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Complex competencies for leader education: artificial intelligence analysis in student achievement profiling
(Taylor @ Francis Online, 2024-07-21) Ramírez Montoya, María Soledad; Morales Menendez, Ruben; Tworek, Michael; Escobar Díaz, Carlos Alberto; Tariq, Rasikh; Tenorio Sepúlveda, Gloria Concepción; https://ror.org/03ayjn504; https://ror.org/03vek6s52
Future education requires fostering high-level competencies to enhance student talent, and artificial intelligence (AI) can help in profile analysis. The aim was to determine the variables that predict the GPA of students in the ‘Leaders of Tomorrow’ program through an integrated methodology of data analytics, machine learning modeling, and feature engineering in order to generate knowledge about the application of AI in social impact programs. This research focused on 466 graduates of a ‘Leaders of Tomorrow’. A regression analysis was performed to model the relationship between the dependent variable and multiple independent variables. The findings revealed: (a) Analysis of variance (ANOVA) demonstrated exceptional model fit for predicting ‘student.term_Grade Academic Performance (GPA)_program’ with an R-squared of 0.999; (b) Visual analysis showed that significant variables like age and origin-school Grade-Point Average (GPA) affect term GPA; (c) Kendall tau correlation revealed a positive correlation of origin-school GPA with term GPA and a slightly negative one with age; (d) Support Vector Machine (SVM) regression aligned actual and predicted GPAs closely, indicating high accuracy; and (e) Recursive Feature Elimination (RFE) identified ‘student_originSchool.gpa’ as the most predictive feature. This study is intended to be of value to academic communities interested in enhancing the academic profiles of students with complex competencies, as well as communities interested in applying AI in education for predictions that contribute to trajectories for training.
AI-Based platform design for complex thinking assessment: a case study of an ideathon using the transition design approach
(Taylor @ Francis Online, 2023-11-28) Sanabria Zepeda, Jorge Carlos; Alfaro Ponce, Berenice; Argüelles Cruz, Amadeo; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504; https://ror.org/059sp8j34
Emerging Artificial Intelligence-enhanced technology platforms in education warrant attention to exploring new learning strategies and dynamics. Keeping up with the accelerating momentum to bring classic traditional learning activities to Artificial Intelligence-supported platforms may unbalance the interest in developing the participants’ higher-order thinking. This article presents case study research of an Artificial Intelligence-based technological platform to measure complex thinking traits of higher education participants in an Ideathon learning scenario. The didactical strategy was grounded in the Transition Design approach, with Sharing Economy as the challenge. An overview of the process for developing Artificial Intelligence-supported activities, the challenges and risks identified in the development, and a classification model and enhancements for future implementation in a subsequent pilot are presented. The findings set a guideline for balancing Artificial Intelligence-powered educational activities and the development of the participants’ complex thinking.

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