The academic success of university students is a problem that depends in a multi-factorial way on the aspects related to the student and the career itself. A problem with this level of complexity needs to be faced with integral approaches, which involves the complement of numerical quantitative analysis with other types of analysis. This study uses a novel visual-predictive data analysis approach to obtain relevant information regarding the academic performance of students from a Peruvian university. This approach joins together domain understanding and data-visualization analysis, with the construction of machine learning models in order to provide a visual-predictive model of the students’ academic success. Specifically, a trained XGBoost Machine Learning model achieved a performance of up to 91.5% Accuracy. The results obtained alongside a visual data analysis allow us to identify the relevant variables associated with the students’ academic performances. In this study, this novel approach was found to be a valuable tool for developing and targeting policies to support students with lower academic performance or to stimulate advanced students. Moreover, we were able to give some insight into the academic situation of the different careers of the university.

Prior research on the successful design and construction of data visualization systems or information management systems has not fully taken into account the holistic experience of employees working with information within the organization but has centered on specific aspects, such as user experience or data quality, attempting to go against information management quality, as those approaches can significantly influence users’ perceptions and their motivation to effectively use such tools for decision making. This study addresses the information consumer experience (ICX) in a Chilean Higher Education institution through the design and implementation of an user-centric centralized information management system. This system was created using an adapted design thinking methodology with an ICX perspective to identify and integrate the information consumers’ demands and other factors correlated with ICX into the system’s design. The proposed system is a technological extension of an information resource validation process that involves senior data analysts from the Analytics Department and external data analysts from other departments across the organization. This process helps to address data quality and information management quality (IMQ) problems of the organization, representing a centralized data source for all information consumers into the organization, offering consistent, accessible, and good quality data to address daily work and enhance information consumers experience, and managerial work.

The experience of employees that work with information has been studied in the literature using approaches that analyze information system success (e.g., Information Management, User Experience) or employee satisfaction (e.g., Job Satisfaction, Employee Experience) as two separate problems. Therefore, there are no approaches that analyze both employee experiences and information used within the organization simultaneously. This scenario has motivated us to define a new approach based on Consumer Experience (CX), called Information Consumer Experience (ICX). In order to accomplish this objective, a systematic review was performed, including articles indexed in four databases (Scopus, Web of Sciences, ACM digital, and Science Direct) published in the last decade (from 2012 to 2022) in order to answer the following research questions: (1) What is ICX? (2) What factors influence ICX? and (3) What methods are used for ICX evaluation? We selected 127 works and analyzed various ICX-related concept definitions, research approaches, data collection, and evaluation methods. The main contribution of this review is to identify a set of definitions, approaches, and methods for ICX modeling, evaluation, and design. The results obtained have allowed us to introduce a formal definition for the ICX concept derived from the CX approach and propose future research lines to explore ICX analysis, considering the factors and methods identified in this work, as ICX can be considered a specific case of CX.