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|title=None
|pdfUrl=https://ceur-ws.org/Vol-2387/20190525.pdf
|volume=Vol-2387
|dblpUrl=https://dblp.org/rec/conf/icteri/VaginHBC19
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https://ceur-ws.org/Vol-2387/20190525.pdf
Computer Intelligent Tutoring System “SQLTOR”
Ievgen Vagin1, Olena Havrylenko1, Juan Pablo Martínez Bastida1, Andrey Chukhray1
1 National Aerospace University, KhAI, Kharkiv, Ukraine
{ie.s.vagin, lm77191220, jpbastida, achukhray}@gmail.com
Abstract. Intelligent tutoring systems are required in different spheres, especially
in IT. The presented system “SQLTOR” provides supporting tools for teachers
and an adaptive tutoring approach for SQL students as well. Tutoring course in
SQLTOR consists of task sequences ordered by complexity. Clustering and or-
dering are automatically performed based on student’s degree of mastery of the
relevant knowledge components in the learning domain. Thus, course structure
allows gradually increase or decrease tasks complexity during tutoring process.
SQLTOR provides hints which depend on learner's mistakes and the task content.
Hints are automatically generated based on comparison of a student’s SQL query
with the referred one or are manually customized. The structure of SQLTOR,
task grouping (clustering), ordering methods, SQLTOR tutoring modes and its
behavior when a student makes mistakes are also described in this paper. As a
conclusion, testing results from a group of students at National Aerospace Uni-
versity “Kharkiv Aviation Institute” are provided.
Keywords: Intelligent Tutoring System authoring tool, SQL computer learn-
ing, clustering, competence components.
1 Introduction
Implementation of computer tutoring programs (CTP) is one of the highest priority di-
rections in educational tools evolution. This fact is reasoned by numerous advantages
of CTP usage over the classical approach: adoption for a particular student, wide pos-
sibilities of virtual modeling of real objects and processes, decrease in time and work
efforts for completion, verification of tutoring courses, e-learning facilities, etc.
Intelligent Tutoring Systems (ITS) are characterized by supporting inner and outer
tutoring loops [1], minimal feedback (prompting hints and advices), nonlinear learning
path, dynamic and customizable knowledge base, and self-learning support [1, 2]. ITS
usually include three main structural elements: a domain model, a student model and a
pedagogical model [2, 3], but researchers also often incorporate an interface model as
the fourth element [3]. Special software is usually developed to overcome ITS devel-
opment difficulties and it is commonly called as ITS Authoring Tools [4]. Users of ITS
Authoring Tools need only basic knowledge in computing and minimal programming
skills. Examples of ITS Authoring Tools are: DIAG, RIDES, SIMQUEST, XAIDA,
Demonstr8, D3, TRAINER, ASPIRE, GTE, REDEEM, Eon, Interbook, MetaLinks,
CALAT, CTAT [3-5], etc. An actual problem in ITS development is a problem of clus-
tering tutoring tasks. Clustering of tasks provides additional possibilities for the peda-
gogical model to select a next task according to the gradual difficulty incremental prin-
ciple. A further problem is to provide adaptive hints in the context of certain subject
area. A hint must be generated according to the place and type of student’s mistake and
assumes possibility of alpha and beta errors.
2 Research Purposes
ITS Authoring Tools must be an easy-for-using tool to simplify ITS development pro-
cess. SQLTOR is a computer software designed with a “thick” architecture that allows
building a distributed system by using existed data transfer protocols without requiring
synchronizing system database between different computers.
Common structural elements of ITS are discussed in [3], they utilize specific ele-
ments of knowledge – Knowledge Components (KCs) [1, 5]. In the SQL tutoring do-
main, solutions are usually represented as queries. A query is registered as a solution
of related tasks by applying one or more KCs. An example of the possible domain
model structure is represented on Fig. 1.
Fig. 1. Structure of the tutoring course in SQLTOR
Clusters on Fig. 1, are used to define groups of similar tasks. Similarity of tasks can be
estimated from the intersection between KCs sets. KCs sets can be allocated by etalon
queries registered as solutions for a particular task. Complexity can be decreased by
means of automated clustering. General ITS development stages are shown on Fig. 2.
Available author actions are represented by rectangles, arrows shows order of actions
and rounded rectangles sign conditions for actions.
Student knowledge can be represented as a set of value pairs {KCi , lKCi } , where
KCi – knowledge component and l KCi – mastering degree estimation of KCi . Values
of l KCi can be changed in accordance with the student success. Correct answers cause
increment of the corresponding l KCi values and incorrect ones cause a decrement.
Fig. 2. Common development stages of ITS in SQLTOR authoring tool
3 Clustering of Tasks
All data required by SQLTOR is stored in two databases. The first of them is used for
executing user queries and retrieve resulting rows to compare with the expected results.
Second database is used to store data required for making tutoring decisions depicted
on Fig. 3.
