This article provides the first review of the existing literature consolidating research into the use of virtual, augmented and mixed reality technologies within K–12 educational environments. The ...

Virtual, augmented and mixed reality in K–12 education: a review of the literature

Practical programming competencies are critical to the success in computer science (CS) education and go-to-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deep, and fair fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, and readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial Boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state of the art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Source code plagiarism is a long-standing issue in tertiary computer science education. Many source code plagiarism detection tools have been proposed to aid in the detection of source code plagiarism. However, existing detection tools are not robust to pervasive plagiarism-hiding transformations and can be inaccurate in the detection of plagiarised source code. This article presents BPlag, a behavioural approach to source code plagiarism detection. BPlag is designed to be both robust to pervasive plagiarism-hiding transformations and accurate in the detection of plagiarised source code. Greater robustness and accuracy is afforded by analyzing the behavior of a program, as behavior is perceived to be the least susceptible aspect of a program impacted upon by plagiarism-hiding transformations. BPlag applies symbolic execution to analyses execution behavior and represents a program in a novel graph-based format. Plagiarism is then detected by comparing these graphs and evaluating similarity scores. BPlag is evaluated for robustness, accuracy and efficiency against five commonly used source code plagiarism detection tools. It is then shown that BPlag is more robust to plagiarism-hiding transformations and more accurate in the detection of plagiarised source code, but is less efficient than the compared tools.

Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity

‘Mind the Gap!’ Exploring the post-graduation outcomes and employment mobility of individuals who are first in their family to complete a university degree

Source code plagiarism is a persistent problem in undergraduate computer science education. Unfortunately, it is a widespread phenomena with many students plagiarising either because they are unwilling or incapable of completing their own work. Many source code plagiarism detection tools have been proposed to identify suspected cases of source code plagiarism. However, these tools are not resilient to pervasive plagiarism-hiding transformations that significantly change the structure of source code. In this paper, two case studies are presented that explore how resilient current source code plagiarism detection tools are to plagiarism-hiding transformations. Furthermore, an evaluation of a new advanced technique for source code plagiarism detection is presented to show that is it possible to identify pervasive cases of source code plagiarism. The results of this evaluation indicate the technique is robust in its ability to identify the same program after it has been transformed.

Detecting Pervasive Source Code Plagiarism through Dynamic Program Behaviours

Source code plagiarism is a common occurrence in undergraduate computer science education. Studies have indicated at least 50% of students plagiarize during their undergraduate career. To identity cases of source code plagiarism, many source code plagiarism detection tools have been proposed. However, conclusively determining the effectiveness these tools at identifying cases of source code plagiarism is difficult. Evaluations are typically performed using unreleased data sets. Without a comprehensive publicly available data set for source code plagiarism detection evaluation, it is difficult to perform an unbiased and reproducible evaluations of tools. To address this problem, this paper presents a tool, SPPlagiarise, which is designed to produce simulated source code plagiarism of Java source code. SPPlagiarise applies a random number of semantics-preserving source code obfuscations at random locations to a Java code base to simulate source code plagiarism. In this paper the design of the tool and an evaluation of a generated plagiarism data set is presented.

SPPlagiarise: A Tool for Generating Simulated Semantics-Preserving Plagiarism of Java Source Code

This roadmap highlights, in an accessible way, some existing and emerging digital technologies and their potential to create deeper and authentic learning opportunities in school and post-school education. Like all useful roadmaps, this report does two things. Firstly, it charts some broad directions in the general types of technologies that are currently commercially available and those that are predicted to be available and affordable within a 3-10 year timeframe. Secondly, the report provides descriptions of these technologies, their key features and some imaginative examples of their current or possible application in education and for careers exploration. The purpose of this roadmap is to provoke the imagination of educators in considering how these technologies might be used for education and career exploration, because engaging educators now will be vital if the characteristics (or affordances) of these technologies are to be used in pedagogically sound and curriculum–aligned ways that are duly informed by learning science.

https://apo.org.au/sites/default/files/resource-files/2016-10/apo-nid69649.pdf

Immersed in the future: a roadmap of existing and emerging technology for career exploration

Source code plagiarism is a persistent problem in computer science education. Many tools have been developed to identify indications of source code plagiarism in large data sets. These tools are good at identifying simple cases of plagiarism (e.g. renaming identifiers or shuffling declarations), but they are vulnerable to semantics-preserving obfuscations. This is due to a reliance on analyzing the structure of source code, and not the implemented program logic. In this paper, a novel approach to source code plagiarism detection is proposed that compares two programs for logic similarity. This approach analyses the logic embedded in a program as a means of identifying similarity. The approach is evaluated on a data set of simulated plagiarism. The evaluation demonstrates that the approach is resilient to semantics-preserving transformations.

Hayden Cheers

Papers

Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity

Detecting Pervasive Source Code Plagiarism through Dynamic Program Behaviours

SPPlagiarise: A Tool for Generating Simulated Semantics-Preserving Plagiarism of Java Source Code

Immersed in the future: a roadmap of existing and emerging technology for career exploration

A Novel Approach for Detecting Logic Similarity in Plagiarised Source Code