Database Course Research Articles

AI chatbot: I want help, not the answer!

In recent years, the integration of artificial intelligence (AI) into educational settings has become a topic of great interest. As technology continues to evolve, educators and students are exploring how AI can enhance learning experiences. University of South-Eastern Norway (USN) and Kristiania University College (HK) are developing a web-based educational tool, DbPersist, where IT students can practice tasks within the database subject and receive automated feedback. The latest version now also offers students suggestions on how to utilize AI for learning while they are solving various database related tasks. In this study, we explore the students' experiences with getting automatically generated “prompts” for further use in their preferred AI tool, whenever they request assistance. Do they see the value in gaining experience in using prompts designed for learning? Do they believe it might help them using AI for learning purposes in other courses in their study? To answer these questions, we conducted surveys and interviews. Participants included bachelor students across various disciplines and multiple campuses at USN. The participants were all enrolled in database courses with learning goals involving the database query language SQL and ER modelling (drawing Entity-Relationship Diagrams). Early findings suggest that students appreciate the assistance provided by DbPersist’s AI prompts. They value the emphasis on AI-assisted learning and find the prompts useful for getting assistance in solving difficult tasks as opposed to getting complete answers. They also see the value in gaining experience in using AI chatbots using prompts designed for learning and understanding. As AI continues to shape education, tools like DbPersist offer exciting possibilities. DbPersist exemplifies how AI may be used to empower students, emphasizing learning and understanding alongside correctness.

An enhanced domain ontology model of database course in computing curricula

The ACM/IEEE Computing Curricula 2020 includes the study of relational databases in four of its six disciplines. However, a domain ontology model of multidisciplinary database course does not exist. Therefore, the current study aims to build a domain ontology model for the multidisciplinary database course. The research process comprises three phases: a review of database course contents based on the ACM/IEEE Computing Curricula 2020, a literature review of relevant domain ontology models, and a design research phase using the NeOn methodology framework. The ontology building involves the ontology reuse and reengineering of existing models, along with the construction of some classes from a non-ontological resource. The approach to ontology reuse and reengineering demonstrates ontology reusability. The final domain ontology model is then evaluated using two ontology syntactic metrics: Relationship Richness and Information Richness. These metrics reflect the diversity of relationships and the breadth of knowledge in the model, respectively. In conclusion, the current research contributes to the Computing Curricula by providing an ontology model for a multidisciplinary database course. The model, developed through ontology reuse and reengineering and the integration of non-ontological resources, exhibits more diverse relationships and represents a broader range of knowledge.

Design and Implementation of Web-based Database Virtual Experiment Platform

Against the backdrop of the COVID-19 pandemic, many courses face challenges in online teaching. Enhancing the teaching quality of online experimental sections in current higher vocational database courses is an urgent need. This paper constructs a lightweight web-based database virtual experiment platform. The results show that the platform can effectively solve the problems of difficult implementation and uncertain experimental effects in experimental sections under the blended learning mode of database courses, effectively ensuring the quality of online teaching for such courses.

Connecting Actionable Feedback with Course Concepts Using an Auto-Feedback Framework - A Case Study in a Database Course

Effective feedback is crucial in engineering education to ensure students are progressing and staying on track. However, providing such feedback can be complex and time-consuming, leading to delays for students. To address this issue, this paper introduces an auto-feedback framework that aims to connect actionable feedback with course concepts in a personalized and efficient manner. We implemented a concrete feedback system using the framework in a database course. We found that students generally could improve their solutions within a few submissions, and the feedback helped students achieve better learning outcomes.

