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Learning SQL with a Computerized Tutor (Centered on SQL-Tutor)

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.

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Learning SQL with a Computerized Tutor (Centered on SQL-Tutor)

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

  2. Agenda • Problem regarding to learning SQL • Purpose of SQL-Tutor System • Architecture of SQL-Tutor • Evaluation of SQL-Tutor

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. System Demo • http://ictg.cosc.canterbury.ac.nz:8000/sql-tutor/login

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

  10. 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

  11. 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

  12. 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)

  13. Results of subjective evaluation

  14. 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.

  15. Evaluation results of learning effects

  16. Result of first experiment

  17. Kinds of feedback • Positive/negative feedback • Error flag • Hint • All errors • Partial solution • Complete solution

  18. Result of second experiment (contd.)

  19. 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.

  20. Thank you

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