The Behavior of Tutoring Systems

By Kurt VanLehn. International Journal of Artificial Intelligence in Education, 2006

905 citations recorded by the source corpus.

Tutoring systems are described as having two loops. The outer loop executes once for each task, where a task usually consists of solving a complex, multi-step problem. The inner loop executes once for each step taken by the student in the solution of a task. The inner loop can give feedback and hints on each step. The inner loop can also assess the student's evolving competence and update a student model, which is used by the outer loop to select a next task that is appropriate for the student. For those who know little about tutoring systems, this description is meant as a demystifying introduction. For tutoring system experts, this description illustrates that although tutoring systems differ widely in their task domains, user interfaces, software structures, knowledge bases, etc., their behaviors are in fact quite similar.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/3fe141759f8e4bbb3c971adc99b81037bc03c900

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Science Applications

Citations in the corpus

Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC (2015, background): “Intelligent tutoring systems provide such support through adaptive feedback and hints during learning by doing [1, 2, 34].”
Pedagogical Agents: Back to the Future (2018, positive): “In a development that surprised many in education, intelligent tutoring systems have become almost as effective as human tutors in one-on-one tutoring for STEM domains (VanLehn 2011).”
The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems (2011, background): “The first type of ITS will be called a step-based tutoring system because it is engineered to let users enter the steps that they would do when solving problems normally, without the tutoring (VanLehn, 2006).”
A Systematic Literature Review of Automated Feedback Generation for Programming Exercises (2018, background): “ITSs help students to arrive at the solution by offering help at each step [181].”
A meta-analysis of the effectiveness of intelligent tutoring systems on college students' academic learning (2014, neutral): “In this regard, CAI, CBT, and Web-based homework are different from ITS in that they lack an inner loop (VanLehn, 2006).”
New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization (2013, background): “Intelligent tutoring systems both guide students through a curriculum of instructional activities in an outer loop and monitor step-by-step progress on an activity within an inner loop (VanLehn 2006).”
A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors (2009, methodology): “We already discussed the GUI Builder, Student Interface, Behavior Recorder, and Example Tracer, but have not yet fully discussed the following components: • The Tutor Engine takes care of a tutor’s inner loop (VanLehn, 2006), that is, it provides step-by-step guidance within a given problem.”
Handbook of Research on Learning and Instruction (2011, background): “Thus, children were taught to move based on elite, adult movement models rather than developmentally appropriate patterns such as those that Roberton (1982) described.”
Effectiveness of Intelligent Tutoring Systems (2016, background): “VanLehn (2006) has noted that ITSs today come in different shapes, sizes, and designs, but whatever their structures, they all share common behavioral characteristics.”