Using Google Analytics to Measure Student Statistics: The Case of Blended Learning Course Websites Nuth Otanasap Department of Computer Science Southeast Asia University 1

1) INTRODUCTION Learning analytics (LA) [1] has become a special topic LA can assist an institution significantly; resource allocation, learner success, and financial purpose. [8] Compared learning analytics and academic analytics are related to optimization of learning and teaching by themselves Educational analytics are related to optimization of learning and teaching activities 2

1) INTRODUCTION (CONT.) [1] More data collected most of them are used to defining requirements rather than to define important questions, and most of them are not used LA as a high cost with expensive tools or data collection methods rather than as a capital, that mainly obstacle. 3

1) INTRODUCTION (CONT.) Combine the existing process to developing models to designing and delivering alerts, dashboards, recommendations and reports, are needed. Assessment of student learning without prepared script is most interested but student job can invent in ways that too complex to be traced by human. 4

1) INTRODUCTION (CONT.) Blikstein [2] has described an automated technique to assess, analyze and visualize students learning. Employ different quantitative techniques to separate behaviors of learner and classify them. 5

1) INTRODUCTION (CONT.) Examples of using learning analytic [8]: - Indicate risk students that need interferences to increase retention. - Suggest to students in relationship between learning activities and materials. - Recognize for need of result measurement that relate to pedagogic improvements. 6

1) INTRODUCTION (CONT.) Examples of using learning analytic [8]: - Design new courses. - Identify high or low performance teacher and who need advice in teaching methods. - Improve in the student admission process. 7

1) INTRODUCTION (CONT.) Website traffic measurement [6] and user navigation analysis are common task for provider Watched items alter from simple to complex statistics and extensive analysis of the navigation behavior of website users How many visitors per day and why do visitors quit before checkout Information can be used to improve user satisfaction 8

RNLL [3] Used several methods to solve what they visitors are looking for on website. 1) INTRODUCTION (CONT.) Used GA to track visitors’ behaviors, and recognize the inspirations behind their information-seeking. 9

GA visually reports offered information on [3] Where visitors came from What pages they visited How long they stayed on page How deep into the site they navigated Where their visits ended Where there went from there 1) INTRODUCTION (CONT.) 10

Made changes to website and compared web usage data from before and after the changes, predicating that website was enhanced [3] 1) INTRODUCTION (CONT.) 11

“Using GA can be rather easy and simple” Using G A on three blended learning course websites Department of Business Computer, Southeast Asia University OBJECTIVE 12

Questions: - How are the websites entered by students? - What content is used by students? - How frequently do students enter to blended learning course websites? - What do we know about the students? - What devices were used to visit course websites? OBJECTIVE 13

TOC 1) INTRODUCTION 2) GOOGLE ANALYTICS 3) MATERIALS AND METHODS 4) RESULTS 5) DISCUSSION 6) CONCLUSIONS 14

2) GOOGLE ANALYTICS [4] (GA) Provided by Google Produces detailed statistics (traffic of website, sources of traffic, and measures conversions) Track visitors from all referrers, search engines and social networks, direct visits and referring sites Displays advertising, pay-per-click networks, marketing and digital collateral 15

2) GOOGLE ANALYTICS [4] (GA) Most powerful digital analytics solutions, free for anyone to use, and most widely used website statistics service GA may be applied as a statistical tool for education researchers easily 16

3) MATERIALS AND METHODS 3.1) Website characteristics 3.2) Measurement with Google Analytics 3.3) Key performance indicators 3.4) Data analysis 17

3.1) Website characteristics 3.1.1Electronic Commerce course (EC-S56) websites (summer semester, 8 Apr - 4 Jun 2014, 2013 ACAD ) used learning process from research result of “student-based blended learning using in-class approach and internet applications, project-based approach [5]” Home Page, Group Assignment, Individual Assignment, Quiz, Score Report, Group Assessment, Learning Contents 18

3.1) Website characteristics 3.1.2Electronic Commerce course websites (EC-156) (first semester, 18 Jun - 4 Oct 2013, 2013 ACAD) used learning process from previous research result [5] “ 61” included contents same as EC-S56. 19

