Google analytics of a pilot study to characterize the visitor website statistics and implicate for enrollment strategies in Medical University

doi:10.21203/rs.3.rs-20012/v3

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Research article

Google analytics of a pilot study to characterize the visitor website statistics and implicate for enrollment strategies in Medical University

https://doi.org/10.21203/rs.3.rs-20012/v3

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published 01 Dec, 2020

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Background: Due to decreasing fertility rates in Taiwan, colleges and universities face significant difficulties in maintaining student attendance. This study aims to investigate students in the higher education market and explore their online behavior patterns in relation to university departmental websites to provide response strategies with the aim of potentially increasing enrollment.

Methods: We used Google Analytics (GA) tools to analyze two departmental websites from a medical university between February 1 and July 30, 2018. We also observed website patterns during relevant periods for three college admission routes (Stars Program, Personal Application, and Admission by Examination and Placement).

Results: Visitors were mostly between 18 and 24 years of age during the three visiting date ranges for the two departments analyzed. The visitor groups were mainly freshmen at the university, and secondarily their parents. The majority of visitors visited “Homepage,” “Subject Credits,” “Course Planning,” “Teacher Lineup,” and “Certificate of Subjects.” The overall timing of daily page views varied depending on academic events.

Conclusions: Results suggest that the department should consider enhancing the presentation of its featured courses on the webpage or distinguish their course characteristics from those of competing departments in the curriculum to ensure clear market segmentation. It is recommended that each department consider the obtainable data online and identify suitable high schools to visit to attract potential students and improve student willingness to choose this university.

Educational Philosophy and Theory

Google analytics

Higher education

Web performance

Enrollment

Institutional research (IR)

The field of institutional research (IR) was developed more than 50 years ago to support the improvement of postsecondary institutions through data-informed decision making and scholarly research in the United States [1]. Today, IR offices are rapidly growing in Taiwanese universities because of the importance placed on them by Taiwan’s Ministry of Education (MOE). Following the growth of the global Association for IR (AIR) and IR’s increasing presence in higher education worldwide, the Taiwan Association for IR (TAIR) was established in 2016 [2].

Colleges and universities in Taiwan have faced difficulties enrolling new students, mainly because of the decreasing fertility rate within the past 10 years. According to the news from Taiwan Business TOPICS, this “falling population squeezes Taiwan’s Universities.” Taiwan’s population is projected to peak at 23.61 million in 2021 and decrease thereafter [3]. The current fertility rate, an average of 1.17 children born to each woman, is insufficient to maintain the present population level. The potential impact of a declining population on the nation’s education system is expected to be enormous, particularly at the university level where a large gap already exists between supply and demand. The MOE estimates that the number of college students will decline by 40% to reach 723,000 by 2028 [3].

Google Analytics (GA), a free service provided by Google in November 2005, allows webmasters to access the content of webpages recorded by Google [4]. The purpose of GA is to enable website proprietors with limited budget, time, and expertise to collect user data through web analytics. Organizations can thus obtain relevant information to analyze user behaviors or traits and derive useful marketing intelligence.

In the past, research using GA has been applied to analyze business performance to inform enterprise management [5–9]. Budd [5] claimed that companies can develop marketing strategies that improve their business performance through the collection of website data by, for example, improving the ability of a company’s marketing strategy to reach a wider market [6]. Gunter and Onder [7] studied a tourism website in Vienna and applied GA analysis to explore website traffic data and predict travel demand. Plaza [8] claimed that the performance evaluation of travel websites is a key issue for the effectiveness of online marketing. The study analyzed the effectiveness of a website based on traffic including access behavior and session length, browser choice, and search engines. Gordon et al. [9] explored medical issues mainly using GA analysis to assess the effectiveness of call-to-action campaigns by measuring visits, visit duration, bounce rate, number of visits, visits to the most webpages, user features, and usage media.

