An Observational Analysis of the Range and Extent of Contract Cheating from Online Courses Found on Agency Websites

Although online courses can provide access to higher education through e-learning systems which would not otherwise be available for students, they also pose challenges for academic integrity. Paramount to this is contract cheating, where students have been observed paying other people to complete work for them to complete their online courses. This paper analyses attempts by students at contract cheating using Transtutors.com, which is a billed as a site for homework support. A sample of 174 online assignments found on Transtutors.com are analysed and traced back to 17 online universities. Assignments from online institutions are demonstrated to be a particular problem for contract cheating detectives, since notifying staff at those institutions of attempts by their students to cheat has proved to be difficult or impossible. The paper concludes by looking at the wider issues posed by online contract cheating and the opportunities for automated detection within this field

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Mining Student Submission Information to Refine Plagiarism Detection

Plagiarism is becoming an increasingly important issue in introductory programming courses. There are several tools to assist with plagiarism detection, but they are not effective for more basic programming assignments, like those in introductory courses. The proliferation of auto-grading platforms creates an opportunity to capture additional information about how students develop the solutions to their programming assignments. In this research, we identify how to extract information from an online autograding platform, Mimir Classroom, that can be useful in revealing patterns in solution development. We explore how and to what extent this additional information can be used to better support instructors when identifying cases of probable plagiarism. We have developed a tool that takes the raw student assignment submissions from Mimir, analyzes them, and produces data sets and visualizations that help instructors to refine information extracted by existing plagiarism detection platforms. The instructors can then take this information to further investigate any probable cases of plagiarism that have been found by the tool. Our main goal is to give insight into student behaviors and identify signals that can be effective indicatives of plagiarism. Furthermore, the framework can enable the analysis of other aspects of students’ solution development processes that may be useful when reasoning about their learning. As an initial exploration scenario of the framework developed in this work, we have used student code submissions from the CSCE 121: Introduction to Program Design and Concepts course at Texas A&M University. We experimented with the student code submissions from the Fall 2018 and Fall 2019 offerings of the course

A Survey of Smart Classroom Literature

Recently, there has been a substantial amount of research on smart classrooms, encompassing a number of areas, including Information and Communication Technology, Machine Learning, Sensor Networks, Cloud Computing, and Hardware. Smart classroom research has been quickly implemented to enhance education systems, resulting in higher engagement and empowerment of students, educators, and administrators. Despite decades of using emerging technology to improve teaching practices, critics often point out that methods miss adequate theoretical and technical foundations. As a result, there have been a number of conflicting reviews on different perspectives of smart classrooms. For a realistic smart classroom approach, a piecemeal implementation is insufficient. This survey contributes to the current literature by presenting a comprehensive analysis of various disciplines using a standard terminology and taxonomy. This multi-field study reveals new research possibilities and problems that must be tackled in order to integrate interdisciplinary works in a synergic manner. Our analysis shows that smart classroom is a rapidly developing research area that complements a number of emerging technologies. Moreover, this paper also describes the co-occurrence network of technological keywords using VOSviewer for an in-depth analysis

Catching Lightning in a Bottle: Surveying Plagiarism Futures

The digitization of higher education is evolving academic misconduct, posing both new challenges to and opportunities for academic integrity and its research. The digital evidence inherent to online-based academic misconduct produces new avenues of replicable, aggregate, and data-driven (RAD) research not previously available. In a digital mutation of the misuse of unoriginal material, students are increasingly leveraging online learning platforms like CourseHero.com to exchange completed coursework. This study leverages a novel dataset recorded by the upload of academic materials on CourseHero.com to measure how at-risk sample courses are to potential academic misconduct. This study’s survey of exchanged coursework reveals that students are sharing a significant amount of academic material online that poses a direct danger to their courses’ academic integrity. This study’s approach to observing what academic material students are sharing online demonstrates a novel means of leveraging digitized academic misconduct to develop valuable insights for planning the mitigation of academic dishonesty and maintaining course academic integrity

State of the art and practice in AI in education

Recent developments in Artificial Intelligence (AI) have generated great expectations for the future impact of AI in education and learning (AIED). Often these expectations have been based on misunderstanding current technical possibilities, lack of knowledge about state-of-the-art AI in education, and exceedingly narrow views on the functions of education in society. In this article, we provide a review of existing AI systems in education and their pedagogic and educational assumptions. We develop a typology of AIED systems and describe different ways of using AI in education and learning, show how these are grounded in different interpretations of what AI and education is or could be, and discuss some potential roadblocks on the AIED highway

Effects of Plagiarism in Introductory Programming Courses on the Learning Outcomes

[EN] We compare two introductory programming courses and the accompanying programming assignments with respect to the learning outcomes and the relation to plagiarism. While in the first course the solutions from the students of their programming assignments are checked directly with a plagiarism detection system to prevent students from plagiarizing, plagiarism is not tracked in the second course. Running a post check against plagiarism after the course reveals a significant higher plagiarism rate with several exact copies. As the number of students handing in copies from fellow students increases, the failure rate in the final examination also rises. Analyzing the data does not only reveal a correlation between plagiarizing and inferior examination results, but also shows, that students confronted with a plagiarism detection system have better skills in fundamental coding concepts. We suppose this might be a result of the fact, that the implementation of a plagiarism detection system does not deter so many students from plagiarizing, but students are strongly motivated to run more modifications on their plagiarisms in order not to be caught.Pawelczak, D. (2019). Effects of Plagiarism in Introductory Programming Courses on the Learning Outcomes. En HEAD'19. 5th International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 623-631. https://doi.org/10.4995/HEAD19.2019.9297OCS62363

Programming Process, Patterns and Behaviors: Insights from Keystroke Analysis of CS1 Students

With all the experiences and knowledge, I take programming as granted. But learning to program is still difficult for a lot of introductory programming students. This is also one of the major reasons for a high attrition rate in CS1 courses. If instructors were able to identify struggling students then effective interventions can be taken to help them. This thesis is a research done on programming process data that can be collected non-intrusively from CS1 students when they are programming. The data and their findings can be leveraged in understanding students’ thought process, detecting patterns and identifying behaviors that could possibly help instructors to identify struggling students, help them and design better courses

An Automated Grading and Feedback System for a Computer Literacy Course

Computer Science departments typically offer a computer literacy course that targets a general lay audience. At Appalachian State University, this course is CS1410 - Introduction to Computer Applications. computer literacy courses have students work with various desktop and web-based software applications, including standard office applications. CS1410 strives to have students use well known applications in new and challenging ways, as well as exposing them to some unfamiliar applications. These courses can draw large enrollments which impacts efficient and consistent grading. This thesis describes the development and successful deployment of the Automated Grading And Feedback (AGAF) system for CS1410. Specifically, a suite of automated grading tools targeting the different types of CS1410 assignments has been built. The AGAF system tools have been used on actual CS1410 submissions and the resulting grades were verified. AGAF tools exist for Microsoft Office assignments requiring students to upload a submission file. Another AGAF tool accepts a student “online text submission” where the text encodes the URL of a Survey Monkey survey and a blog. Other CS1410 assignments require students to upload an image file. AGAF can process images in multiple ways, including decoding of a QR two-dimensional barcode and identification of an expected image pattern