Reforming Database Courses Based on Knowledge Graphs

Reforming Database Courses Based on Knowledge Graphs

Database Course Teaching Reform Plan Based on CDIO

Database Course Teaching Reform Plan Based on CDIO

Analysing Gaming Behaviour: Insights on Personality Traits

Educational games have become an integral part of the educational process at various levels, with their usage rapidly increasing as innovative e-learning methods. These games have proven to be highly engaging and effective in knowledge retention. Previous research primarily focused on the impact of violent video games on behaviour and the correlation between game behaviour and personality. Traditionally personality assessment relies on psychometric questionnaires, with the Big Five Inventory (BFI) being a widely used tool. However, these approaches often have certain drawbacks as respondents tend to carefully consider their answers, prioritizing correctness over authenticity. To address these limitations, novel approaches are being developed that incorporate gaming elements to indirectly measure personality. Therefore, an intriguing question arises: Can the subconscious moves, choices, and behaviours exhibited during gameplay serve as indicators of players' personality? In this case study, we developed an educational game focused on Databases courses for university students. The game aims to capture everyday life experiences at the university such as social connections and curiosity or willingness to try new things, based on the Five-Factor Model (OCEAN). The educational content is presented in the form of a quiz with four possible answers, providing appropriate feedback based on the selected responses. The objectives were to strengthen the knowledge and comprehension of Databases subject and also to gather information about players' gaming behaviour and thus predict their scores on two personality traits: Extraversion and Openness to Experience, based on the Five-Factor Model. A total of 149 computer science students of the University of Macedonia participated in the study by playing the game and completing the BFI questionnaire. We utilized classification algorithms to develop a model to predict student’s personality. The goodness of the model was assessed using different metrics and the results showed that it is effective to model both the extraversion and openness personality dimensions using serious games instead of questionnaires. These findings can be used by educators and game designers to develop personalized educational games taking into account learner’s personality and thus provide valuable insights for future research in this domain.

Pengembangan Topik Pembelajaran SQL – DDL Menggunakan DBMS MYSQL Pada Platform Intelligent Computer-Assisted Programming Learning Platform (ICLOP)

Learning about databases at Polinema campus still relies on several lecturers within the database courses. However, due to limited time, the lecturers have constraints in conducting database course lessons, typically once a week for 3 credit hours. Additionally, lecturers have commitments outside the campus, affecting the efficiency of their assessments for students. Therefore, there is a need for a SQL – DDL learning system using MySQL on the iClop platform. The designed and implemented system utilizes PHP, MySQL, MyTAP, HTML, CSS, JavaScript, Bootstrap, and others. The system has been tested by various users, and the results show that each user can engage in SQL - DDL learning and exercises. The system also provides users with grades based on their performance.

Evaluation of Submission Limits and Regression Penalties to Improve Student Behavior with Automatic Assessment Systems

Objectives . Automatic assessment systems are widely used to provide rapid feedback for students and reduce grading time. Despite the benefits of increased efficiency and improved pedagogical outcomes, an ongoing challenge is mitigating poor student behaviors when interacting with automatic assessment systems including numerous submissions, trial-and-error, and relying on marking feedback for problem solving. These behaviors negatively affect student learning as well as have significant impact on system resources. This research quantitatively examines how utilizing submission policies such as limiting the number of submissions and applying regression penalties can reduce negative student behaviors. The hypothesis is that both submission policies will have a significant impact on student behavior and reduce both the number of submissions and regressions in student performance. The research questions evaluate the impact on student behavior, determine which submission policy is the most effective, and what submission policy is preferred by students. Participants . The study involved two course sections in two different semesters consisting of a total of 224 students at the University of British Columbia, a research-intensive university. The students were evaluated using an automated assessment system in a large third year database course. Study Methods . The two course sections used an automated assessment system for constructing database design diagrams for assignments and exams. The first section had no limits on the number of submissions for both assignments and exams. The second section had limits for the exams but no limits on assignments. On the midterm, participants were randomly assigned to have either a restriction on the total number of submissions or unlimited submissions but with regression penalties if a graded answer was lower than a previous submission. On the final exam, students were given the option of selecting their submission policy. Student academic performance and submission profiles were compared between the course sections and the different submission policies. Findings. Unrestricted use of automatic grading systems results in high occurrence of undesirable student behavior including trial-and-error guessing and reduced time between submissions without sufficient independent thought. Both submission policies of limiting maximum submissions and utilizing regression penalties significantly reduce these behaviors by up to 85%. Overall, students prefer maximum submission limits, and demonstrate improved behavior and educational outcomes. Conclusions . Automated assessment systems when used for larger problems related to design and programming have benefits when deployed with submission restrictions (maximum attempts or regression penalty) for both improved student learning behaviors and to reduce the computational costs for the system. This is especially important for summative assessment but reasonable limits for formative assessments are also valuable.