Several studies have also applied GA for collecting information for academic institutions. For example, Fang [10] used GA to analyze the online visitor information of the Rutgers-Newark University Law Library. Librarians used different tools to create the website and found that a web-based interactive learning platform improved usability for the library’s visitors, facilitating the borrowing of books and increasing student usage of the school library collections. GA also provides information on where visitors come from, what pages they visit, how long they stay on each page, how deep into the site they navigate, where their visits end, and where they went from there [10]. Turner [11] used GA to develop a web-based messaging application that provides users with behavior, demographic variables, and technical information. Internet applications are used in university libraries to improve the efficiency of book use because they facilitate readers receiving book resources, which can improve satisfaction with the library. Braender et al. [12] applied web technology to the School of Management at the College of New Jersey (TCNJ) to promote students’ social issues and cultivate students’ ethics through an online learning community platform. Thus, using free GA technology, Braender et al. [12] were able to collect behavioral information about students using a learning platform and analyze which factors are important for influencing their learning environment.

This study used website data collected from students in the higher education market to gather network marketing intelligence from Taiwanese university websites with the aim of providing early response strategies to increase enrollment. We posed the question of what users are concerned about and how website information could be redesigned in advance to maximize visitor use and engagement with different departmental websites.

Research target

The chosen school is one of the three major medical universities located in central Taiwan, and currently has five colleges and 20 departments. The gender ratio of males to females is 39:61. The departments analyzed are affiliated with the School of Health Management; Department A is the Leisure and Sports Group and Department B is the Medical Hygiene Group. The annual enrollment quotas approved by the MOE are 40 and 45, respectively.

For entrance into these schools, students can choose from three routes of admission recognized by the MOE: (1) Stars Program (Stars), (2) Personal Application (PA), and (3) Admission by Examination and Placement (AE&P) [13]. Stars seeks to provide all public and private high school students across the country with an equal opportunity to attend college. The program utilizes the student’s grade point average from the first two years of high school as the admission criterion, in conjunction with a General Scholastic Ability Test (GSAT) score that has been specified by the college department. All candidates must be recommended by their high schools and there is a specified quota of students who are able to enter college through this program.

The PA process provides all high school students with an opportunity to apply to their preferred college or department. Students choosing this option first take the GSAT. After receiving their GSAT results, the students determine college application eligibility based on the criteria set by the colleges. If a student is qualified, the college will invite the student to participate in the second stage of the screening process. During this stage, students may be asked to take additional tests given by the department, prepare a portfolio, and attend interviews. The AE&P process is for students who either did not take part in the Stars or PA processes, failed to gain admission through either of these routes, or were dissatisfied with their results. Students taking part in the AE&P process must take the Advanced Subjects Test (AST). After receiving their results, the students fill out a preference list indicating their interest in specific colleges and departments.

Definition of the enrollment stages

For the Personal Application (PA) route, the study divides the individual application period for the university into two parts (before and after PA) to compare results. Before PA considers the time period before the deadline for the candidate’s registration on February 26, 2018. During this stage, candidates used the internet to seek information about the department. After PA refers to when the results of the selection committee were announced on March 23, 2018. This stage includes the test dates for designated projects within each school, which occurred between April 11 and 29, 2018. During this period, students may use the website to become familiar with the department, teachers, and the future directions of the department. The definition of this period was based on the individual’s application schedule for admission to the university (Appendix Table 1).

For the designated enrollment period for the AE&P route, the study is divided into three parts (Appendix Table 2). The three periods for admission by AE&P were (1) July 19 to 23, 2018, corresponding to the announcement of grades for AST. During this period, the students filling the enrollment quotas, the registered elective stand-alone editions, and the designated subject examinations are announced. (2) July 24 to 28, 2018, wherein students fill out a preference list on the website (PLW), register on the network, and electives are distributed and (3) July 29 to August 7, 2018, during which the announcement of placement results (APR) occurs and the Admission Notice for University Entrance Examinations is announced.

Google Analytics (GA)

GA was used in this study to obtain site usage data across five months (from February 1, 2018 to July 30, 2018). Google collects and links actions by installing JavaScript code on the webpage. By analyzing the information collected by GA regarding visitor behavior information, the information that visitors expect to receive through the faculty page can be summarized, thus providing a reference for the department to better design the webpage and plan the curriculum. By understanding the content of the information that is of interest to online visitors, the department can explore and better meet the requirements of potential students. GA provides data for the following metrics: the number of page views, duration of visit, pages most often visited, depth of the visit, user demographics (e.g., gender, age range, city, and country), and user media (e.g., devices).

