Work in Progress: Intelligent Tutoring Systems in Computer Science and Software Engineering Education

Abstract

where he conducts research on argumentation and learning, multimedia learning, self-regulated learning and the effectiveness of intelligent tutoring systems. He has collaborated with colleagues to publish over 60 peerreviewed journal articles, book chapters and indexed conference papers. Li Liu, Simon Fraser UniversityLi Liu is a second year doctoral student in Educational Technology and Learning Design at Simon Fraser University. With an interdisciplinary background in interaction design, media arts and education, her passion lies in exploring how innovative technologies can be harnessed to promote teaching and learning. Qing Liu, Simon Fraser UniversityQing Liu is a doctoral student in Educational Technology and Learning Design at Simon Fraser University. Her research focuses on conceptual change, the potential of learning by arguing, the role of need for cognition in learning, the effectiveness of intelligent tutoring systems, and meta-analysis of empirical studies. Dr. Olusola O Adesope, Washington State University-PullmanDr. Olusola O. Adesope is an Assistant Professor of Educational Psychology at Washington State University, Pullman. His research is at the intersection of educational psychology, learning sciences, and instructional design and technology. His recent research focuses on the cognitive and pedagogical underpinnings of learning with computer-based multimedia resources; knowledge representation through interactive concept maps; meta-analysis of empirical research, and investigation of instructional principles and assessments in STEM. AbstractMany research reports have been published over the last 30 years on the use of intelligent tutoring systems in computer science and software engineering education, but no previous systematic review has been conducted to describe and assess the field as a whole. This project (in progress) searched for publications meeting defined inclusion criteria and identified 280 eligible reports. We are currently coding these works using 28 variables that will allow us to describe the research field in aggregate. The results will tell us: What research questions are being asked? What are the types of student modeling being used? What subject domains have ITS been designed for? What issues or themes are most evident in recent research? What are the gaps in research on intelligent tutoring systems in computer science and software engineering education. Finally, what technological and pedagogical innovations are needed to advance research in this field?

A Neural Network Model for a Tutoring Companion Supporting Students in a Programming with Java Course

“…Hints. Hints are messages to assist with solving the problem (Nesbit, Liu, et al, 2015). Since the goal is that this hint replicates the assistance of a human tutor, the hints must adapt to the student's progress and work (Crow, Luxton-Reilly, et al, 2018).…”

“…Systems. An Intelligent Tutoring System (ITS) is a computer system that "performs teaching or tutoring functions…and adapts or personalizes those functions by modeling students' cognitive, motivational or emotional states" (Nesbit, Liu, et al, 2015). Student modeling is a primary distinguishing factor of ITS compared to other instructive systems (Nesbit, Liu, et al, 2015;Sottilare, Graesser, et al, 2013), which involves identifying a student's problem in real-time (Shute and Psotka, 1996).…”

“…An Intelligent Tutoring System (ITS) is a computer system that "performs teaching or tutoring functions…and adapts or personalizes those functions by modeling students' cognitive, motivational or emotional states" (Nesbit, Liu, et al, 2015). Student modeling is a primary distinguishing factor of ITS compared to other instructive systems (Nesbit, Liu, et al, 2015;Sottilare, Graesser, et al, 2013), which involves identifying a student's problem in real-time (Shute and Psotka, 1996). Its ability to customize the teaching function by, essentially, "remembering" the states from student responses, in order to provide better feedback in the future to a student in a similar situation is what sets an ITS apart (Nesbit, Liu, et al, 2015;Ma, Adesope, et al, 2014).…”

“…Student modeling is a primary distinguishing factor of ITS compared to other instructive systems (Nesbit, Liu, et al, 2015;Sottilare, Graesser, et al, 2013), which involves identifying a student's problem in real-time (Shute and Psotka, 1996). Its ability to customize the teaching function by, essentially, "remembering" the states from student responses, in order to provide better feedback in the future to a student in a similar situation is what sets an ITS apart (Nesbit, Liu, et al, 2015;Ma, Adesope, et al, 2014). In other words, the knowledge of the tutor should be persistent, continually growing over time (VanLehn, 2006;Pillay, 2003).…”

Towards a Systematic Review of Automated Feedback Generation for Programming Exercises

“…Most reviews describe the features and characteristics of a number of tools, identify challenges, and direct future research. Except for Ihantola et al [10] and the review in progress by Nesbit et al [17], authors select papers and tools using unknown criteria, some mention qualitative factors such as impact (citations) or the thoroughness of the evaluation of the tool. Most studies do not strive for completeness.…”