Learning SQL with a Computerized Tutor (Centered on SQL-Tutor) Antonija Mitrovic (University of Canterbury) Presented by Danielle H. Lee

Agenda  Problem regarding to learning SQL  Purpose of SQL-Tutor System  Architecture of SQL-Tutor  Evaluation of SQL-Tutor

Problem regarding to learning SQL  Burden of having to memorize database schemas (incorrect table or attribute names)  Misconceptions in student’s understanding of the elements of SQL and the relational data model in general  Not easy to learn SQL directly by working with a DBMS Inadequacy of feedback from a RDBMS  Example (in Ingres): E_USOB63 line 1, the columns in the SELECT clause must be contained in the GROUP BY clause. Inability of a RDBMS to deal with semantic errors

Research By the Univ. of Canterbury  DatabasePlace Web portal for database related lectures. SQL-tutor: teaches the SQL database query language NORMIT: data normalization tutor ER-tutor: teaches database design using the Entity-Relationship data model Constraint-based tutors

Automated Tutoring System  The School of Computing, Dublin City University  Developed for an online course name ‘the introduction to databases’  To Provide a certain level advice and guide by using feedback, assessment, and personalized guidance  Limited the contents to the SQL SELECT sentence. The most fundamental of the SQL Simple but having the capacity to become quite complex  There are correction model and pedagogical model. Correction model: Multi-level error categorization scheme according to three aspects (from, where, select) Pedagogical model: analyses the information stored by the student’s answers, it provides feedback, assessment, and guidance

Purpose of Project  Personalized ITS for Database Courses Personalized tutoring system for learning SQL To adapt SQL-tutor technology for use with a different audience and to explore some ways to maximize the educational value for every student. Exploration of personalized guidance technology based on the ideas of adaptive hypermedia

Purpose of SQL-Tutor system  To explore and extend constraint based modeling  Problem-solving environment intended to complement classroom instruction.  Problem sets with nine levels of complexity defined by a human expert  Students have a assigned educational level and the level is updated by observing the student’s behavior. Novice, intermediate, or experienced

System Demo  tutor/login

Architecture of SQL-Tutor Student Modules Constraints Databases, Problems, Solutions CBM Pedagogical module Interface Student

Constraint-based model (contd.)  Ohlsson’s theory of learning from errors (1996) Error recognition Error correction  Conceptual domain knowledge is represented in terms of over 500 constraints  Constraints define equivalence classes of problem states  Equivalence class triggers the same instructional action  A student’s solution is matched to constraints to identify any that are violated.  Neutral with respect to the pedagogy and knowledge domain

Constraint-based model  Example: specifying the SELECT clause of a SQL query cannot be empty (p 2 “The SELECT clause is a mandatory one. Specify the attributes/expressions to retrieve from the database.” (not (null (select-clause ss))) “SELECT”) Unique No. Instructional Message Part of the constraint

Evaluation  Computer Science students, Univ. of Canterbury  Three experiments for evaluation First (April 1998): to evaluation how well CBM supports student learning and to evaluate the interface and constraint base of SQL-Tutor  Subject No: 20 Second (May 1999): to evaluate the effectiveness of various types of feedback in the system  Subject No: 33 Third (October 1999): to evaluate the advanced pedagogical agent (no explanation)

Results of subjective evaluation

Mastery of constraints  The degree of mastery of a given constraint is a function of the amount of practice on that constraint  Measured the number of occasions relevant to each constraint and calculate the probability of violating a given constraint.

Evaluation results of learning effects

Result of first experiment GroupMeanStd Dev. Experimental Control Total

Kinds of feedback  Positive/negative feedback  Error flag  Hint  All errors  Partial solution  Complete solution

Result of second experiment (contd.)

Result of second experiment  CBM-based general feedback is superior to offering a correct solution.  Among six feedbacks, the initial learning rate is highest for all errors (0.44) and error flag (0.40), closely followed by positive/negative (0.29) and hint (0.26). The learning rate for partial (0.15) and full solution (0.13) are low.

Thank you