Modelling the role of learner presence within the community of inquiry framework to determine online course satisfaction in distance education

This study sought to investigate the nuances in predictive relationships existing among teaching presence, cognitive presence and social presence as well as learner presence in the Community of Inquiry (CoI) framework towards online course satisfaction. The study is necessitated by the deficiency of current literature in providing information on the nuances in interaction among the three original presences and learner presence, prior to the final determination of online course satisfaction. Thus, the study adopted a survey design and collected data via a questionnaire from 347 postgraduate students on an online database course. Partial Least Squares Structural Equation Modelling was used to validate a definite model on the predictive relationships existing among teaching presence, cognitive presence, social presence, learner presence and online course satisfaction. Results from the structural model analysis proved a statistically significant predictive relationship between learner presence and the three other presences (i.e. cognitive presence, social presence and teaching presence). Other relationships established include social presence and cognitive presence; social presence and teaching presence. Finally, online course satisfaction was predicted by social presence and teaching presence. Based on the findings it was recommended that institutions that offer online courses should device concrete strategies that promote social presence and teaching presence since these variables are precursors to online course satisfaction. Finally, the design of online courses should be effective and learner-centred to attract the learner since learner presence determines all the other three ‘presences’ in online learning environment.

Web-based Database Courses E-Learning Application

This study was focused on the development of a web e-learning application for the database courses taken by Information Technology (IT) students at the College of Information and Communications Technology (CICT) of Bulacan State University (BulSU). The research methodology used in this project was the cross-sectional developmental approach. The Agile Software Development methodology was followed phase by phase, up to the development phase, to develop the system. It was used to produce the desired output rapidly while allowing users to go back through phases without finishing the whole cycle. The goal of this study was to create a web application that teaches Structured Query Language (SQL) in both the MySQL and SQL Server approaches. The application contains quizzes and examinations to allow self-assessment of learning. Additionally, an Entity Relationship Diagram (ERD) simulation was included to provide ERD creations in a drag-and-drop fashion. This study was evaluated using ISO/IEC 25010 software quality evaluation criteria. The study's overall mean was 4.24, 4.41, and 4.33, all with the descriptive meaning of "Very Good," which showed that the system performed its necessary functions as perceived by students, faculty members, and experts, respectively. In summary, the e-learning web application for database courses was fully developed. Moreover, the entity-relationship diagram was integrated well within the system and is accessible to the users. Lastly, respondents evaluated the developed web application using the ISO/IEC 25010 with an overall descriptive interpretation of "Very Good." For future developments of the study, an administrator panel may be developed to manage users and do other administrative tasks. Lastly, higher-order thinking skills questions on assessments and quizzes may be included.

Students’ interest in knowledge acquisition in Artificial Intelligence

Some students’ expectations and points of view related to the Artificial Intelligence course are explored and analyzed in this study. We anonymous collected answers from 58 undergraduate students out of 200 enrolled in the Computer Science specialization. The answers were analysed and interpreted using thematic analysis to find out their interests and attractive and unattractive aspects related to the Artificial Intelligence study topic. We concluded that students are interested in Artificial Intelligence due to its trendiness, applicability, their passion and interest in the subject, the potential for future growth, and high salaries. However, the students’ expectations were mainly related to achieving medium knowledge in the Artificial Intelligence field, and men seem to be more interested in acquiring high-level skills than women. The most common part that wasn't enjoyed by the students was the mathematical aspect used in Artificial Intelligence. Some of them (a small group) were also aware of the Artificial Intelligence potential which could be used in an unethical manner for negative purposes. Our study also provides a short comparison to the Databases course, in which students were not that passionate or interested in achieving medium knowledge, their interest was related to DB usage and basic information.

