On the Use of Semantic-Based AIG to Automatically Generate Programming Exercises

In introductory programming courses, proficiency is typically achieved through substantial practice in the form of relatively small assignments and quizzes. Unfortunately, creating programming assignments and quizzes is both, time-consuming and error-prone. We use Automatic Item Generation (AIG) in order to address the problem of creating numerous programming exercises that can be used for assignments or quizzes in introductory programming courses. AIG is based on the use of test-item templates with embedded variables and formulas which are resolved by a computer program with actual values to generate test-items. Thus, hundreds or even thousands of test-items can be generated with a single test-item template. We present a semantic-based AIG that uses linked open data (LOD) and automatically generates contextual programming exercises. The approach was incorporated into an existing self-assessment and practice tool for students learning computer programming. The tool has been used in different introductory programming courses to generate a set of practice exercises different for each student, but with the same difficulty and quality

Graphical scaffolding for the learning of data wrangling APIs

In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction

A constructivist, mobile and principled approach to the learning and teaching of programming

Novices in programming courses need to acquire a theoretical understanding of programming concepts as well as practical skills for applying them, but in traditional learning environments students passively listen to the lecture without proactive practice-based learning. There is a need for a constructivist approach to learning based on the ability of the learner to construct his or her own knowledge from the concepts provided by the instructors. Therefore, learning that uses a practical approach offers more in-depth understanding to students and sustains students’ attention as well as encourages students to be active players in their own learning process. The ubiquitous use of mobile devices and the evolution of mobile device technologies have led to a growing interest in these devices as pedagogical aids in a constructivist learning approach where students can immediately practice the concepts being taught in the lecture on their mobile devices