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

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

Teaching HDFS/MapReduce Systems Concepts to Undergraduates

This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses

Teaching HDFS/MapReduce Systems Concepts to Undergraduates

This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses

Teaching HDFS/MapReduce Systems Concepts to Undergraduates

This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses

Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence

In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI concepts through the lens of reproducibility. The focal point of the course is a group project based on reproducing existing FACT-AI algorithms from top AI conferences and writing a corresponding report. In the first iteration of the course, we created an open source repository with the code implementations from the group projects. In the second iteration, we encouraged students to submit their group projects to the Machine Learning Reproducibility Challenge, resulting in 9 reports from our course being accepted for publication in the ReScience journal. We reflect on our experience teaching the course over two years, where one year coincided with a global pandemic, and propose guidelines for teaching FACT-AI through reproducibility in graduate-level AI study programs. We hope this can be a useful resource for instructors who want to set up similar courses in the future

Development of Visualization-Animation Software for Learning Transportation Algorithms

Recognizing the steady decline in US Science Technology Engineering Mathematics (STEM) interests and enrollments, the National Science Foundation (NSF) and the White House have developed national strategies and provided significant budget resources to STEM education research [1-2] in the past years, with the ultimate goals being to improve both the quality and number of highly trained US educators, student workforce in STEM topics, in today’s highly competitive global markets. With the explosion of the internet’s capability and availability, it is even more critical to effectively train this future USA-STEM work-force and/or to develop effective STEM related teaching tools to reach a maximum possible number of “distance learners/audiences”. Various teaching philosophies have been proposed, tested and documented by educational research communities, such as video lectures (YouTube), “flipped” class lectures (where students are encouraged to read the lecture materials on their own time, and problem solving and/or question/answer sessions are conducted in the usual classroom environments), STEM summer camps, game-based-learning (GBL) [3-5], virtual laboratories [6] and concept inventory [7]. The goal of this study is to develop useful, user friendly Java computer animation for “teaching” these basic/important STEM algorithms that will not only help both the students and their instructors to master this technical subject, but also provide a valuable tool for obtaining the solutions for homework assignments, class examinations, and self-assessment tools. Java software tools were developed for this research which include the Unloading and Pre-Marshalling algorithms for Terminal Yard Operations, the Hungarian algorithm for worker to job optimum assignment, the Dijkstra algorithm for solving the shortest-path of a transportation network, and the Cholesky Decomposition algorithm for solving simultaneous linear equations. This “educational version” of the Java-based application were implemented with several desirable features, such as: A detailed, precise and clear step-by-step algorithm will be displayed in text and human voice during the animation of the algorithm. Options to hear animated voice in several major languages (English, Chinese and Spanish). Options to input/output data (CVS file), or manually edit the data using an editor, or “randomly generating” data. Output of the “final/optimal” results can be exported to text so that the users/learners can check/verify their “hand-calculated” results, which is an important part of the learning process

Learning to code in class with MOOCs: Process, factors and outcomes

Problem: Python became the most popular programming language in recent years, beating Java, the programming language still widely used as the main programming language in many undergraduate degrees on computer science related areas. Students from those degrees often do not get Python in their syllabus, but the job market is demanding it increasingly. Objective: To assess if learning a new programming language by following a MOOC is feasible in a fully dedicated mode and allows achieving a learning outcome comparable to the traditional in-class learning process. Proposal: Students from undergraduate degrees lacking Python skills followed a dedicated and intensive learning process on that language based on an in-class MOOC. The latter is suitable for students with some background in programming, as is the case, allowing a faster learning pace. Participants’ subjective perception of the corresponding workload was monitored. Validation: A programming contest, using an automatic judge, was used as a validation for this proposal. Two groups of students participated: those from three degrees lacking Python, which followed the proposed MOOC (experimental group), and those from the degree that includes Python programming, which had a traditional in-class learning process (control group). Conclusions: The experiment results were analysed and it was inferred that the proposed in-class MOOC learning approach is as effective as the traditional learning approach. Furthermore, it was identified that the students’ average grades obtained in the previous programming courses taken as part of their degree’s syllabus and the number of MOOC modules finished in the context of this experiment directly influence the number of points obtained in the contest.Problema: Nos últimos anos, Python tornou-se a linguagem de programação mais popular, ultrapassando o Java, que continua a sermuito usada como principal linguagem de programação em muitas licenciaturas relacionadas com informática. Estas licenciaturas acabam muitas vezes por não oferecer esta competência aos estudantes, no entanto o mercado de trabalho procura-a cada vez mais. Objectivo: Avaliar a possibilidade de aprender uma nova linguagem de programação através de um MOOC num regime de total dedicação. E por fim, perceber se este permite obter resultados comparáveis ao ensino tradicional. Proposta: Os estudantes com falta de conhecimentos de Python realizaram um processo de aprendizagem intensivo desta linguagem através de um MOOC em sala de aula. Este último é adequado a estudantes com alguns conhecimentos de programação, permitindo assim um ritmo mais rápido de aprendizagem. A perceção subjetiva dos participantes sobre a respetiva carga de trabalho foi monitorizada. Validação: Realização de um concurso de programação recorrendo a um juiz automático. Dois grupos de estudantes participaram neste concurso: estudantes das 3 licenciaturas sem conhecimentos de Python, que realizaram o MOOC (grupo experimental), e os estudantes da licenciatura que inclui Python e que teve uma aprendizagem tradicional (grupo de controlo). Conclusões: Os resultados deste experimento foram analisados e inferiu-se que a aprendizagem de um MOOC em sala de aula é tão eficaz quanto o ensino tradicional. Para além disso, foi também verificado que a média de notas dos estudantes obtida nas unidades curriculares de programação que já frequentaram no seu curso e o número de módulos feitos no MOOC no contexto desta experiência influenciam diretamente os pontos obtidos no concurso de programação