## 5 Results and Discussion
### Analysis of the Manually Added Research Label of Learning Analytics  
Our analysis of \"manually added research labels\" showed that the most common research label was \"Educational Data Mining\", followed by \"Machine Learning\" and then \"Educational Technology\" as depicted in Figure 2. We observed a variety of research labels manually added by researchers, which reflected the multidisciplinary and interdisciplinary nature of the LA research area. The results showed that the research label \"learning analytics\" was added by authors with a wide-ranging length of experience in scientific research, showing LA's emergence as a new area of research as recently as 2011. Figure 3 shows that the oldest public profiles with LA as a research label were created as early as 1980, while the latest were in the 2020s. Figure 3: Number of authors with LA research label, by starting year of their first publication in GS. Figure 2: All authors’ most frequent research labels. An analysis of the frequency of research labels used by authors revealed that using manually added research labels did not usefully differentiate specialist LA researchers from others, for the following reasons:  
* Consistency: Manually added research labels may vary in the level of detail and specificity used to describe a particular research interest. For example, some authors may use very broad labels that encompass a wide range of topics, while others may use more specific labels that relate to a narrow subfield. * Timeliness: manually added research labels may not be updated as frequently as the content of the paper itself or the author's current areas of research. * Objectivity: As noted earlier, manually added research labels may be more subjective, thus potentially biased. This is because research labels are added by the authors themselves and may be influenced by their personal perspectives and priorities. * Mischaracterization: manually added research labels are not verified or reviewed by GS or any other independent organization, which signify that there might be chances of potential error. Analysis of Automatically Extracted Research Keyword for Authors Who Use Manually Added Learning Analytics Research Labels  
As discussed previously, manually added research labels cannot be used to classify authors who have added LA to their public GS profiles. The data filtered through the two experiments (1 and 2, discussed in Sections 4.1 and 4.2) were then analysed in various forms, depending on the count of the publications for each author. A count of publications was maintained for both experiments side by side, which yielded some authors that had either no publications in experiment 1 but multiple or at least one publication in experiment 2, and vice versa. Therefore, there were authors in the final output aggregated file that had no publications in either experiment 1 or experiment 2. Further, for some in-depth analysis, filters were applied on each method to analyse the number of publications and the impact of one filter on the other. This textual analysis (experiments 1 and 2) of 4732 publications by 1236 authors with \"Learning Analytics\" in their public GS profile labels and top most cited publications, identified four unique clusters of researchers:  
* Specialist researchers;
* Occasional researchers;
* Early career researchers;
* Interested researchers. The definition of each category, based on the number of authors, papers, and average number of papers directly related to LA, is presented in Table 5.