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Reverse SQL question generation algorithm in the dblearn adaptive e-learning system
(2019-01-01)
Atchariyachanvanich, Kanokwan
;
Nalintippayawong, Srinual
;
Julavanich, Thanakrit
Using a traditional e-learning system, when teaching structured query language (SQL) queries in classical classrooms help instructors, to improve the students' SQL skills and learning effectiveness. However several problems in using e-learning as a teaching and learning assistant remain-such as difficulties in differences in learning ability and knowledge level. We solved these problems by applying an adaptation module to our e-learning system. However, we still found it required considerable effort to create enough exercises to make the adaptation effective enough. So, we developed a novel automatic question generating algorithm, named Reverse SQL Question Generation Algorithm (RSQLG), to automatically generate exercises (including both answer and question) from a source database. RSQLG reverses the traditional manual process used previously by instructors. Instead of creating questions and answers for them, RSQLG creates the answers first. The generated exercises are presented to students by applying question adaptation methodology based on student knowledge level in each supported learning objective. We evaluated the learning effectiveness of our approach by using outcome-based learning. After post-Test to pre-Test scores were compared, we found students using our system improved their scores by 26%. Consequently, the adaptive e-learning framework using RSQLG could be applied in any adaptive or traditional e-learning for a database course to benefit the instructors leading to less effort in exercise management and to improve the learning outcome from the students allowing as much practice as they need.
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DBLearn: Adaptive e-learning for practical database course - An integrated architecture approach
(2017-08-29)
Nalintippayawong, Srinual
;
Atchariyachanvanich, Kanokwan
;
Julavanich, Thanakrit
In this paper, an integrated architecture approach in designing and developing a DBLearn web-based application is presented. The DBLearn system is a personalized and adaptive e-learning system designed especially for learning practices in database courses. This approach focused on topics that are important but difficult for new learners, such as database design and structured query language (SQL) command query. The concept of adaptive e-learning and autonomous agents were applied in this system to eliminate the traditional constraints of effective e-learning, such as the problem of different learning sensory and knowledge levels. Four approaches were used to solve this problem. First, learning style theory was used to classify the way of learning for each student. Second, the student activity (historical data) is kept in the system to analyze the next knowledge the student should learn or review. Next, the SQL query automated grader was used to judge the correctness of the student's query. This grader supports all the necessary commands in both DML and DDL. Finally, the SQL query question generator module that can generate SQL query questions automatically is presented. This will reduce the instructor's work load in creating enough questions and allow the students to practice at their own pace as much as they want. By using these four techniques, the students will have a better learning experience and becoming more successful in learning outcomes.

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