Justin Do
Prof. Jacobson
Writing for Engineering
Lab Report
There are eight essential elements that are found in most lab reports which are title, abstract, introduction, materials, and methods, results, discussion, conclusion, and acknowledgments. However, some reports, such as “Proposing a Machine Learning Approach to Analyze and Predict Employment and its Factors” by Francisco J. Garcia-Penalvo, Juan Cruz-Benito, Martin Martin-Gonzalez, Andrea Vazquez-Ingelmo, Jose Carlos Sanchez-Prieto, and Roberto Theron, have more than eight elements. A lab report like “Student modeling: supporting personalized instruction, from problem-solving to exploratory open-ended activities” by Cristina Conati and Samad Kardan has exactly eight elements. These two lab reports are both computer science-related articles that provide information using techniques and programs such as machine learning, algorithms, and frameworks to support their claim. These articles are about different topics; however, they have a similar build in terms of the essential elements. Comparing and Contrasting these two articles will give the audience an understanding of the importance of organization and structure.
The first article, “Proposing a Machine Learning Approach to Analyze and Predict Employment and its Factors,” is very well organized. The title itself already gives the audience an idea of their method, which is machine learning, to accomplish their goal of analyzing and predicting employment. This full title is informative enough for the audience to decide whether the report is interesting. As a result, the article already saves the audience’s time by proving keywords that are relevant to their needs. In this case, the keyword “Machine Learning” will filter and help the audience find what they need. In fact, following the abstract, this article also has keyword sections that include machine learning, employment, artificial intelligence, and more, which further helps people to consider if this source is for them. As mentioned, there is an abstract that briefly explains the aim while also providing a background for this lab report. The purpose of this lab is stated within the first two lines of the abstract. Following the purpose, the authors explain what data is involved in performing this task of analyzing employment. The information that they are analyzing comes from the Spanish Observatory for Employability and Employment (OEEU), and they explain what this data is followed by why they chose to use this data. Once the audience feels attracted, the introduction will inform the audience about the purpose of the work along with relevant information such as concepts, research, and focus.
The authors of “Proposing a Machine Learning Approach to Analyze and Predict Employment and its Factors” successfully establish these aspects for their introduction. In the first paragraph of their introduction, they provide a reason as to why this research is essential. “…[employability] presents difficulties…[and] poses many questions…For these reasons, the area is still growing and need to push the outcomes to further research levels” (Garcia-Penalvo 1). By adding this to their conclusion, the audience understands that, because of the many questions that employability presents, the area is still growing. The authors also include several research projects that have been administered by the Organization for Economic Co-operation and Development (OECD) and the European Commission. Their aim is also presented in the introduction, which allows the reader to understand the focus of this paper. What is interesting about this article’s introduction is that the authors included the paper’s structure towards the end of their paragraph, which shows their attention to the organization. Followed by the introduction, the authors chose to provide a background section that presents their analysis of other research that pertains to employability and employment. Because of this additional element, the paper becomes more credible due to their above and beyond attitude. This also further enhances their introduction, which allows people to understand more about the research behind the lab report.
As for this article’s methods and materials, the authors shared that they used machine learning, random forest algorithms, and universal principles in data science. They also showed a numbered list that explains their workflow, which helps the audience to replicate this process. Similar to adding a background section, the authors also included a case study to show the initial results achieved. Because of this section, the initial results could be compared with the final results, which are in the discussion. Every lab should have a discussion that explains the purpose and explains the results of the experiment. Thus, the writers for this article shared that “The machine learning approach has been applied successfully by…other researchers in fields like Human-Computer Interaction. [Therefore], [we] tried this approach in a complex research like employment and employability” (Garcia-Penalvo 1). Because of their mention of why they chose the machine learning approach, the audience understands how machine learning correlates with predicting employment, which is their purpose. The authors also mention an algorithmic approach and how it affects the experiment. Towards the end of the discussion, the data proposes a new way of preparing students for their professional future.
The writers end the lab report with three sections, which include a conclusion and future work, acknowledgment, and also references. This follows the eight basic elements; however, providing future work allows the audience to grow and research from their lab report. The conclusion combines everything and leaves an open door for the audience to build from the given information. Their conclusion summarizes the results, purpose, and the overall experiment. Following the conclusion comes the acknowledgment and reference section, which provides credit for researching that help support and build the report’s claim.
Compared with the second article, “Student modeling: supporting personalized instruction, from problem solving to exploratory open-ended activities,” the first article contains more elements while still fulfilling the basic ones. The title for this article is quite detailed; however, not as complete as the first report. There is no information about the methods being used for this experiment; however, it does explain the topic that is to use technology to provide specific needs for every learner. While skimming through this article, it may be quite challenging to find particular sections because there are no indications for the essential elements. Compared with our employment article, this one lacks organization. That being said, the information that this article provided by the abstract, which is the only element that is clearly stated, makes this a good lab report. This experiment relies more on algorithms, machine learning, and systems like the Intelligent Tutoring System (ITS), which are stated in the abstract. Despite the poor organization, this article excels in terms of research and information. The authors seemed to rely more on the audience’s knowledge about information such as frameworks and several rules such as the class association rules (CAR) or association rule mining.
Similarly, the writers depend more on prior knowledge about algorithms, such as the Hotspot and AC-3 algorithms. As mentioned, this experiment is algorithm heavy, which means this article caters more towards experienced people. The elements are not explicitly stated as the audience should know that testing a framework relates more to the results rather than using the framework, which is part of the process. That being said, the discussion and future work are clearly presented in the lab report, which talks about the methods and the usages of the algorithms as well as the user-modeling framework, CAR, and CSP applet data set. This includes the results and diagrams for each method that is presented and explained in detail in the discussion section as well. Similar to the employability article, there is a future work section that helps give an idea to the audience about possible self-exploration. In other words, there are opportunities for growth and usage for the provided information. With that being said, the authors end the lab reports with a mention of related works as well as the conclusion and references.
The difference between these two articles, besides the topic, is the organization. The employability article is superior in terms of structure because of the excess elements such as background, case studies, and keywords. The authors of the student modeling article had exactly eight basic elements, but it does take some time to find specific sections as they are not labeled.
However, they provide excellent information and depend mostly on the audience’s prior knowledge. Because the student modeling article excels in information, diagrams, and explanation, it has a better discussion as opposed to the machine learning article. That being said, both articles are worth looking at because of the difference in the topic. However, compared in terms of format, the employability article is excellent. It is much easier to find information while skimming through the employability article, which shows the significance of the organization and the elements.
Consequently, one would appreciate both articles regardless of the difference because they both achieve providing concise information that is beneficial to the audience. If a reader wants quick information to determine whether it is necessary for their research, the employability article will successfully cater to that need. But, if they are well-rounded on concepts and techniques like algorithms, data science principles, frameworks, testing, and systems, the article based on student modeling’s structure will help due to its admirable ability to group information. In terms of formatting, the authors of these articles included exactly or more than the eight elements. As a result, this proves that the writers are aware of the default structure of a lab report.
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