Department characteristics

Figures 1A and 1C show the student enrollment and admission options for 2014–2018 in Departments A and B. The results show that the numbers of people in Departments A and B increased from 33 to 45 and from 39 to 53, respectively, over the five year period. A difference from the annual approved quota was observed because this study did not consider independent enrollment, international exchange students, or transfer students, and because the results were analyzed based solely on the data pertaining to national students. Figures 1B and 1D show the overall percentage of students who were enrolled through Stars, PA, and AE&P in the two departments. The Stars, PA, and AE&P admissions channels contributed 9%, 35%, and 56% of the total enrolled students, respectively, for Department A and 23%, 33%, and 44% of the total enrolled students, respectively, for Department B. Both departments have thus adopted university assessment as the main enrollment channel.

Figure 2 presents the geographic distribution of students. The results show that the majority of students enrolled in Department A were from a metropolitan area with 16%–20% of students coming from Taipei city, New Taipei city, and Taichung city and 11%–15% coming from Taoyuan city. For Department B, 16%–20% of the students came from Taipei city and Taichung city, and 11%–15% of the students came from Taoyuan city and Kaohsiung city.

Website profile during PA stage

Figures 3A, 3C, 3E, and 3G show the page view time-series before (February 26 to March 22, 2018) and after PA (March 23 to May 17, 2018) in the two departments. The average daily browsing times Before PA were slightly higher than those After PA. The average page views per day during whole periods were 39 and 27 times for Departments A and B, respectively. Department B showed a similar trend and the page views per day are 87 and 35 times Before and After PA, respectively (See Appendix 3 for more details).

The percentage of students visiting each link on the page (Figures 3B, 3D, 3F, and 3H) was also recorded. The results from Department A show that the Withdrawal webpage received the most visitors with 36% and 49% Before and After PA, respectively, followed by “About Us” and “Courses and Credits.” The Withdrawal webpage means that web pages that were not available during data analysis because the department had removed them from the website. The unavailable pages may include past news, announcements, or invited lectures, and even website capacity is limited. In contrast, the results for Department B showed that visitors mostly visited the “About Us” link during both the Before and After PA phases with 44% and 42% of visits, respectively, followed by “Faculty” (26%) and “Programs” (25%). Overall, the timing of the daily page views varied depending on admissions events; for example, the highest number of page views was observed on April 14, 15, 21, and 22, 2018, which corresponded with interview dates at the university.

Website profile during the AE&P stage

For Department A, the number of visitors to “Course Information” and the “Certificate of Subjects” links peaked following the announcement on July 19 (Figure 4). Before registering for an elective, visitors to the website first looked for an overview of the course information and subject credits. Online registration and distribution of electives was from July 24 to 28, 2018. During this period, visitors began to navigate to the “Teachers” page. Results indicate that the “Faculty” page is a priority item for registering for electives especially at the end of the registration day, which saw the most traffic, followed by the “Program” page. It is recommended that the department update the course information before announcing the assessment results. The department’s curriculum planning can be related to the student’s choice of electives (Figures 4A-D). The results for Department B showed that the average daily number of visits during the three stages of the AE&P period was 89, 102, and 21, respectively. The second stage received the highest number of overall page views, with “About Us” receiving the largest number of visitors among the three stages followed by the “Program” plan (Figures 4E-H).

Webpage interaction information

Table 1 shows the depth of the visits from the university’s website. For Department A, the homepage received the most initial clicks by visitors, accounting for 81% and 87% of the total number of people Before and After PA, respectively. From the homepage, 47% of visitors clicked on “Courses and Credits” and 29% clicked on “About Us.” From there, most of the visitors returned to the homepage, accounting for 51% and 63% Before and After PA, respectively, before selecting either “Program” (27%) or “About Us” (36%) Before and After PA, respectively. We speculate that after the visitors confirm their admission to the school, they want to have a more complete understanding of the department. Results showed that the number of visitors remaining after visiting three pages (“Courses and Credits”, “About Us”, and “Program”) for both periods dropped to 12%. The results for Department B show that homepage again received the most initial clicks by visitors to the website, accounting for 68% and 63% of the total number of people Before and After PA, respectively. The total views Before PA decreased from 1,750 to 248, leaving only 14% of visitors, and the total views After PA decreased from 1,823 to 202, leaving 11% of visitors, which suggests that users leave after obtaining the necessary information (Table 1).