A methodology for teaching database design based on a four-component instructional design model

Based on the analysis of relevant literature, the article highlights the difficulties of teaching a relational database course in higher educational institutions. Such problems are mainly associated with the lack of simple and at the same time formalized methods to teach the basics of this academic discipline. The research illustrates the importance of the correct choice of the instructional design model while developing the course. The article also provides a comparative analysis of the existing instructional design models performed with regard to their application to the development of a database course. The study has revealed the advantages of choosing a four- component model of instructional design (4C/ID). Such a model served as the basis for developing a methodology for teaching database design. On the one hand, this methodology makes it possible to avoid deep immersion in the mathematical foundations of the discipline. On the other hand, it is strictly formalized and fully consistent with these foundations. Formalization has been achieved through a cognitive strategy and cognitive rules that are clearly expressed in natural language. The cognitive strategy and rules are universal for database design in any subject area. The fact that students quickly go on to solve real practical problems in professional activity proves the effectiveness of the proposed methodology.

Effect of semantic distance on learning structured query language: An empirical study.

Students of database courses usually encounter difficulties in learning structured query language (SQL). Numerous studies have been conducted to improve how students learn SQL. However, learning SQL remains difficult. This study analyzed the difficulties in learning SQL from the viewpoint of semantic distance by using semantic network theory. An experiment involving a database course was performed to assess the influence of semantic distance on learners' understanding of SQL. The participants were requested to perform a query-writing task at the end of the course to investigate their understanding of SQL. The data analysis results indicated that the participants developed a better understanding of the formulation-to-planning transformation than the planning-to-coding transformation. This implies that the semantic distance of the planning-to-coding transformation is greater than that of the formulation-to-planning transformation, and the semantic distance of the planning-to-coding transformation is attributable to the semantic transformation from natural language to SQL, which are two essentially different languages and belong to different knowledge categories. Accordingly, this study concludes that SQL learning difficulties can mainly be ascribed to the planning-to-coding transformation because the large semantic distance. The findings suggest that SQL instructions should emphasize the semantic mapping of the planning-to-coding transformation by incorporating materials related to the transformation and should shorten the semantic distance involved in learning SQL. These two principles can be used to evaluate the effectiveness of SQL teaching methods in assisting SQL learning, and motivate researchers to develop more effective teaching methods from the viewpoint of semantic distance.

Knowing group motivation using Bolzano method on PageRank computation

On the e-learning system, student assignments are collected, but problems arise that based on observations from two classes of database courses, 73% of students make plagiarism so lecturers need to give motivation to students. Self motivate is needed by with a group of students. For this reason, using a bolzano method or bisection method will provide an overview of the development of the plagiarism trend between groups of students based on scores on similarity score that compute by PageRank algorithm used by Google. Research method is carried out by conducting preliminary observations of plagiarism scores and creating markov matrix. PageRank compute this ranking and Bolzano method find the intersection of two eigenvalues. Bolzano results the maximum and minimum values on range of intervals where each group consists of 5 students. Experiments were conducted in 3 classes of database courses. Trend analysis found that the plagiarism score averaged 54% with a gradient of 3°, this is relatively small but when spread in different groups it becomes larger and student group have a higher plagiarism score. Results implies a way of looking student motivation based on the plagiarism score by a small groupto motivate each other.

The 2022 undergraduate database course in computer science

research-article The 2022 undergraduate database course in computer science: what to teach? Authors: Mikey Goldweber Xavier University Xavier UniversityView Profile , Min Wei Microsoft MicrosoftView Profile , Sherif Aly The American University in Cairo The American University in CairoView Profile , Rajendra K. Raj Rochester Institute of Technology Rochester Institute of TechnologyView Profile , Mohamed Mokbel University of Minnesota University of MinnesotaView Profile Authors Info & Claims ACM InroadsVolume 13Issue 3September 2022 pp 16–21https://doi.org/10.1145/3549545Published:23 August 2022Publication History 0citation126DownloadsMetricsTotal Citations0Total Downloads126Last 12 Months126Last 6 weeks19 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The application of online practicum in assisting learning process of database courses using Waterfall method

