concentrate on evaluating teacher performance to make appropriate recommendations and increase the competency of teachers and lecturers [1].…”

Section: Introductionmentioning

confidence: 99%

“…The BERT pre-training model can be fine-tuned with just one additional output layer for advanced modeling of various tasks, such as language inference queries, without any substantial task-specific architectural modifications. Research related to the BERT model is done by fine-tuning using the top layer of the BERT model for text classification [5].…”

Section: Introductionmentioning

confidence: 99%

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Development of Classification Method for Lecturer Area of Expertise Based on Scientific Publication Using BERT

Didi Rustam,

Suhendra,

Harmanto

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Implementing the Artificial Intelligence concept in higher education can be utilized in the context of Human Resource (HR) talent management. The lecturer portfolio provided by the Integrated Resource Information System (SISTER DIKTI) is expected to give an overview of the profiles of all lecturers and map competencies based on groups of fields of knowledge. However, the map of scientific fields based on SISTER data currently available is still subjective. The data is in the form of a group of lecturers' chosen fields of science, independently selected by each lecturer to recognize their expertise. This study discusses the problem of processing unstructured SISTER data. It looks for mapping solutions and classification methods by developing a strategy for classifying groups of scientific fields from unstructured data input. It is necessary to identify the suitability of the chosen field of science compared to that developed through the tri-dharma through identification based on a mapping of the group of fields that can be extracted from the tri-dharma activity, in this case, research represented by scientific publications recorded on SISTER. Therefore, we need an appropriate model to measure similarity, which can then be classified based on abstract documents and scientific publication titles for the classification of scientific fields using NLP based on classification run on DGX A100. This study aims to develop a classification method from titles and abstracts. Scientific publications contained in SISTER are unstructured data, so a corpus is formed to identify the lecturer's field of science. The results show that the classification method developed in this study can measure the similarity of lecturer publications based on abstracts and titles through a vector formation process based on bidirectional encoders and also produces a deep learning model to classify 24 categories of fields of science with an accuracy of 95,0345 percent on training data and 92.876 percent on the test data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Classification Method for Lecturer Area of Expertise Based on Scientific Publication Using BERT

Didi Rustam,

Suhendra,

Harmanto

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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A Comprehensive Review of Data Mining Usage in Education

Kilicer

Şamlı

2022

Advances in Human and Social Aspects of Technology

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In this chapter, the data mining method in the field of education will be examined, an emerging technology. In this study, studies conducted since 2011 using data mining method, one of the developing technologies, and the results obtained from these studies will be examined in order to increase the success in the field of education. What kind of data has been used in data mining application in the field of education, which algorithms have been used to analyze these data and the success cases obtained from these algorithms will be examined. By examining the results obtained from these studies, it will be examined which algorithms are more successful in the analysis to be obtained. It is aimed to identify the deficiencies that affect the success in the field of education. This study is aimed to be a guide in determining the work to be done to increase the success in the education sector and in determining the algorithms that can be preferred in order to achieve more successful results in these studies.

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Data-Driven Decisions for Employee Motivation

Chakraborty,

V. S.,

Sharma

2024

Advances in Logistics, Operations, and Management Science

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Performance reviewing (PR) is a cornerstone of human resource management (HRM), crucial for assessing and improving employee performance. This chapter explores how PR aligns individual efforts with organizational goals, motivating employees through recognition and constructive feedback. It examines the revolutionary impact of HR metrics and technology, including key performance indicators (KPIs), performance ratings, and performance management software (PMS). The chapter discusses the evolution of PMS, its role in streamlining PR processes, and the integration of artificial intelligence (AI) and data analytics to enhance objectivity and decision-making. It addresses challenges such as data security, resistance to change, potential biases, and the need for a personal touch in feedback. Real-world examples illustrate successful technology implementation, highlighting best practices and future trends. By navigating these challenges, organizations can leverage technology to enhance employee motivation, engagement, and overall performance.

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Discovering Performance Evaluation Features of faculty Members using Data Mining Techniques to Support Decision Making

Cited by 3 publications

References 8 publications

Development of Classification Method for Lecturer Area of Expertise Based on Scientific Publication Using BERT

Development of Classification Method for Lecturer Area of Expertise Based on Scientific Publication Using BERT

A Comprehensive Review of Data Mining Usage in Education

Data-Driven Decisions for Employee Motivation

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