Table 2 presents the page views by visitors for each of the different subject webpages. The majority of visitors for Department A started on the homepage before selecting “Courses and Credits,” returning to the homepage, and then visiting the Program pages. The results for Department B are slightly different between the AST, PLW, and APR stages. After the results are announced, the majority of visitors click on the homepage (57%), followed by “About Us” (40%), the homepage again (25%), and then “Courses and Credits” (32%). For the network registration and distribution of electives (PLW) stage, visitors started at the homepage and then clicked on “Program” and “Faculty.” After interacting with three pages, the total views decreased from 410 to 56, leaving only 14% of visitors, while the remaining percentages were 21% and 10%, respectively.

In Department A, the majority of visitors visited “Homepage,” “Subject Credits,” and “Course Planning.” In Department B, the visitors started at “Homepage” and moved to the “Introduction, Teacher Lineup” before returning back to the “Homepage” and then moving to the “Certificate of Subjects.” After interacting with three webpages, the page views for Departments A and B were 12% and 13% (Table 1), and 56% and 15% (Table 2), respectively, indicating that the visitor has obtained the required information and left the page.

User characteristics

Table 3 describes the user information and characteristics. Visitors were mainly between 18 and 24 years of age during the three time periods for Department A, with less in the next age bracket (25–34 years old). The visitor groups were mainly freshmen at the university, followed by their parents. In terms of browsing tools, the visitors used desktops followed by smart phones; thus, the design of the webpage should be able to meet the usage requirements of desktop computers as well as smart phones. Among the visitors to Department B, there were slightly more women than men and they were mainly 18–24 years old, followed by 45–54 years old. Other potential uses of GA include visitor geographic and demographic data, which offering the possibility to design websites with a preference for women, as most visitors were female.

Average page time

Figure 5 shows the average time spent on the site in seconds before and after PA. For Department A, visitors spent most of their time at the “Faculty” page, while those for Department B spent most of the time visiting the admission list for graduate students. These findings suggest that some visitors are students of the institute.

The average time spent on the site in seconds is shown in Figure 6. The results for Department A show that the total browsing time is longest for the Admissions Announcement for the master’s class followed by the “HACCP Course Admissions Guide” in the five days before the results of the AST were announced. The overall browsing time during the second phase (PLW) showed that visitors spent most of the time browsing the license information. In the third phase (APR), the visitors spent most of the time browsing the Study Program, followed by the Food Technician Exam. Overall, the visitors to the website sought information relating to the selection of electives on the “Review Information” and “Training Qualifications” pages. Therefore, the list of examinations should be kept up to date on the website. Visitors also viewed the teacher’s professional background and items of concern. The results for Department B showed that visitors stayed for more than five minutes when viewing the latest news, enrollment status (double major and adjunct), the subject and credits table, and the graduate flow tracking (five-year) survey.

The average time visitors stayed on the webpage during Department B’s recommendation stage is about twice that of the assessment period, while findings for Department A were directly inverse (Figures 5 and 6). The two pages where visitors stayed the longest were different. Department A is a resting and kinematics group with a focus on five-year follow-up surveys and subject credits. Department B is a medical and health group. The longest stay times were on the “Investigation List, Graduation Topics, and Master’s Degrees Credits Tables,” indicating that students are seeking information on the university’s referral stage and entrance exam. These visitors are likely candidates for the master’s class. In the assessment phase, visitors to the “Certificate of Subjects (University)” and “Graduate Flow Tracking” stayed the longest.

Visitor living area

The locations of visitors to Department A were similar during the recommendation and assessment stages, coming mainly from Zhongli, Taoyuan, and Changhua. The majority of visitors to Department B came mainly from Taoyuan City and Changhua County.

This study uses GA to explore and gather marketing intelligence from students and to understand their potential network behavior patterns while visiting university department websites for the purposes of facilitating website use and increasing enrollment.

In average daily page views, the results for Department A show that for each department that includes the interview in their scores, students collected information on and strengthened their understanding of the department before the interview. Assessing students’ choices of electives will affect the credits and course arrangements. This research suggests that the department may enhance the presentation of its featured courses on the webpage or distinguish its characteristics from those of other competitive departments in the curriculum to ensure a clear market segmentation. The results of Department B show that the highest level of network traffic occurred when students checked their scores at the end of the registration period and received their assessment results before the distribution of electives. The department can determine what to promote on their website during this period, especially for the descriptions of the “Introduction to the Department” and “Course Planning” links.

