Lab reports can serve as devices of communication that simplify the process of sharing and understanding different ideas. The reports, Database Oriented Big Data Analysis Engine Based On Deep Learning by Xiaoran Shang and Data Analysis of Educational Evaluation Using K-Means Clustering Method by Riu Liu, are examples of reports that follow a common structure. Shang’s report focuses on the use of a more efficient engine system to meet the needs of growing demand for cloud databases. Liu’s report focuses on improving systems in education by using an analysis model built by a specific method. Upon review, both reports show use of the eight elements of any lab report: abstract, introduction, materials and methods, results, discussion, conclusion, and references. These elements are incorporated into the reports in a way that best represents all the information the authors intend to include. Because of the difference in the scope of their research, the structures also show slight differences. Both reports include titles that are informative because they include the main subjects of the study and the authors’ ideas. The titles are effective because they are specific, Liu includes the subjects, data analysis, education, and clustering method, while Shang introduces databases and data analysis engine. They only include words necessary to depict their topics, straying from aesthetic. 

According to the textbook, the abstract aspect of a report is the summary of its major components, introduction, results, and conclusions as they answer the questions of the motivation behind the study, discoveries, and the implications of these findings (Stuart 2021, pg. 932). Both authors follow this sequence in their abstracts, however they also detail their method beyond stating their plans. Within the experimental design represented in the abstract, Liu breaks down the design by showing the main ideas of each stage of their plan, which includes construction of the model, calculating the weight of unit of measurement, the value of the weight and representing their data (2022, pg.1). Along with the summary of the introduction, results, and conclusions, this follows an informative abstract because the major results are presented and made clear after the detailed follow through of the experiment’s design. This contrasts with Shang’s abstract, which does not specify the major results of the study but only restates the purpose of the experiment. “… compared with the traditional data analysis engine system with character search as the core, the database oriented big data analysis engine system based on a deep learning model and wolf swarm greedy algorithm has faster response speed and intelligence” (2022, pg. 1). Shang’s discovery only states that their idea was correct, which follows a descriptive abstract, the less popular of the forms because it requires more time to examine the report. It is important to recognize the two different forms of abstract because of audience awareness. If the audience is not someone particularly invested in the study, a descriptive abstract would not prove to be satisfactory while an informative abstract displays all the major ideas and findings to the audience before requiring further reading. 

The introduction is required to show the purpose of the study and its potential contribution to the broader field. To build the validity of the research, the introduction is expected to include knowledge of existing research so that the audience is aware that what is being studied is significant and not redundant. Liu makes sure to build an understanding with the audience of the importance of the research by supplying background information on current systems used by educational institutions, presenting the clear problem that schools are not able to manage the growing and overwhelming range of information (2022, pg. 2). By supplying extensive background information, the research has meaning to audiences beyond experts in the field, including more people in support of the goal. This is supported with the background information of reasons as to why there is a continuing problem: current algorithms are not designed to handle the abundance of information; the use of the author’s method is supported here explaining it is appropriate because it is able to better algorithms significantly, “this paper introduces K-means clustering algorithm to analyze the education evaluation data…. address the current educational data’s long processing time, uncertain parameters, and low clustering quality…. approach is suitable for large-scale DM (Data Mining)” (Liu 2022, pg. 2). As well as defining important terms, the paper does not fail to provide detailed information ahead of the methods section and allows the audience to digest the necessary information beforehand.  

Comparing Shang’s report on analysis engines, the author also provides a significant amount of background information that contributes to the purpose of the study instead of being excessive. This is done by explaining the impact of cloud computing thus far as the author’s goal of developing more efficient algorithms for faster search engine responses relies on a strong cloud database (Shang 2022, pg. 2). The author supports the use of “deep learning strategy and wolf swarm greedy algorithm” with meaningful reasons from efficient structure to minimize cost. These atone to a wide range of audience as well, both experts, investors, and the general audience. As the textbook also mentions to include previous work in the introduction, the reports follow suit through a slightly different format where they include the information right after the introduction in a related works section. They develop upon the works of previous authors and discuss the findings and how they correlate with the current studies’ purpose. This goes beyond the textbooks teaching to make previous research known because this format is still effective and shows extensive knowledge and understanding. 

The two lab reports differ in their reported methods because they take different approaches. Whereas Liu develops upon processes and the correlation between the method and purpose, Shang’s approach is to examine and evaluate different approaches to show how their supported method is most appropriate. Shang’s evaluation of different methods is justified because the goal of the new algorithm is to relate and perform knowing as much human behavior as possible, which predecessors have not accomplished (2022, pg. 6). In contrast, Liu’s methodology sections consist of subsections that develop upon the relationship between the purpose and the technology before specifying the study’s methodology in full detail. Even though these aspects of the report are formatted differently from the textbook’s descriptions, they still achieve the goal of a methodology section. Liu’s report for example, describes in large detail the primary material used in the research, “The core function of DM technology is to discover potential rules from large-scale data. DM (data mining) is a specialized technology for mining extraordinary knowledge from large-scale data” (2022, pg. 6). With as much detail as they use, the primary reasons of the distinct aspects of a lab report are still respected and contribute to the organization of the authors’ ideas rather than serve as a distraction.

Whereas the textbook adheres to separating results and discussions, the reports both include the two sections together. In Liu’s report, the results are displayed and compared in a graph and simultaneously analyzed, “In terms of recall rate, this method also has certain advantages, and the recall rate of this method is better than the other two algorithms” (2022, pg. 14). The comparisons between the precisions of each algorithm add to the validity of the author’s argument for the method. Likewise, Shang’s results also support the purpose and methodology as the chosen algorithm showed comparative data that distinguished from other algorithms specifically in speed and accuracy (2022, pg. 12). 

The conclusion of a report serves as the final way of persuading the audience to validate the author’s ideas and experiment. It consists of a summary of the major aspects of the author’s argument and the author’s parting knowledge as now they know even more about the subject. Both authors follow the usual format of a conclusion. However, as aforementioned the reports once again exemplify going beyond the textbook example. In addition to analyzing and summarizing their experiments and background information, the reports also mention the limits of their findings. Liu’s report mentions factors such as time constraints and limited knowledge (2022, pg. 18) while Shang’s factors include limited abilities of the algorithm as well as a more detailed experimental design (2022, pg. 13). By including their own limitations, they deepen the integrity of the experiment and their findings as well as give direction for future studies. Both reports also credit all the works cited in their references in alphabetical order, which also adds to the integrity of their works. 

Analyzing lab reports, focusing primarily on the format is not a linear task. To understand the expectations of a valid report, the contents must be understood to a certain degree, especially because the format of a report relies on its content. Though the textbook format mentions the eight elements: title, abstract, introduction, materials and methods, results, discussion, conclusion, and references, the two reports are evident that there can be flexibility depending on the abundance of information. Although, the format should not be drastically different because the primary goal of the report itself is to serve as straightforward evidence of the author’s idea and communicate that to any audience. 

Bibliography 

1. Liu, R. (2022). Data Analysis of Educational Evaluation Using K-Means Clustering Method. Computational Intelligence and Neuroscience, 2022. https://link.gale.com/apps/doc/A712889874/CDB?u=cuny_ccny&sid=bookmark-CDB&xid=5cdf67bf 
2. Shang, X. (2022). Database Oriented Big Data Analysis Engine Based on Deep Learning. Computational Intelligence and Neuroscience, 2022. https://link.gale.com/apps/doc/A717005268/CDB?u=cuny_ccny&sid=bookmark-CDB&xid=bee83119