3.1) Website characteristics 3.1.3System Analysis course website (SA-256) (second semester, 5 Nov Feb 2014 ACAD) used normal blended learning approach Included: Home Page, Individual Assignment, Quiz, Score Report, and Learning Contents “ sign/sa562” 20

3.2) Measurement with Google Analytics sau.ac.th subdomain from 18 Jun 2013 to 4 Jun

3.3) Key performance indicators Table 1: Definition of the KPI [4] used in this study KPI Users Bounce Rate Page Pages/Session Pageviews Avg. Time on Page Sessions Avg. Session Duration % New Sessions Unique Pageviews %Exit 22

3.4) Data analysis Main analysis was done using GAs Integrated with the results of three websites Based on the results provided directly by GAs 23

3.4) Data analysis Each of the web pages was classified in categories: – Home page – Group Assignment – Group Assessment – Individual Assignment – Quiz – Score Report – Learning Contents 24

3.4) Data analysis Student visited number was summed together inside each category and scaled with the total number Result was a distribution of visits by content type for each study website Top 100 referring sites were listed with the number of visits and time on site Each of websites was categorized by their main interest category 25

3.4) Data analysis Category was decided subjectively by visiting each of the referring sites Time on site was weighted average of time on site classified by the main interest of the referring site 26

3.4) Data analysis Number of visits was used for counting a blended visit ranked by the number of visits separately for each study site; then the study sites were blended. Same categorizing of the main interest category was used also for the analysis of the bounce rates in the EC-S56, EC-156 and SA- 256 sites. 27

3.4) Data analysis Comparison of the devices, the KPI were first estimated for each OS OS were categorized according to their main usage desktop, mobile, and tablet. Traffic of sources and student origins were measured 28

3.4) Data analysis Used in the estimation of weighted averages Number of visits was used as the weighting factor. OS categories used for indicating the device were not fully inclusive as some of the OS could be used in several types of devices and thus the results were facilitated and demonstrative 29

4) RESULTS 4.1) General information 4.2) How are the websites entered by students? 30

4.1) General information Key performance indicatorEC-156SA-256EC-S56 Users 6911, Bounce rate 26.05%25.37%17.36% Pages / Session Page views 11,43013,3434,539 Average time on page 0:02:140:02:290:02:25 Unique Pageviews 6,9029,1162,937 Sessions 2,3034,0051,031 Avg. Session Duration 0:08:520:05:470:08:12 % New Sessions 29.74%28.21%26.19% % Exit 20.15%30.02%22.71% New Visitor 29.80%28.40%26.40% Returning Visitor 70.20%71.60%73.60% 31 Table 2: Key performance indicators of the study blended learning websites.

Figure 1: Example of the information provided by Google Analytics dashboard from the EC-S56 class website. 32

4.2) How are the websites entered by students? Table 3: Traffic sources of the study blended learning websites. Source EC-S56EC-156SA-256 sites.google.com52.85%57.78%56.91% accounts.google.com40.21%10.11%11.28% facebook.com6.32%3.88%10.14% m.facebook.com0.41%0.83%0.76% google.com0.21%27.15%20.80% mail.google.com-0.25%0.11% 33

Table 4: Traffic sources by different types of channel. Default Channel Grouping EC-S56EC-156SA-256 Referral87.29%62.31%81.82% Social6.30%4.04%10.01% Direct4.75%5.64%7.47% Organic Search1.65%2.69%0.70% (not set)-25.31%- 34

4.3) What content is used by students? Distribution of page views of the most commonly visited web pages was used to indicate the content traced by students. Average times spent on the web page indicate show interesting the content was to the students (Table 5). 35

Table 5: Distribution of pages viewed and the average time on the page by the content type of the web pages for study websites. EC-S56EC-156SA-256 Site Avg:% of Total:0:02:25% of Total:0:02:14% of Total:0:01:14 Page4,539 (100%) Avg. Time on Page 11,430 (100%) Avg. Time on Page 818 (100%) Avg. Time on Page Home Page 1,074(23.66 %) 0:00:45686(6.00%)0:00:4843(5.26%)0:01:11 Group Assignment 655(14.43%)0:04:19501(4.38%)0:04:12-- Individual Assignment 593(13.06%)0:03:51338(2.96%)0:03:34158(19.32%)0:03:01 Quiz314(6.92%)0:04:46481(4.21%)0:05:1426(3.18%)0:01:16 Score Report155(3.41%)0:01:56446(3.90%)0:01:0038(4.65%)0:01:11 Group Assessment 150(3.30%)0:07:01332(2.90%)0:05:16-- Learning Contents 123(2.71%)0:02:05 2,062(18.04 %) 0:04:2551(6.23%)0:00:58 36