GA can instantly capture student location information for each department after application results are distributed. This is contrary to traditional means, which typically require students to report their location to the school after starting. The School Affairs Research Office has requested the Information Room to assist the department in installing GA code. It is recommended that after results are distributed, each department evaluate the information online and identify suitable high schools to visit to identify potential students and improve student recruitment to the university. However, the way to confirm our results is to compare the degree of matching between GA user information and actual admissions in each department. From the “visitor living area” paragraph and Figure 2, we observed that “visitor living area” is partially repeated in each department, especially in Taoyuan city. This may indicate that some website visitors are parents or siblings of students or that some students were not admitted.

This study has the following limitations: (1) The data metrics used are limited to those provided by GA. The variables we collected included “page views,” “webpage interactive information,” “average time on site,” “page subject,” and “user characteristic: gender, age, location, and devices.” All analysis results for these variables are presented in the text, and no continuative analysis of other variables is possible. (2) The same visitor may click multiple times, which would increase the reading rate. During the data collection period, inquiries were made about the scheduling of the Master’s class. However, while some of the readers may be candidates for the Master’s class, this is unlikely to be the case for all potential students at the university department. (3) Considering the school administrative unit, the department’s processes and operations, and the influence of enrollment or important activities pertaining to other master’s programs, it is still necessary to consider how to obtain representative data and determine other factors that cannot be summarized.

In our study, we found that the most concerned pages are “Homepage,” “Subject Credits,” “Course Planning,” “Teacher Lineup,” and “Certificate of Subjects.”. Results suggest that the department should consider enhancing the presentation of its featured courses on the webpage or distinguish their course characteristics from those of competing departments in the curriculum to ensure clear market segmentation. It is recommended that each department consider the obtainable data online and identify suitable high schools to visit to attract potential students and improve student willingness to choose this university.

GA: Google Analytics; IR: Institutional Research; PA: Personal Application; AE&P: Admission by Examination and Placement; GSAT: General Scholastic Ability Test score; AST: Advanced Subjects Test; PLW: Preference List on Website; APR: Announce Placement Results

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed in this study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Author contributions

Dr. YF Tsai designed the work and interpreted data. Dr. SC Chen analyzed and wrote the manuscript. TCY Tsao and KH Lue reviewed, commented, and discussed the manuscript. All authors have read and approved the manuscript.

Funding

Funding for this project is derived from the Chung Shan Medical University (F107N0008).

Acknowledgements

The results of this research were obtained thanks to the colleagues and students of the Library and Information Division of Chung Shan Medical University, who provided information and assistance. We would like to thank Uni-edit (www.uni-edit.net) for editing and proofreading this manuscript. We would like to thank Uni-edit (www.uni-edit.net) for editing and proofreading this manuscript.

Authors’ information

¹Department of Public Health, Chung Shan Medical University, Taichung 40201, Taiwan, ROC. ²Department of Family Medicine, Chung Shan Medical University Hospital, Taichung 40201, Taiwan, ROC. ³School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan, ROC. ⁴Department of Medical Quality Management, Chung Shan Medical University Hospital, Taichung 40201, Taiwan, ROC. ⁵School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan, ROC. ⁶Department of Pediatrics, Chung Shan Medical University Hospital, Taichung 40201, Taiwan, ROC. ⁷Department of Health Policy and Management, Chung Shan Medical University, Taichung 40201, Taiwan, ROC. ⁸Department of Medical Management, Chung Shang Medical University Hospital, Taichung 40201, Taiwan, ROC.

