Competency-based assessment tools for engineering higher education: a case study on complex problem-solving

• Published in: Cogent education 2024-12, Vol.11 (1)
• Main Authors: Garay-Rondero, Claudia Lizette, Castillo-Paz, Alvaro, Gijón-Rivera, Carlos, Domínguez-Ramírez, Gerardo, Rosales-Torres, Conrado, Oliart-Ros, Alberto
• Format: Article
• Language: English
• Subjects: Assessment & Testing; authentic assessment; competency-based education; Educational innovation; Engineering Education; engineering higher education; Higher Education; personalised learning technologies
• ISSN: 2331-186X; 2331-186X
• DOI: 10.1080/2331186X.2024.2392424

Higher Education faces challenges in providing learning experiences and cultivating competencies in our modern environment. Academic discourse centres on tutoring automation, a complicated educational dilemma in the age of Machine Learning, Deep Learning, and Artificial Intelligence. Establishing a structured technique for real competency assessments is an early challenge. Therefore, this research develops and implements a structured evaluation process to assess student competencies in complex problem-solving scenarios. The study finds dimensional variation in student performance through statistical analysis of multivariate data from two engineering courses, demonstrating that each evaluation method measures different competencies. Key findings demonstrate that while traditional evaluation methods provide quantitative results, the newly proposed instruments offer a nuanced understanding of student abilities by capturing quantitative and qualitative data. This approach standardises the assessment process and lays the groundwork for developing intelligent tutoring systems to automate and personalise learning. This method may lead to computer-assisted instruction to adapt educational technology for engineering complex problem-solving and other skills. It provides a solid foundation for personalised learning technology assessment methods. Higher education faces significant challenges in providing effective learning experiences and cultivating essential competencies. This study addresses these challenges by developing advanced assessment instruments to evaluate complex problem-solving skills in engineering courses. Utilising a detailed competence rubric and a binary evaluation checklist, the research standardises the assessment process, offering a comprehensive analysis of student performance. Statistical analysis of data from online and hybrid courses reveals that these tools capture a wide range of competencies more effectively than traditional methods. This structured approach enhances the objectivity and reliability of evaluations and paves the way for Intelligent Tutoring Systems (ITS). These ITS can automate and personalise learning using artificial intelligence and machine learning, significantly improving the teaching and assessment of complex problem-solving and other critical skills in higher education. This innovative method establishes a robust foundation for the future of personalised learning technologies.