Fig. 3. ER-diagram of SQLTOR system database
Clustering algorithm in SQLTOR uses a specific method of f D calculation that can be
used for estimation of Qi and C j dissimilarity. Hamming distance can be used for
such purpose: hij xic x jc , where is a binary XOR operation. Queries that
have equal similarities with two or more centroids will be set into a cluster with less
amount of etalons at the current algorithm iteration. Let specify distance calculation
as follows:
Dij f D Qi , C j h ij Qi , C j j cn1 x ic y jc j , (1)
where h ij is a function for calculating distance between the i th query and j th centroid;
| j | is the number of queries in the j th cluster; | | is total number of queries. Cor-
rection | j | / | {Qi } | is required to provide even distribution of queries in clusters.
Another peculiarity of SQLTOR clustering algorithm is related to the calculation of
fC ( j ) . Each variable y jc in tuple C*j f C ( j ) , must be true, with a probability pro-
portional to the number of true xic variables included in queries Qi j . We can
obtain frequency of the c th KC inclusion in queries of a j th cluster as:
Fjc 1 j x Q x ic .
ic i j
(2)
Sum of xic is the number of true variables x ic Qi j . Variable y jc is initialized
by “1”, if frequency Fjc is greater than certain value of threshold frequency F . Oth-
erwise, y jc must be initialized by “0” when Fjc F . Thus, we can perform calculation
of C*j as follows:
C*j fC j fC* x i1 ,..., fC* x in ,
1, if Fjc F, (3)
fC* x ic x ic Qi j .
0, if Fjc F,
4 Task Ordering in the Outer Loop
Outer tutoring loop is responsible for selecting a next task in the educational flow.
SQLTOR manages tasks in order to provide a more effective scenario for the passing
course. SQLTOR can only use complexities for KCs, complexities of queries and tasks
can be automatically calculated. Complexity of etalon query Qi is calculated as fol-
lows:
PQi c1 Pxic , xic Qi ,
n
(4)
where PQi is complexity of query Qi ; Px ic is complexity of c th KC (if c th KC is not
in relation with query Qi , then Px ic 0 ). Complexity of tasks is defined as maximal
complexity of queries that are associated with a task:
PT max QiT PQi , (5)
where PT is complexity of the task T ; PQi is complexity of query Qi , which is one of
the solutions for task T . Average complexity of etalon queries is also calculated in the
same cluster:
PC j 1 N j i PQi , Qi j . (6)
5 Tutoring Method Description
SQLTOR analyzes student’s solutions by means of Abstract Syntax Trees (AST),
which can be built based on a SQL query syntax structure. This approach gives possi-
bility to avoid α and β-errors in student’s solutions analysis and automatically generate
hints. AST is a tree whose leaf nodes are operands, and other nodes are operators. In
order to construct the AST for a particular SQL query, let us assume language key-
words, such as SELECT, FROM, and other as operators and consider the construction
of an AST of SQL language for the following query example:
SELECT name, salary FROM employees
WHERE salary>1000 ORDER BY salary DESC
The root of the AST is a synthetic node and the analysis of the query string is made
from the beginning to the end, therefore, first keyword is SELECT. Column names in
the result will be the child nodes, similarly to SELECT and the operator FROM. Besides
the WHERE clause has a sub-operator ">", which will be the children of the node
WHERE, and its operands that will respectively be children of the ">" node. ORDER
BY clause is processed like other sections, except the keyword DESC. Node corre-
sponding to DESC keyword will be the leaf one. AST of etalon query is provided on
Fig. 4. We consider one of the possible situations when student’s solution is:
SELECT name, salary FROM employees ORDER BY salary DESC
Fig. 4. Etalon query AST constructing
Nodes at current level hi are compared pairwise in consideration of parent node at
each iteration of the algorithm. In the case of inequality of nodes, student AST is con-
sidered as wrong. This search strategy is the most effective, because general query com-
ponents are at high levels in the AST structure.
6 Conclusions
SQLTOR tests were performed on a group of 29 students. Tutoring course contained
47 tasks with 67 etalons. This list was extended by 25 new etalons after the testing
process. New etalons were found from 9 students. All 29 students fully completed the
course and solved the most complex tasks in each cluster. Testing outcomes exceed
input testing results by 27% average. 16 of 25 students made mistakes in each task, but
during output test, each student correctly solved at least two tasks. This indicates effec-
tiveness of SQLTOR as a tool for supporting educational process on learning SQL pro-
gramming language. Moreover, it provides the necessary creational tools for teachers
of SQL courses. In addition, software provides ITS features to help students in acquir-
ing SQL knowledge. It can be used for supporting learning process as well as self-
learning and self-testing. System uses PostgreSQL database and is written in Java.
SQLTOR proposes to simplify work for authoring a tutoring course, it also includes
several tools and features of tutoring tasks clustering, AST comparison algorithms of
user and etalon solutions, represents knowledge as a set of knowledge components,
supports sorting of tasks, etalons and clusters to provide a soft increment/decrement of
the tutoring complexity.
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