Students' syntactic mistakes in writing seven different types of SQL queries and its application to predicting students' success

© 2016 ACM. The computing education community has studied extensively the errors of novice programmers. In contrast, little attention has been given to student's mistake in writing SQL statements. This paper represents the first large scale quantitative analysis of the student's syntactic mistakes in writing different types of SQL queries. Over 160 thousand snapshots of SQL queries were collected from over 2000 students across eight years. We describe the most common types of syntactic errors that students make. We also describe our development of an automatic classifier with an overall accuracy of 0.78 for predicting student performance in writing SQL queries

Early identification of novice programmers' challenges in coding using machine learning techniques

It is well known that many first year undergraduate university students struggle with learning to program. Educational Data Mining (EDM) applies machine learning and statistics to information generated from educational settings. In this PhD project, EDM is used to study first semester novice programmers, using data collected from students as they work on computers to complete their normal weekly laboratory exercises. Analysis of the generated snapshots has shown the potential for early identification of students who later struggle in the course. The aim of this study is to propose a method for early identification of "at risk" students while providing suggestions on how they can improve their coding style. This PhD project is within its final year

SQL Logic Error Detection by Using Start End Mid Algorithm

Data base is an important part of a system and it stores data to be manipulated. A language called SQL (Structured Query Language) is used for manipulating those data to make needed information. There are two types of error which make SQL more difficult in practical implementation. They are syntax error and logic error. The difference between them is that syntax error can be detected by compiler so it is easy to learn by its warning. But compiler does not show error warning if logical error was occurred. It makes logic error is more difficult to understand than syntax error. To help data base's user to learn SQL in practical implementation, web based SQL compiler that be able to detect syntax and logic error is developed by using Start End Mid algorithm

Generating SQL queries from visual specifications

Abstract: Structured Query Language (SQL) is the most widely used declarative language for accessing relational databases, and an essential topic in introductory database courses in higher learning institutions. Despite the intuitiveness of SQL, formulating and comprehending written queries can be confusing, especially for undergraduate students. One major reason for this is that the simple syntax of SQL is often misleading and hard to comprehend. A number of tools have been developed to aid the comprehension of queries and improve the mental models of students concerning the underlying logic of SQL. Some of these tools employed visualisation and animation in their approach to aid the comprehension of SQL. This paper presents an interactive comprehension aid based on visualisation, specifically designed to support the SQL SELECT statement, an area identified in the literature as problematic for students. The visualisation tool uses visual specifications depicting SQL operations to build queries. This is expected to reduce the cognitive load of a student who is learning SQL. We have shown with an online survey that adopting visual specifications in teaching systems assist students in attaining a richer learning experience in introductory database courses

DBSnap-Eval: Identifying Database Query Construction Patterns

Learning to construct database queries can be a challenging task because students need to learn the specific query language syntax as well as properly understand the effect of each query operator and how multiple operators interact in a query. While some previous studies have looked into the types of database query errors students make and how the availability of expected query results can help to increase the success rate, there is very little that is known regarding the patterns that emerge while students are constructing a query. To be able to look into the process of constructing a query, in this paper we introduce DBSnap-Eval, a tool that supports tree-based queries (similar to SQL query plans) and a block-based querying interface to help separate the syntax and semantics of a query. DBSnap-Eval closely monitors the actions students take to construct a query such as adding a dataset or connecting a dataset with an operator. This paper presents an initial set of results about database query construction patterns using DBSnap-Eval. Particularly, it reports identified patterns in the process students follow to answer common database queries

Supervised Learning Algorithms in Educational Data Mining: A Systematic Review

The academic institutions always looking for tools that improve their performance and enhance individuals outcomes. Due to the huge ability of data mining to explore hidden patterns and trends in the data, many researchers paid attention to Educational Data Mining (EDM) in the last decade. This field explores different types of data using different algorithms to extract knowledge that supports decision-making and academic sector development. The researchers in the field of EDM have proposed and adopted different algorithms in various directions. In this review, we have explored the published papers between 2010-2020 in the libraries (IEEE, ACM, Science Direct, and Springer) in the field of EDM are to answer review questions. We aimed to find the most used algorithm by researchers in the field of supervised machine learning in the period of 2010-2020. Additionally, we explored the most direction in the EDM and the interest of the researchers. During our research and analysis, many limitations have been examined and in addition to answering the review questions, some future works have been presented

Semi-automatic assessment of basic SQL statements

Learning and assessing the Structured Query Language (SQL) is an important step in developing students' database skills. However, due to the increasing numbers of students learning SQL, assessing and providing detailed feedback to students' work can be time consuming and prone to errors. The main purpose of this research is to reduce or remove as many of the repetitive tasks in any phase of the assessment process of SQL statements as possible to achieve the consistency of marking and feedback on SQL answers.This research examines existing SQL assessment tools and their limitations by testing them on SQL questions, where the results reveal that students must attaint essential skills to be able to formulate basic SQL queries. This is because formulating SQL statements requires practice and effort by students. In addition, the standard steps adopted in many SQL assessment tools were found to be insufficient in successfully assessing our sample of exam scripts. The analysis of the outcomes identified several ways of solving the same query and the categories of errors based on the common student mistakes in SQL statements. Based on this, this research proposes a semi-automated assessment approach as a solution to improve students’ SQL formulation process, ensure the consistency of SQL grading and the feedback generated during the marking process. The semi-automatic marking method utilities both the Case-Based Reasoning (CBR) system and Rule-Based Reasoning (RBR) system methodologies. The approach aims to reduce the workload of marking tasks by reducing or removing as many of the repetitive tasks in any phase of the marking process of SQL statements as possible. It also targets the improvement of feedback dimensions that can be given to students.In addition, the research implemented a prototype of the SQL assessment framework which supports the process of the semi-automated assessment approach. The prototype aims to enhance the SQL formulation process for students and minimise the required human effort for assessing and evaluating SQL statements. Furthermore, it aims to provide timely, individual and detailed feedback to the students. The new prototype tool allows students to formulate SQL statements using the point-and-click approach by using the SQL Formulation Editor (SQL-FE). It also aims to minimise the required human effort for assessing and evaluating SQL statements through the use of the SQL Marking Editor (SQL-ME). To ensure the effectiveness of the SQL-FE tool, the research conducted two studies which compared the newly implemented tool with the paper-based manual method in the first study (pilot study), and with the SQL Management Studio tool in the second study (full experiment). The results provided reasonable evidence that using SQL-FE can have a beneficial effect on formulating SQL statements and improve students’ SQL learning. The results also showed that students were able to solve and formulate the SQL query on time and their performance showed significant improvement. The research also carried out an experiment to examine the viability of the SQL Marking Editor by testing the SQL partial marking, grouping of identical SQL statements, and the resulting marking process after applying the generic marking rules. The experimental results presented demonstrated that the newly implemented editor was able to provide consistent marking and individual feedback for all SQL parts. This means that the main aim of this research has been fulfilled, since the workload of the lecturers has been reduced, and students’ performance in formulating SQL statements has been improved.</div