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Preface-Emerging Technologies and Landmark Systems for Learning Mathematics and Science: Dedicated to the Memory of Erica Melis-Part 2

Year: 2014

Type: editorial

Abstract: This issue of the International Journal of Artificial Intelligence in Education is the second of two special issues dedicated to Erica Melis, a close colleague of ours and an important contributor to the research field of Artificial Intelligence in Education (AIED) who passed away in the beginning of 2011.Given Erica's dedication to mathematics and science-and the work she did in developing intelligent tutors in the area of mathematics-it is only fitting that these volumes be dedicated to her memory.In the preface to the prior issue, we provided an overview of Erica's life and research and introduced papers on emerging educational technologies (McLaren et al. 2014).In the current special issue, we present papers on more established AIED systems, those that have become landmarks in the field.Since the very beginning of AIED research, mathematics and sciences have been among the most popular application domains for developing AI-based technologies intended to improve learning.Many successful intelligent and adaptive systems for learning and teaching mathematical and scientific subjects have been created over the years, (e.g.(Andersonet al. 1995;Melis et al. 2001;Vanlehn et al. 2005)).These domains have been unique and important for AIED researchers and educators in general.There has always been a great demand for teaching and instructional support in these domains.Therefore, they have always provided many opportunities to apply and evaluate a wide range of AIED approaches.From a pedagogical perspective, mathematics and science are challenging domains to learn and teach.They rely on formal concepts whose meaning is not always intuitive and require mastering a complex skill set for applying, manipulating, communicating and modeling mathematical and scientific notions and notations.Students often come to math and science classes equipped with different background knowledge,

Source: International Journal of Artificial Intelligence in Education

DOI: 10.1007/s40593-014-0025-9

Authors: Sergey Sosnovsky, Bruce M. McLaren, Vincent Aleven

Institutions: German Research Centre for Artificial Intelligence, Carnegie Mellon University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $3,190