Jigsaw Java: Intelligent, Automated Marking

The popularity of Computing degrees in the UK has been increasing significantly over the past number of years. In Northern Ireland, from 2007 to 2015, there has been a 40% increase in acceptances to Computer Science degrees with England seeing a 60% increase over the same period (UCAS, 2016). However, this is tainted as Computer Science degrees also continue to maintain the highest dropout rates.
In Queen’s University Belfast we currently have a Level 1 intake of over 400 students across a number of computing pathways. Our drive as staff is to empower and motivate the students to fully engage with the course content. All students take a Java programming module the aim of which is to provide an understanding of the basic principles of object-oriented design. In order to assess these skills, we have developed Jigsaw Java as an innovative assessment tool offering intelligent, semi-supervised automated marking of code.
Jigsaw Java allows students to answer programming questions using a drag-and-drop interface to place code fragments into position. Their answer is compared to the sample solution and if it matches, marks are allocated accordingly. However, if a match is not found then the corresponding code is executed using sample data to determine if its logic is acceptable. If it is, the solution is flagged to be checked by staff and if satisfactory is saved as an alternative solution. This means that appropriate marks can be allocated and should another student have submitted the same placement of code fragments this does not need to be executed or checked again. Rather the system now knows how to assess it.
Jigsaw Java is also able to consider partial marks dependent on code placement and will “learn” over time. Given the number of students, Jigsaw Java will improve the consistency and timeliness of marking.

Benchmarking a modified version of the civ3 nonrelativistic atomic-structure code within Na-like-tungsten R -matrix calculations

In this work we explore the validity of employing a modified version of the nonrelativistic structure code civ3 for heavy, highly charged systems, using Na-like tungsten as a simple benchmark. Consequently, we present radiative and subsequent collisional atomic data compared with corresponding results from a fully relativistic structure and collisional model. Our motivation for this line of study is to benchmark civ3 against the relativistic grasp0 structure code. This is an important study as civ3 wave functions in nonrelativistic R-matrix calculations are computationally less expensive than their Dirac counterparts. There are very few existing data for the W LXIV ion in the literature with which we can compare except for an incomplete set of energy levels available from the NIST database. The overall accuracy of the present results is thus determined by the comparison between the civ3 and grasp0 structure codes alongside collisional atomic data computed by the R-matrix Breit-Pauli and Dirac codes. It is found that the electron-impact collision strengths and effective collision strengths computed by these differing methods are in good general agreement for the majority of the transitions considered, across a broad range of electron temperatures.