Intelligent tutoring system

About: Intelligent tutoring system is a research topic. Over the lifetime, 3472 publications have been published within this topic receiving 58217 citations. The topic is also known as: ITS.

A Comparison of Learning Environments: All That Glitters

Abstract: : Aptitude-treatment interactions (ATI) refer to the covariation between learner characteristic and instructional treatment in relation to some outcome measure To systematically test for ATI, I used an intelligent tutoring system instructing basic principles of electricity as a complex but controlled learning task I created two instructional environments from this one tutor, differing only in feedback In the rule-application environment, the system provided learners with relevant principles, and in the rule-induction environment, learners had to induce principles on their own The learner characteristic examined in this paper was 'exploratory behavior,' a measure of on-line tool usage I hypothesized that exploratory learners would learn faster and better if they had been assigned to the inductive environment and less exploratory learners would benefit from the more structured, application environment Results showed significant aptitude-treatment interactions in the data, confirming the above hypotheses Implications of these findings are discussed in relation to the design of intelligent tutoring systems

A feasibility study of using automated program repair for introductory programming assignments

Abstract: Despite the fact an intelligent tutoring system for programming (ITSP) education has long attracted interest, its widespread use has been hindered by the difficulty of generating personalized feedback automatically. Meanwhile, automated program repair (APR) is an emerging new technology that automatically fixes software bugs, and it has been shown that APR can fix the bugs of large real-world software. In this paper, we study the feasibility of marrying intelligent programming tutoring and APR. We perform our feasibility study with four state-of-the-art APR tools (GenProg, AE, Angelix, and Prophet), and 661 programs written by the students taking an introductory programming course. We found that when APR tools are used out of the box, only about 30% of the programs in our dataset are repaired. This low repair rate is largely due to the student programs often being significantly incorrect - in contrast, professional software for which APR was successfully applied typically fails only a small portion of tests. To bridge this gap, we adopt in APR a new repair policy akin to the hint generation policy employed in the existing ITSP. This new repair policy admits partial repairs that address part of failing tests, which results in 84% improvement of repair rate. We also performed a user study with 263 novice students and 37 graders, and identified an understudied problem; while novice students do not seem to know how to effectively make use of generated repairs as hints, the graders do seem to gain benefits from repairs.

Teaching the tacit knowledge of programming to noviceswith natural language tutoring

Abstract: For beginning programmers, inadequate problem solving and planning skills are among the most salient of their weaknesses. In this paper, we test the efficacy of natural language tutoring to teach and scaffold acquisition of these skills. We describe ProPL (Pro-PELL), a dialogue-based intelligent tutoring system that elicits goal decompositions and program plans from students in natural language. The system uses a variety of tutoring tactics that leverage students' intuitive understandings of the problem, how it might be solved, and the underlying concepts of programming. We report the results of a small-scale evaluation comparing students who used ProPL with a control group who read the same content. Our primary findings are that students who received tutoring from ProPL seem to have developed an improved ability to solve the composition problem and displayed behaviors that suggest they were able to think at greater levels of abstraction than students in the read-only group.

An Intelligent Tutoring System Incorporating a Model of an Experienced Human Tutor

Abstract: Symbolization is the ability to translate a real world situation into the language of algebra. We believe that symbolization is the single most important skill students learn in high school algebra. We present research on what makes this skill difficult and report the discovery of a "hidden" skill in symbolization. Contrary to past research that has emphasized that symbolization is difficult due to both comprehension difficulties and the abstract nature of variables, we found that symbolization is difficult because it is the articulation in the "foreign" language of "algebra". We also present Ms. Lindquist, an Intelligent Tutoring System (ITS) designed to carry on a tutorial dialog about symbolization. Ms. Lindquist has a separate tutorial model encoding pedagogical content knowledge in the form of different tutorial strategies, which were partially developed by observing an experienced human tutor. We discuss aspects of this human tutor's method that can be modeled well by Ms. Lindquist. Finally, we present an early formative showing that students can learn from the dialogs Ms. Lindquist is able to engage student in. Ms. Lindquist has tutored over 600 students at www.AlgebraTutor.org.

Guru: a computer tutor that models expert human tutors

Abstract: We present Guru, an intelligent tutoring system for high school biology that has conversations with students, gestures and points to virtual instructional materials, and presents exercises for extended practice. Guru's instructional strategies are modeled after expert tutors and focus on brief interactive lectures followed by rounds of scaffolding as well as summarizing, concept mapping, and Cloze tasks. This paper describes the Guru session and presents learning outcomes from an in-school study comparing Guru, human tutoring, and classroom instruction. Results indicated significant learning gains for students in the Guru and human tutoring conditions compared to classroom controls.