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

Data analytics and the novice programmer

University of Technology Sydney. Faculty of Engineering and Information Technology.The aptitude of students for learning how to program (henceforth Programming learn-ability) has always been of interest to the computer science education researcher. This issue of aptitude has been attacked by many researchers and as a result, different algorithms have been developed to quantify aptitude using different methods. Advances in online MOOC systems, automated grading systems, and programming environments with the capability of capturing data about how the novice programmer’s behaviour has resulted in a new stream of studying novice programmer, with a focus on data at large scale. This dissertation applies contemporary machine learning based analysis methods on such “big” data to investigate novice programmers, with a focus on novices at the early stages of their first semester. Throughout the thesis, I will demonstrate how machine learning techniques can be used to detect novices in need of assistance in the early stages of the semester. Based on the results presented in this dissertation, a new algorithm to profile novices coding aptitude is proposed and its’ performance is investigated. My dissertation expands the range of exploration by considering the element of context. I argue that the differential patterns recognized among different population of novices is very sensitive to variations in data, context and language; hence validating the necessity of context-independent methods of analyzing the data

OPUS: an Alternate Reality Game to learn SQL at university

The project aims to test the effectiveness of applying the principles of experiential learning within a university course. In particular, the objective of the paper is to investigate the educational effectiveness of the Alternate Reality Games (ARGs) and of their characterizing elements: the immersive storytelling, which blends reality and fiction, and the collaborative approach, which activates collective intelligence dynamics. The project combines the concepts of a Database course with the transmedial interaction techniques of a Transmedia course. The idea was to stimulate the interest of Databases course’s students in this subject and help them learn and consolidate SQL. The result was the creation of a playful experience that is classified as Alternate Reality Game, a realistic and highly immersive interactive storytelling, set in a likely fictional universe where the basic rule is “This is not a game”. The ARG was designed to complement the laboratory practice in the context of a Databases university course. In this way, students can practice, review and consolidate the skills acquired during the course. Furthermore, the playful component is accompanied by on-demand educational content, which players have the opportunity to request when they experience difficulties in solving puzzles that require querying the database

Improving the effectiveness of SQL learning practice: a data-driven approach

Most engineering courses include fundamental practice activities to be performed by students in computer labs. During lab sessions, students work on solving exercises with the help of teaching assistants, who often have a hard time for guaranteeing a timely, optimized, and “democratic” support to everybody. This paper presents a learning environment to improve the experience of the lab sessions participants, both the students and the teaching assistants. In particular, the environment was designed, implemented, and experimented in the context of a database course. The application designed to support the learning environment stores all the events occurring during a SQL practice lab, i.e., task progression, query submissions, error feedback, assistance requests and interventions, and it provides information useful both for use on-the-fly and for later analysis. Thanks to the analysis of these data, the application dynamically provides teaching assistants with a graphical interface highlighting where assistance is most needed, by considering different factors such as the progression rate, the percentage of correct solutions, and the difficulties in solving the current exercise. Furthermore, the stored data allow teachers later on to analyze and to interpret the behavior of the students during the lab, and to have insights on their main mistakes and misconceptions. After describing the environment, the interfaces, and the approaches used to identify the students’ teams that need timely assistance, the paper presents the results of different analyses performed using the collected data, to help the teacher better understand students’ educational needs

The Query Cube: A Framework for Assessing User Productivity with Database Information Retrieval

Three key factors that affect user productivity on database information retrieval are representation realism, expressive ease, and task complexity. Representation realism is the level of abstraction used in formulating queries. Expressive ease is the syntactic flexibility of a query language. Task complexity is the level of difficulty of queries. These factors formed a three dimensional query cube. A laboratory experiment was conducted to evaluate user productivity on database information retrieval corresponding to different vertices of the query cube. The results show that the query cube is a viable framework for assessing user productivity, both on effectiveness and efficiency perspective

SQL Tester: An online SQL assessment tool and its impact

Learning SQL can be surprisingly difficult, given the relative simplicity of its syntax. Automated tools for teaching and assessing SQL have existed for over two decades. Early tools were only designed for teaching and offered increased feedback and personalised learning, but not summative assessment. More recently, however, the trend has turned towards automated assessment, with learning as a side-effect. These tools offer more limited feedback and are not personalised. In this paper, we present SQL Tester, an online assessment tool and an assessment of its impact. We show that students engaged with SQL Tester as a learning tool, taking an average of 10 practice tests each and spending over 4 hours actively engaged in those tests. A student survey also found that over 90% of students agreed that they wanted to keep trying practice tests until they got a “good” mark. Finally, we present some evidence that taking practice tests increased student achievement, with a strong correlation between the number of practice tests a student took and their score on the assessed test

Data-Driven Database Education: A Quantitative Study of SQL Learning in an Introductory Database Course

The Structured Query Language (SQL) is widely used and challenging to master. Within the context of lab exercises in an introductory database course, this thesis analyzes the student learning process and seeks to answer the question: ``Which SQL concepts, or concept combinations, trouble students the most?\u27\u27 We provide comprehensive taxonomies of SQL concepts and errors, identify common areas of student misunderstanding, and investigate the student problem-solving process. We present an interactive web application used by students to complete SQL lab exercises. In addition, we analyze data collected by this application and we offer suggestions for improvement to database lab activities

TLAD 2010 Proceedings:8th international workshop on teaching, learning and assesment of databases (TLAD)

This is the eighth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2010), which once again is held as a workshop of BNCOD 2010 - the 27th International Information Systems Conference. TLAD 2010 is held on the 28th June at the beautiful Dudhope Castle at the Abertay University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.This year, the workshop includes an invited talk given by Richard Cooper (of the University of Glasgow) who will present a discussion and some results from the Database Disciplinary Commons which was held in the UK over the academic year. Due to the healthy number of high quality submissions this year, the workshop will also present seven peer reviewed papers, and six refereed poster papers. Of the seven presented papers, three will be presented as full papers and four as short papers. These papers and posters cover a number of themes, including: approaches to teaching databases, e.g. group centered and problem based learning; use of novel case studies, e.g. forensics and XML data; techniques and approaches for improving teaching and student learning processes; assessment techniques, e.g. peer review; methods for improving students abilities to develop database queries and develop E-R diagrams; and e-learning platforms for supporting teaching and learning