Analytics on talent search examination data

Abstract

Learning analytics and educational data mining has greatly supported the process of assessing and improving the quality of education. While learning analytics has a longer development cycle, educational data mining suffers from the inadequacy of data captured through learning processes. The data captured from examination process can be suitably extended to perform some descriptive and predictive analytics. This paper demonstrates the possibility of actionable analytics on the data collected from talent search examination process by adding to it some data pre-processing steps. The analytics provides some insight into the learner's characteristics and demonstrates how analytics on examination data can be a major support for bringing the quality in education field.

Management Implications for HR Analytics in Light of Systems Theory

“…During the searches in the Scopus database, other papers with themes related to the use of analytics in the context of HR could be found, such as e-HRM (electronic Human Resources Management, related to IT support in the construction and implementation of IS prepared for the HR demands, according to Schalk et al, 2013), Artificial Intelligence and Big Data in specific HR practices and subsystems (such as talent management and admissions, for example), according to Hamilton & Sodeman (2020), Pillai & Sivathanu (2020), Vaidya et al (2020), Garcia-Arroyo & Osca (2019), Oentaryo et al (2018), Brynjolfsson and Mitchell (2017), Aral et al (2012), Yasodha & Prakash (2012) and Jantan et al (2009). Such applications are sometimes addressed without the use of typical keywords such as "Human Resources Analytics" or "People Analytics" and may delineate a field derived from the use of technology in the context of HRM, but perhaps not central to HR Analytics, as suggest Cheng & Hackett (2021).…”