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<p>The exercise is one of the educational activities that help students to understand and achieve their learning goals. Quizizz is a great online tool which helps students to check their knowledge and learning progress. In this paper, the researcher applied the SQL (Structured Query Language) skill in data definition language (DDL) and data manipulation language (DML) exercises and engaged the student’s learning by using Quizizz on students’ Introduction to Database course. The exercises and Quizizz were employed to an experimental group and a conventional teaching method was used for the control groups. The groups of students were of the heterogeneous level. Using the DDL and DML exercises, the students can review the knowledge repeatedly by doing exercises. In addition, the student can learn something from doing quizzes via Quizizz. The study on SQL exercises were conducted in order to improve students' achievement in DDL and DML. The purposes of this research were to 1) compare the students’ pre-test and post-test achievement using Quizizz and 2) investigate the students’ satisfaction while using SQL language skill exercises and Quizizz. The sample consisted of 34 students who enrolled in an Introduction to Database course in semester 1 of the academic year 2017. The students applied SQL language skill exercises when working on SQL assignment statement syntax. The students' pre-tests and post-tests were assessed. Percentage, mean and standard deviation, and average score (t-test) were used to analyze the data. The result showed that the students’ scores from the post-test was higher than the pre-test. There was a statistical significance at the level of 0.05. According to the result, it indicated that the students’ achievement was improved by the implementation. The students' satisfaction with the SQL language skill exercises and Quizizz was at a highest level (X = 4.58, S.D. = 0.54).</p>

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Chatbots have the potential to be used as motivational learning tools, particularly for boosting academic performance. The purpose of this study is to construct a Facebook Messenger chatbot to promote accomplishment through the use of blended learning, guided by the ARCS (attention, relevance, confidence, satisfaction) motivation model that compares how engagement works, and explores the chatbot in terms of its usability. Integrated with Facebook Messenger, chatbot software was designed to answer inquiries based on the chatbot's communication framework. This included course alerts, a gradebook for each student, attendance statistics, and assignment feedback. Using a quasi-experimental research approach, the influence of the chatbot on student motivation and academic achievement was empirically investigated. The trial covered 18 weeks, and the sample comprised 48 students enrolled in a course on Information Technology for Learning. The results suggest that the chatbot increased the learning accomplishment of the students to a considerable extent, and that a motivational setting may lead to a better outcome than a blended learning environment. Overall, our approach produced reliable findings which validated the chatbot's capacity to communicate with students. The students agreed that the chatbot facilitated their learning, but that a few modifications were required in terms of ongoing development.   Keywords: Motivation; Learning Achievement; Chatbot; Blended Learning

Discovery of association rule is one of the most interesting areas of research in data mining, which extracts together occurrence of itemset. In a dynamic database where the new transaction are inserted into the database, keeping patterns up-to-date and discovering new pattern are challenging problems of great practical importance. This may introduce new association rules and some existing association rules would become invalid. It is important to study efficient algorithms for incremental update of association rules in large databases. In this paper, we modify an existing incremental algorithm, Probability-based incremental association rule discovery. The previous algorithm, probability-based incremental association rule discovery algorithm uses principle of Bernoulli trials to find frequent and expected frequent k-itemsets. The set of frequent and expected frequent k-itemsets are determined from a candidate k-itemsets. Generating and testing the set of candidate is a time-consuming step in the algorithm. To reduce the number of candidates 2-itemset that need to repeatedly scan the database and check a large set of candidate, our paper is utilizing a hash technique for the generation of the candidate 2-itemset, especially for the frequent and expected frequent 2-itemsets, to improve the performance of probability-based algorithm. Thus, the algorithm can reduce not only a number of times to scan an original database but also the number of candidate itemsets to generate frequent and expected frequent 2 itemsets. As a result, the algorithm has execution time faster than the previous methods. This paper also conducts simulation experiments to show the performance of the proposed algorithm. The simulation results show that the proposed algorithm has a good performance.