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Table 1 The depth of visit is a measure of visit quality. We listed the page views of the different page subjects for the initial, first, second, and third pages visited. Before PA was from February 26 to March 22, 2018; After PA was from March 23 to May 17, 2018

Department A			Department B
Page subject	Page view (%)		Page subject	Page view (%)
Page subject	Before PA	After PA	Page subject	Before PA	After PA
Visit depth (initial pages)
Main website	679 (81%)	1,221 (87%)	Main website	1,185 (68%)	1,155 (63%)
About us	79 (10%)	77 (6%)	About us	350 (20%)	314 (17%)
Courses and credits	31 (4%)	21 (2%)	News and events	116 (7%)	204 (11%)
Program	21 (3%)	18 (1%)	Faculty	99 (6%)	150 (8%)
International conference and exhibition program area	18 (2%)	36 (3%)
HACCP	12 (1%)	35 (3%)
Total views	840	1408	Total views	1750	1823
Visit depth (first pages)
Courses and credits	132 (47%)	80 (26%)	About us	302 (42%)	80 (21%)
Program	64 (23%)	58 (19%)	Program	199 (28%)	58 (15%)
About us	45 (16%)	89 (29%)	Faculty	101 (14%)	89 (24%)
Main website	24 (9%)	28 (9%)	Aim	53 (7%)	28 (7%)
Brief history	18 (6%)	53 (17%)	News and events	34 (5%)	61 (16%)
			Admission	25 (4%)	61 (16%)
Total views	283	308	Total views	714	377
Visit depth (second pages)
Main website	119 (51%)	192 (63%)	Main website	124 (29%)	134 (37%)
Courses and credits	56 (24%)	25 (8%)	Courses and credits (2017)	81 (19%)	40 (11%)
About us	32 (14%)	35 (12%)	Courses and credits (past)	73 (17%)	43 (12%)
Program	17 (7%)	23 (8%)	Program	62 (15%)	39 (11%)
Brief history	9 (4%)	29 (10%)	About us	52 (12%)	65 (18%)
			Faculty	31 (7%)	44 (12%)
Total views	233	304	Total views	423	365
Visit depth (third pages)
Program	27 (27%)	30 (18%)	Courses and credits (2017)	86 (35%)	54 (27%)
About us	24 (24%)	60 (36%)	Program	66 (27%)	48 (24%)
Courses and credits	22 (22%)	20 (12%)	About us	36 (15%)	41 (20%)
Main website	21 (21%)	30 (18%)	Courses and credits (past)	34 (14%)	20 (10%)
Features of the department	5 (5%)	25 (15%)	News and events	26 (10%)	39 (19%)
Total views	99	165	Total views	248	202

Table 2 We listed the page views of the different page subjects for initial, first, second, and third pages. The three phases for the duration of the AE&P were July 19 to 23, 2018 (announcement of grades for AST), July 24 to 28, 2018 (fill out a PLW), and July 29 to August 7, 2018 (APR)

