Constraint-based Tutors Research Articles

Using a constraint-based expert model to provide step-level feedback for user-inputted mathematics equations

Studies across a variety of educational fields have shown the efficacy of feedback on student performance and learning. Web-based homework is a common feature of secondary and collegiate mathematics courses to provide such feedback. While web-based homework provides often instantaneous feedback to students as they complete assignments, the feedback is limited to the correctness of the final answer or to a limited number of pre-programmed common mistakes. Intelligent Tutoring Systems have the ability to provide step-by-step feedback and guidance tailored to individual students, however authoring content for such systems is generally considered a huge barrier to their wider use. This paper presents an alternative system, called SANYMS (Show and Name Your Math Steps), which combines the wide-scale usability of web-based homework with a Constraint-Based Intelligent Tutoring System Expert Model based on the open-source computer algebra system Maxima. With the integration of Maxima, SANYMS can provide step-by-step feedback to students constructing their own solutions to a wide variety of algebraic equations of their choice without the need for extensive content authoring inherent in many Intelligent Tutoring Systems.

Data calibration for statistical-based assessment in constraint-based tutors

Intelligent Tutoring Systems (ITSs) are one of a wide range of learning environments, where the main activity is problem solving. One of the most successful approaches for implementing ITSs is Constraint-Based Modeling (CBM). Constraint-based tutors have been successfully used as drill-and-practice environments for learning. More recently CBM tutors have been complemented with a model derived from the field of Psychometrics. The goal of this synergy is to provide CBM tutors with a data-driven and sound mechanism of assessment, which mainly consists in applying the principles of Item Response Theory (IRT). The result of this synergy is, therefore, a formal approach that allows carrying out assessments of performance on problem solving tasks. Several previous studies were conducted proving the validity and utility of this combined approach with small groups of students, in short periods of time and using systems designed specifically for assessment purposes. In this paper, the approach has been extended and adapted to deal with a large set of students who used an ITS over a long period of time. The main research questions addressed in this paper are: (1) Which IRT models are more suitable to be used in a constrained-based tutor? (2) Can data collected from the ITS be used as a source for calibrating the constraints characteristic curves? (3) Which is the best strategy to assemble data for calibration? To answer these questions, we have analyzed three years of data from SQL-Tutor.

Teaching Database Design with Constraint-Based Tutors

Design tasks are difficult to teach, due to large, unstructured solution spaces, underspecified problems, non-existent problem solving algorithms and stopping criteria. In this paper, we comment on our approach to develop KERMIT, a constraint-based tutor that taught database design. In later work, we re-implemented KERMIT as EER-Tutor, and extended its instructional domain. Several evaluation studies performed with KERMIT and EER-Tutor show that they are effective Intelligent Tutoring Systems (ITSs). We also comment on various extensions made to EER-Tutor over the years. There are several contributions of our research, such as developing effective problem-solving support for conceptual database design in terms of interface design. Our database design tutors deal with large solution spaces efficiently by specifying constraints that capture equivalent solution states, and using ideal solutions to capture the semantics of the problem. Instead of requiring a problem solver, the ITS checks whether the student’s database schema is correct by matching it to constraints and the ideal solution. Another contribution of our work is in guidelines for developing effective feedback to the student.

Implementing CBM: SQL-Tutor After Fifteen Years

SQL-Tutor is the first constraint-based tutor. The initial conference papers about the system were published in 1998 (Mitrovic 1998a, 1998b, 1998c), with an IJAIED paper published in 1999 (Mitrovic and Ohlsson, International Journal Artificial Intelligence in Education, 10(3–4), 238–256, 1999). We published another IJAIED paper in 2003, focussed on the Web-enabled version of the same system (Mitrovic, Artificial Intelligence in Education, 13(2–4), 173–197, 2003). In this paper, we discuss the reasons for developing the system, our experiences with the early versions, and also provide a history of later projects involving SQL-Tutor.

