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Conference Program

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Daily Overview

Session

H.15. Migrant-background Youth in Education: the Role of Policy and Practice in (Re)producing and Challenging Inequalities (1/2)
Location: Scienze Politiche (CU002): Aula A

Convenor(s): Berenice Scandone (Polytechnic University of Milan, Italy); Giulia Marroccoli (Polytechnic University of Milan, Italy)

Presentation 6

The Development of Computer Adaptive Learning and Assessment: Toward Increasing Student Success and Intercultural and Social Tolerance

Rina Manuela Contini¹, Michael Nettles², Marianna Boero³

1: University eCampus, Italy; 2: Morgan State University, MD, U.S.A.; 3: University of Teramo, Italy

The emergent development of Adaptive STEM Learning and Assessment Research focuses on advancing our knowledge and understanding of learning and assessment in science, technology, engineering, and mathematics (STEM) through computer-adaptive systems. While the primary aim is improving learning, retention, and degree completion opportunities among students, it also aims to enhance intercultural collaboration and dialogue (Cantle, 2012), and social equality and justice (Pica-Smith, Contini, Veloria, 2019).

Integrating research on computer-adaptive learning (CAL), computer-adaptive assessment (CAA), psychometrics, and socio-semiotic should permit a better understanding of how real-time, data-driven feedback supports both student learning and faculty instructional design and delivery across socially diverse populations via technology. Computer adaptive learning (CAL) and computer adaptive assessment (CAA) systems offer the opportunity to expand the scientific understanding of how students develop conceptual, procedural, and metacognitive competencies and tolerance. The work contributes to evidence-centered design (ECD), multidimensional psychometric modeling, socio-cognitive learning theory, and student success. The proposed research will be guided by learning theory (Bandura, 1986), and the socio-cultural experiences of students (Aronson et al., 2002; Steele, 2010).