Department A				Department B
Page subject	Page view (%)			Page subject	Page view (%)
Page subject	AST	PLW	APR	Page subject	AST	PLW	APR
Visit depth (initial pages)
Main website	27 (44%)	0 (0%)	76 (67%)	Main website	234 (57%)	293 (64%)		208 (58%)
About us	17 (27%)	16 (30%)	7 (6%)	About us	92 (22%)	73 (16%)	65 (18%)
Program	11 (18%)	21 (39%)	10 (9%)	Program	46 (11%)	58 (13%)	11 (3%)
Admission	3 (5%)	0 (0%)	2 (2%)	Courses and credits	13 (3%)	7 (2%)	5 (1%)
Courses and credits	2 (3%)	9 (17%)	4 (4%)	Admission	13 (3%)	16 (4%)	40 (11%)
Faculty	2 (3%)	5 (9%)	7 (6%)	Faculty	12 (3%)	10 (2%)	29 (8%)
Brief history	0 (0%)	3 (6%)	7 (6%)
Total views	62	54	113	Total views	410	457	358
Visit depth (first pages)
Courses and credits	19 (29%)	32 (34%)	20 (20%)	About us	63 (40%)	58 (32%)	17 (16%)
Program	16 (24%)	30 (32%)	26 (26%)	Program	44 (28%)	68 (37%)	18 (17%)
Brief history	16 (24%)	15 (16%)	15 (15%)	Courses and credits	13 (8%)	14 (8%)	2 (2%)
Faculty	10 (15%)	10 (11%)	19 (19%)	Main website	12 (8%)	8 (4%)	6 (6%)
Admission	5 (8%)	6 (7%)	19 (19%)	Faculty	11 (7%)	20 (11%)	52 (48%)
				Career information	6 (4%)	16 (9%)	0 (0%)
				New student	9 (6%)	0 (0%)	14 (13%)
Total views	66	93	99	Total views	158	184	109
Visit depth (second pages)
Program	25 (38%)	9 (15%)	11 (13%)	Main website	24 (25%)	38 (27%)	31 (53%)
Courses and credits	12 (18%)	16 (27%)	11 (13%)	Program	20 (21%)	27 (19%)	7 (12%)
Brief history	14 (21%)	5 (8%)	9 (10%)	Courses and credits (past)	17 (18%)	29 (21%)	7 (12%)
Faculty	10 (15%)	2 (3%)	11 (13%)	Courses and credits (2017)	16 (17%)	25 (18%)	2 (3%)
Main website	0 (0%)	23 (38%)	41 (47%)	About us	13 (14%)	15 (11%)	6 (10%)
Features of the department	5 (8%)	5 (8%)	5 (6%)	Faculty	5 (5%)	5 (4%)	6 (10%)
Total views	66	60	88	Total views	95	139	59
Visit depth (third pages)
Program	11 (33%)	10 (37%)	11 (20%)	Courses and credits (2017)	18 (32%)	38 (40%)	7 (20%)
Courses and credits	7 (21%)	3 (11%)	4 (7%)	Program	14 (25%)	21 (22%)	5 (14%)
Main website	4 (12%)	6 (22%)	12 (22%)	Courses and credits (past)	9 (16%)	19 (20%)	6 (17%)
License information	4 (12%)	7 (26%)	6 (11%)	About us	9 (16%)	3 (3%)	3 (9%)
Faculty	4 (12%)	8 (30%)	12 (22%)	Faculty	3 (5%)	9 (10%)	5 (14%)
Brief history	3 (9%)	2 (7%)	10 (18%)	Admission	3 (5%)	4 (4%)	9 (26%)
Total views	33	36	55	Total views	56	94	35

Table 3 User Information and Characteristics for Department A and Department B

Variables	Department A
Variables	Before PA	After PA	AST	PLW	APR
Gender	ND	ND	Female (65%)	Female (60%)	Female (75%)
Age	ND	ND	18–24 years (28%) 45–54 years (22%)	18–24 years (34%) 45–54 years (31%)	18–24 years (50%) 25–34 years (47%)
Location	Taoyuan city (44%) Changhua county (30%)	Taoyuan city (28%) Taipei city (13%) Changhua county (12%)	Taoyuan city (37%) Changhua county (14%) Hsinchu county (20%)	Taoyuan city (36%) Changhua county (16%) Tainan city (10%)	Taoyuan city (37%) Changhua county (11%)
Devices	Desktop (55%) Mobile (43%)	Desktop (55%) Mobile (43%)	Desktop (61%) Mobile (38%)	Desktop (57%) Mobile (40%)	Desktop (63%) Mobile (44%)
	Department B
Gender	ND	ND	Female (58%)	Female (57%)	Female (54%)
Age	ND	ND	18-24 years (46%) 45-54 years (33%)	18-24 years (55%) 45-54 years (28%)	18-24 years (49%) 25-34 years (27%)
Location	Taoyuan city (39%) Hsinchu county (11%)	Taoyuan city (31%) Hsinchu county (14%) Changhua county (13%)	Taoyuan city (52%) Changhua county (6%)	Taoyuan city (42%) Changhua county (9%)	Taoyuan city (55%) Changhua county (11%)
Devices	Mobile (50%) Desktop (48%)	Desktop (53%) Mobile (44%)	Desktop (50%) Mobile (47%)	Desktop (56%) Mobile (41%)	Desktop (53%) Mobile (44%)

Appendix0923.docx
Appendix 1. Individual Application Project and Date Description (University Selection Admission Committee) Appendix 2. Timeline for Assessment and Distribution (University Entrance Examination Center) Appendix 3. Time-series of page views before (February 26 to March 22, 2018) and after PA (March 23 to May 17, 2018) in the two departments.

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published 01 Dec, 2020

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Editorial decision: Minor revision
19 Oct, 2020
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Reviewer #1 agreed at journal
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Google analytics of a pilot study to characterize the visitor website statistics and implicate for enrollment strategies in Medical University

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