Lucene4IR: Developing information retrieval evaluation resources using Lucene

The workshop and hackathon on developing Information Retrieval Evaluation Resources using Lucene (L4IR) was held on the 8th and 9th of September, 2016 at the University of Strathclyde in Glasgow, UK and funded by the ESF Elias Network. The event featured three main elements: (i) a series of keynote and invited talks on industry, teaching and evaluation; (ii) planning, coding and hacking where a number of groups created modules and infrastructure to use Lucene to undertake TREC based evaluations; and (iii) a number of breakout groups discussing challenges, opportunities and problems in bridging the divide between academia and industry, and how we can use Lucene for teaching and learning Information Retrieval (IR). The event was composed of a mix and blend of academics, experts and students wanting to learn, share and create evaluation resources for the community. The hacking was intense and the discussions lively creating the basis of many useful tools but also raising numerous issues. It was clear that by adopting and contributing to most widely used and supported Open Source IR toolkit, there were many benefits for academics, students, researchers, developers and practitioners - providing a basis for stronger evaluation practices, increased reproducibility, more efficient knowledge transfer, greater collaboration between academia and industry, and shared teaching and training resources

Overview of technologies for building robots in the classroom

This paper aims to give an overview of technologies that can be used to implement robotics within an educational context. We discuss complete robotics systems as well as projects that implement only certain elements of a robotics system, such as electronics, hardware, or software. We believe that Maker Movement and DIY trends offers many new opportunities for teaching and feel that they will become much more prominent in the future. Products and projects discussed in this paper are: Mindstorms, Vex, Arduino, Dwengo, Raspberry Pi, MakeBlock, OpenBeam, BitBeam, Scratch, Blockly and ArduBlock

Student success model in programming course: A case study in UUM

The complexity and difficulty ascribed to computer programming has been asserted to be the causes of its high rate of failure record and attrition. It is opined that programming either to novice, middle learner, and the self-branded geeks is always a course to be apprehensive of different studies with varying findings. Studies on factors leading to the success of programming course in higher institution have been carried out. The record at Universiti Utara Malaysia (UUM) shows that 38% of semester one undergraduate students failed the programming course in 2013. This really motivates this study, which aims at investigating the practical factors affecting the success of programming courses, and to position its’ theoretically findings to complement the existing findings. Data were gathered using a quantitative approach, in which a set of questionnaire were distributed to 282 sampled respondents, who are undergraduate and postgraduate students of Information Technology (IT) and Information and Communication Technology (ICT). Having screened and cleaned the data, which led to the deletion of four outlier records, independent T-test, correlation, and regression were run to test the hypotheses. The results of Pearson correlation test reveal that teaching tools, OOP concepts, motivation, course evaluation, and mathematical aptitude are positively related to academic success in programming course, while fear is found to be negatively related. In addition, the regression analysis explains that all the elicited independent variables except fear are strongly related. Besides, the independent T-test also discovers no deference between groups with and without previous programming experience

Potential Uses of AI-Based Platforms in Teaching and Learning

Since its development more than fifty years ago, AI and AI-based platforms have been used in many areas including education. More recently, with the development and release of various chatbots and, in particular, platforms such as GPT-4 researchers and institutions of higher education are more seriously looking at the more meaningful, constructive, and ethical uses of AI in teaching and learning. In this editorial, we will briefly review the potential uses and expansion of AI-based technologies in support of innovative teaching and learning to intentionally, ethically, and economically help students, faculty, and higher education institutions to eliminate or at least reduce the educational gap that exists in many communities

Confessions of a live coder

This paper describes the process involved when a live coder decides to learn a new musical programming language of another paradigm. The paper introduces the problems of running comparative experiments, or user studies, within the field of live coding. It suggests that an autoethnographic account of the process can be helpful for understanding the technological conditioning of contemporary musical tools. The author is conducting a larger research project on this theme: the part presented in this paper describes the adoption of a new musical programming environment, Impromptu, and how this affects the author’s musical practice

A Data-Driven Approach to Compare the Syntactic Difficulty of Programming Languages

Educators who teach programming subjects are often wondering “which programming language should I teach first?”. The debate behind this question has a long history and coming up with a definite answer to this question would be farfetched. Nonetheless, several efforts can be identified in the literature wherein pros and cons of mainstream programming languages are examined, analysed, and discussed in view of their potential to facilitate the didactics of programming concepts especially to novice programmers. In line with these efforts, we explore the latter question by comparing the syntactic difficulty of two modern, but fundamentally different, programming languages: Java and Python. To achieve this objective, we introduce a standalone and purely data-driven method which stores the code submissions and clusters the errors occurred under the aid of a custom transition probability matrix. For the evaluation of this model a total of 219,454 submissions, made by 715 first-year undergraduate students, in 259 unique programming exercises were gathered and analysed. The results indicate that Python is an easier-to-grasp programming language and is, therefore, highly recommended as the steppingstone in introductory courses. Besides, the adoption of the described method enables educators to not only identify those students who struggle with coding (syntax-wise) but further paves the pathway for the adoption of personalised and adaptive learning practices