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The rough draft serves several functions. A quickglance willreveal whether whole sections are appearing without citations. At thedraftstage, you have the opportunity to educate the student further anddiscusshow proper citation works. You can also mark places and ask for moreresearchmaterial to be incorporated. If you are suspicious of the paper at thispoint, ask for the incorporation of some specific material that youname,such as a particular book or article. Keep the drafts and letstudentsknow that you expect major revisions and improvements between drafts.(Thisis actually a great way to improve students' writing, quite apart fromthe other goal of preventing plagairism.)
Use descriptive words that you would associate strongly with the content of your paper: the molecule studied, the organism used or studied, the treatment, the location of a field site, the response measured, etc.
Perhaps the most effective discussion will ask the students tothinkabout who is really being cheated when someone plagiarizes. Copyingpapers or even parts of papers short circuits a number of learningexperiencesand opportunities for the development of skills: actually doing theworkof the research paper rather than counterfeiting it gives the studentnotonly knowledge of the subject and insights into the world ofinformationand controversy, but improves research skills, thinking and analyzing,organizing, writing, planning and time management, and evenmeticulousness(those picky citation styles actually help improve one's attention todetail). All this is missed when the paper is faked, and it is these missedskillswhich will be of high value in the working world. A degreewill helpstudents get a first job, but performance--using the skills developedbydoing just such assignments as research papers--will be required forpromotion.
- Disintegrating the paper in hot distilled water and determining the pH of the extract.- Disintegrating the paper in cold distilled water and determining the pH of the extract.
- Directly using a wet electrode on the paper surface.
In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users’ information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.
4. Require process steps for the paper. Seta series ofdue dates throughout the term for the various steps of the researchpaperprocess: topic or problem, preliminary bibliography, prospectus,researchmaterial (annotated photocopies of articles, for example), outline,roughdraft, final annotated bibliography, final draft. Some of these partscanbe reverse engineered by the determined cheater, but most studentsshouldrealize that doing the assignment honestly is easier than thealternative.