The effect of positive feedback in a constraint-based intelligent tutoring system

Tutoring technologies for supporting learning from errors via negative feedback are highly developed and have proven their worth in empirical evaluations. However, observations of empirical tutoring dialogs highlight the importance of positive feedback in the practice of expert tutoring. We hypothesize that positive feedback works by reducing student uncertainty about tentative but correct problem solving steps. Positive feedback should communicate three pieces of explanatory information: (a) those features of the situation that made the action the correct one, both in general terms and with reference to the specifics of the problem state; (b) the description of the action at a conceptual level and (c) the important aspect of the change in the problem state brought about by the action. We describe how a positive feedback capability was implemented in a mature, constraint-based tutoring system, SQL-Tutor, which teaches by helping students learn from their errors. Empirical evaluation shows that students who were interacting with the augmented version of SQL-Tutor learned at twice the speed as the students who interacted with the standard, error feedback only, version. We compare our approach with some alternative techniques to provide positive feedback in intelligent tutoring systems.

Fifteen years of constraint-based tutors: what we have achieved and where we are going

Fifteen years ago, research started on SQL-Tutor, the first constraint-based tutor. The initial efforts were focused on evaluating Constraint-Based Modeling (CBM), its effectiveness and applicability to various instructional domains. Since then, we extended CBM in a number of ways, and developed many constraint-based tutors. Our tutors teach both well- and ill-defined domains and tasks, and deal with domain- and meta-level skills. We have supported mainly individual learning, but also the acquisition of collaborative skills. Authoring support for constraint-based tutors is now available, as well as mature, well-tested deployment environments. Our current research focuses on building affect-sensitive and motivational tutors. Over the period of fifteen years, CBM has progressed from a theoretical idea to a mature, reliable and effective methodology for developing effective tutors.

ASPIRE: An Authoring System and Deployment Environment for Constraint-Based Tutors

ASPIRE: An Authoring System and Deployment Environment for Constraint-Based Tutors

Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams

We present COLLECT-UML, a constraint-based intelligent tutoring system (ITS) that teaches object-oriented analysis and design using Unified Modelling Language (UML). UML is easily the most popular object-oriented modelling technology in current practice. While teaching how to design UML class diagrams, COLLECT-UML also provides feedback on collaboration. Being one of constraint-based tutors, COLLECT-UML represents the domain knowledge as a set of constraints. However, it is the first system to also represent a higher-level skill such as collaboration using the same formalism. We started by developing a single-user ITS that supported students in learning UML class diagrams. The system was evaluated in a real classroom, and the results showed that students’ performance increased significantly. In this paper, we present our experiences in extending the system to provide support for collaboration as well as domain-level support. We describe the architecture, interface and support for collaboration in the new, multi-user system. The effectiveness of the system has been evaluated in two studies. In addition to improved problem-solving skills, the participants both acquired declarative knowledge about effective collaboration and did collaborate more effectively. The participants have enjoyed working with the system and found it a valuable asset to their learning.

Intelligent Tutors for All: The Constraint-Based Approach

This paper presents a new type of intelligent tutoring systems, called constraint-based tutors. The system have been thoroughly evaluated and proven to achieve significant learning gains.

Evaluating the Effect of Open Student Models on Self-Assessment

This paper discusses the simple open student models used in two of our constraint-based tutors, SQL-Tutor and KERMIT, and their effects on self-assessment. The systems present a high-level abstract...

Scaffolding answer explanation in a data normalization tutor

Self-explanation is one of the most effective learning strategies, resulting in deep knowledge. In this paper, we discuss how NORMIT supports self-explanation. NORMIT is a constraint-based tutor that teaches data normalization. We present the system first, and then discuss how it supports self-explanation. We hypothesized the self-explanation support in NORMIT would result in increased problem solving skills and better conceptual knowledge. An evaluation study of the system was performed, the results of which show that both problem-solving performance and the understanding of the domain of students who self-explained increased. We also discuss our plans for future research.