The Open University is a State University prioritizing independent learning. Independent learning would certainly succeed if the students owned a strong determination in effectively and efficiently manage their time. In the Information Systems Study Program curriculum, there are several practical courses that must be conducted offline. In 2019 however, the phenomenon of the COVID-19 pandemic outbreak requiring all educational units to carry out learning from home had impacted on the learning process, especially the implementation of practicum at the Open University. The implementation of practicum activities usually held in collaboration with several institutions has become constrained. This triggered the possibility of not achieving student learning outcomes related to learning materials learned through practicum. Therefore, as an alternative solution, an application is required that could assist the students in carrying out practicum learning activities independently which is adapted to each teaching material. This practical application would be developed in a study using the Waterfall method consisting of analysis, design, implementation, testing and maintenance. From the results of test using the System Usability Scale (SUS), the score obtained is 64.44 (margin low). This means that the application online practicum can be used but does not meet the requirements to be implemented into existing learning systems until it gets a score of more than 80 (Acceptable).

Big Data: A Transition from Conventional Databases

The primary programming language created to manage data contained in database systems is called Structured Query Language (SQL). SQL was initially solely used with relational database management systems (RDBMS), but as additional categories of database systems have emerged, its use has substantially expanded. When processing Big Data, or datasets with great volume, velocity, and variety, highly distributed and scalable systems have been proven to be particularly effective at using SQL as a query language. Although conventional relational databases currently only make up a small portion of the database systems landscape, the majority of database courses that cover SQL only take traditional relational systems into account when using SQL. In this study, we suggest that SQL be taught as a general language, which can be applied to a variety of database systems, ranging from conventional RDBMSs to Big Data systems. In the context of emerging database systems like MapReduce, NoSQL, and NewSQL, this paper offers well-organized suggestions for introducing SQL. The description of a variety of course tools, such as virtual machines, sample projects, and in-class exercises, to provide hands-on experience with SQL across a wide range of contemporary database systems is a fundamental addition of this work. Descriptors for Categories and Subjects General terms for computer and information science education in K.3.2 [Computers and Education] Creating and experimenting Keywords Curriculums for databases; SQL; Query Language; Big Data 1.

Investigating the causal relationships between badges and learning outcomes in SQL-Tutor

The practice of adding game elements to non-gaming educational environments has gained much popularity. Gamification has been shown in some studies to enhance engagement, motivation and learning outcomes in technology-supported learning environments. Although gamification research has matured, there are some shortcomings such as inconsistency in applying gamification theories and frameworks and evaluating multiple game mechanics simultaneously. Moreover, there is little research on applying gamification to Intelligent Tutoring Systems (ITS). This paper investigates the causal effects of gamification on learning in SQL-Tutor, a mature ITS teaching students how to phrase queries in SQL. Having conducted a study under realistic conditions, we present a quantitative analysis of the performance of 77 undergraduate students enrolled in a database course. There are three main findings of our study: (1) gamification affects student learning by mediating the time-on-task; (2) students’ background knowledge does not influence time-on-task unless students achieve badges; and (3) students’ interest in topic (motivational construct) moderates the relationship between badges and time-on-task, but does not improve learning outcomes directly.

Teaching Method for Entity–Relationship Models Based on Semantic Network Theory

Students in database courses often have difficulty learning entity–relationship (ER) modeling. According to semantic network theory, learning to construct an ER diagram for a database problem requires complex semantic transformations between the problem and the diagram. Such complex transformation may require excessive mental effort by learners, jeopardizing their learning outcomes. A concept map is a learning tool that incorporates elements of both learning theory and semantic network theory. In this study, concept maps were used to describe the semantic transformation process to increase learner understanding of ER modeling. An empirical experiment was conducted on two database courses (one concept-map-based and one conventional course) to examine the effect of using concept maps on understanding ER modeling according to cognitive load theory. The experimental results revealed that the concept-map-based teaching method was superior to the conventional teaching method because it improved mental efficiency by reducing extraneous load while increasing germane load. Moreover, concept maps can be used as a medium to facilitate communication regarding ER modeling problems between learners and instructors, thereby improving learning efficiency. The results can help educators and researchers understand the effectiveness of concept maps for ER model learning, motivate them to resolve learning difficulties, and encourage them to develop improved teaching methods by using semantic network theory.