4.4) How frequently do students enter to blended learning course websites? Students’ frequently visited times were used to indicate whether the content provided and the layout of the websites were found to be satisfactory and trustworthy by the students. 37

Figure 2: Distribution of students’ frequently visited times 38

4.5) What do we know about the students? Used the average time spent on page to indicate how important a content our course websites were to the different student groups. Who spent most of the time on the study blended learning course websites seemed to be interested in contents and activities. 39

Figure 3: Distribution of students’ time on site 40

Table 6: Pages per visit by different cities. CityEC-S56EC-156SA-256 Bangkok76.72%75.77%80.40% (not set)17.26%17.02%15.18% Nakhon Pathom2.72%2.17%2.40% Mueang Samut Sakhon District 2.13%4.26%1.42% 41

4.6) What devices were used to visit course websites? Table 7: Pages per visit by different types of device. Device Category EC-S56EC-156SA-256 desktop 77.11%71.82%77.20% mobile 19.50%14.81%13.91% tablet 3.39%13.37%8.89% 42

Table 8: Pages per visit by different types of OS. Operating System EC-S56EC-156SA-256 Windows77.11% 63.64%76.95% iOS10.77%13.71% Android12.12%33.59%9.01% Macintosh0.25% Windows Phone 2.77%0.07% 43

5) DISCUSSION All the study websites that used and not used learning process from previous research had a significant number of visits and difference behaviors. Access via referral method can be considered a good way for the blended learning website There are two kinds of students, first who just come through and leave after a few seconds and second who spend various minutes upon arriving. 44

5) DISCUSSION When the students found the needed content, they spent more time on such web pages. Group assignment of EC-S56 and EC-156 that using learning process compiled by previous research has more average time on page. Referral traffic was the most ordinary way of entering all the study websites also. 45

5) DISCUSSION Students’ frequently visited times were used to indicate whether the content provided Layout of the websites were found to be satisfactory and trustworthy by the students. 30 % of students visit the website only once but about 20% visited > 9 times. 46

5) DISCUSSION 1: visit website for less than 10 sec. 2: spent > 60 sec. Desktop: most devices used to visit the study website ( % of visits) Mobile students: medium on the study websites (13.91 – 19.50% of visits) Tablet device: very low (3.39 – 8.89% of visits) quite insignificant. 47

6) CONCLUSIONS GA is a useful tool that gives important information in little cost and small exertion GA = Piwik [7] & Yahoo! Web Analytics Tracking e-Learning site traffic and learning analytics should be routine for every e-Learning, making student behavior clearer Made suitable e-Learning sites for online or blended students Offer better online contents for learner 48

REFERENCES [1] Bichsel, J. (2012). Analytics in higher education: Benefits, barriers, progress, and recommendations. EDUCAUSE Center for Applied Research. [2] Blikstein, P. (2011, February). Using learning analytics to assess students' behavior in open-ended programming tasks. In Proceedings of the 1st international conference on learning analytics and knowledge (pp ). ACM. [3] Fang, W. (2007). Using google analytics for improving library website content and design: a case study. [4] Google Analytics (2014), Retrieved from [5] Nuth Otanasap. (2013) Student-based Blended Learning Using In-class Approach and Internet Applications, Project-based Approach. Proceeding of National E-Learning Conference Bangkok. THA [6] Pakkala, H., Presser, K., & Christensen, T. (2012). Using Google Analytics to measure visitor statistics: The case of food composition websites. International Journal of Information Management, 32(6), [7] Piwik (2014), Retreived from [8] van Harmelen, M., & Workman, D. (2012). Analytics for Learning and Teaching. CETIS Analytics Series, 1(3). 49

Thank You 50