Retrieving and classifying instances of source code plagiarism

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.     

The Librarian BFF: A Case Study of a Cohort-Based Personal Librarian Program

This case study presents the development and implementation of a personal librarian program for a cohort-based, executive-style higher education administration doctoral program. Librarians and the program director collaborated to create a student-centered program based on individual research needs. The personal librarian program was designed to build a close relationship between the librarian and individual students; to help identify their research knowledge gaps; and to identify resources to meet each individual students' research needs.     

Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology

ABSTRACT

Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication research. Yet, questions regarding reliability and validity of the approach have received little attention thus far. In applying LDA to textual data, researchers need to tackle at least four major challenges that affect these criteria: (a) appropriate pre-processing of the text collection; (b) adequate selection of model parameters, including the number of topics to be generated; (c) evaluation of the model’s reliability; and (d) the process of validly interpreting the resulting topics. We review the research literature dealing with these questions and propose a methodology that approaches these challenges. Our overall goal is to make LDA topic modeling more accessible to communication researchers and to ensure compliance with disciplinary standards. Consequently, we develop a brief hands-on user guide for applying LDA topic modeling. We demonstrate the value of our approach with empirical data from an ongoing research project.     

Investigating the quality of interactions and public engagement around scientific papers on Twitter

This study explores science communication on Twitter by investigating a sample of tweets referring to academic papers in five different scientific fields. The specifications of science communicators on Twitter, the characteristics of those who initiate actions (by tweeting), the extent and quality of reactions (retweeting), individual and group interactions, and the distribution of tweets across types of engagement in the process of science communication (i.e., dissemination, consultation, and evaluation) were explored. A broad array of actors is involved in the communication of science on Twitter, with individual citizens and individual researchers playing an important role. In principle, this is promising for creating direct interaction, which can be difficult through more traditional mass media. The vast majority of communication activities regarding academic papers is undigested dissemination with almost no sign of debate, contestation, or collective reflection. Another general finding of this study is that bot accounts play a major role in the science communication landscape on Twitter.     

Course Design,Quality Matters Training,and Student Outcomes

ABSTRACT

North Carolina Central University (NCCU) recognized the need to address the increasing rates of Ds, Fs, and Withdrawal by students matriculating in online courses. Led by two science faculty, a faculty learning community in partnership with the NCCU Division of Extended Studies was created to assess online science course offerings and instruction. Faculty within this learning community completed the Quality Matters (QM) “Applying the Quality Matters Rubric” course. This online training course, coupled with services provided by the NCCU Division of Extended Studies, offered the faculty learning community a variety of pedagogical strategies to meet their instructional needs. All faculty participating in this learning community completed the online training course and all faculty indicated that they implemented changes in their subsequent online course offerings. This article describes the impact of that training and the application of quality course design standards in the Quality Matters Rubric on the design and student outcomes for an Introductory Biology course over four terms. As faculty learning communities are broadly utilized, it is anticipated that this article may present an effective strategy to increase the quality and quantity of online science, technology, engineering and mathematics (STEM) courses at similar institutions.