SlideShare una empresa de Scribd logo
1 de 16
Descargar para leer sin conexión
EFL Student’s Ability for Website
Information Comprehension and
Perceptions on Website Usability
s1170173
Makoto Yoshida
Supervised by
Prof. Debopriyo Roy
Overview
• Abstract
• Research Workflow
• Question what We Set (to Participants)
• Results and Discussion (1-9)
• Conclusion and Future Work
Abstract
The purpose of this study is to observe and consider the impression of L2
readers during website analysis in target language (English). At first,
participants completed an extensive website analysis using established web
design models with a closed-ended website information comprehension
questionnaire (testing readers’ understanding of the text content in the
website). In the second stage, they answered three usability questionnaires
(QUIS, CSUQ and MPRC questionnaires), recording their impression about
the interface, navigational efficiency, content comprehension, learning
aspects of the interface, basic comfort level with text-graphics content etc. I
used Pearson Correlation and non-parametric Friedman Test to analyze
user data. The self-reports on the questionnaires (QUIS, CSUQ and MPRC)
provided a general outlook about the website content and navigation and
might not have been specific to the accuracy scores (related to the text
content of the website).
This exploratory analysis as discussed in this article could help us obtain
initial data on how EFL readers in a typical context as this would perform
with English website information and the type of impression they have
about the website.
Research Workflow
• 59 participants completed the website information comprehension
questionnaire. Accuracy scores on the questionnaire were measured.
•  Following the interaction with the website, readers completed the three
questionnaires on self-reporting.
• Collection their answers
• Data Analysis
• Consider the Results
Questionnaires
The website information comprehension questionnaire focused on the
efficiency with which L2 readers are able to search through
information from the Belize tourism website. The focus at this stage
was on readers’ ability to navigate through the pages, based on cues
from the questionnaire. The questionnaire was designed on the basis of
the following:
1.  multiple-choice questions asking readers to accurately pinpoint the
information available in the website.
2.  readers’ ability to sequence order steps in the correct order when
searching for an information from the webpage. (Q1 with 8 steps to
be correctly ordered).
10 questions were asked and each question (except Q1) could be scored
as 1(correct) or 0 (incorrect) (binary scale). The total accuracy scores
for each student were measured.
QUIS, CSUQ and MPRC questionnaires are standard software
usability questionnaires used for self-reporting users’ preference for
the website.
Results and Discussion
Ques%on N Minimum Maximum Mean Std.	
  Devia%on
Q1
59
0 10 8.66 2.496
Q2 0 2 1.97 .260
Q3 2 2 2.00 .000
Q4 0 2 1.19 .991
Q5 0 2 1.19 .991
Q6 0 2 1.73 .691
Q7 0 2 1.90 .443
Q8 0 2 1.64 .663
Valid	
  N
(List	
  wise) 	
   	
   	
   	
  
Table 1: Descriptive Statistics for the 8 Website Information Comprehension Questions
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7
MeanAccurateScore
Question Number (1 = Q2; 2 = Q3; 3 = Q4; 4 = Q5; 5 = Q6; 6 = Q7; 7 = Q8)
Mean Accuracy Score (Maxium Value =
2)
Mean Accuracy Score
(Maxium Value = 2)
Figure 1. Mean Accuracy Scores for Q2 – Q8 (Wrong Answer = 0; Correct Answer = 2)
Results and Discussion 2
Ques%on	
  
Pearson	
  
Correla%on	
  
Sig.(2-­‐
tailed)	
   N=59	
  
Q2-­‐Q7	
   	
  	
  .567**	
   .000	
  
59	
  
Q2-­‐Q8	
   .328*	
   .011	
  
Q7-­‐Q4	
   .280*	
  	
   .032	
  
Q8-­‐Q7	
   .344*	
   .008	
  
**.	
  Correla%on	
  is	
  significant	
  at	
  the	
  0.01	
  level	
  (2-­‐tailed)	
  
*Correla%on	
  is	
  significant	
  at	
  the	
  0.05	
  level	
  (2-­‐tailed).	
  
	
  	
  	
  
Table 2: Pearson Correlation Values (Statistically Significant Values only) among the Website Information Comprehension Questions
Results and Discussion 3
Ques%on	
   N	
   Minimum	
   Maximum	
   Mean	
   Std.	
  Devia%on	
  
Q1	
   59	
   0	
   7	
   4.10	
   1.658	
  
Q2	
   59	
   1	
   7	
   4.29	
   1.521	
  
Q3	
   59	
   0	
   7	
   3.81	
   1.503	
  
Q4	
   59	
   0	
   7	
   3.61	
   1.576	
  
Q5	
   59	
   0	
   7	
   3.69	
   1.664	
  
Q6	
   59	
   1	
   7	
   4.03	
   1.575	
  
Q7	
   59	
   1	
   7	
   3.97	
   1.553	
  
Q8	
   59	
   0	
   7	
   3.32	
   1.824	
  
Q9	
   59	
   0	
   7	
   3.29	
   2.205	
  
Q10	
   59	
   0	
   7	
   2.92	
   2.020	
  
Q11	
   59	
   0	
   7	
   4.36	
   1.689	
  
Q12	
   59	
   1	
   7	
   4.37	
   1.376	
  
Q13	
   59	
   1	
   7	
   4.20	
   1.412	
  
Q14	
   59	
   0	
   7	
   3.95	
   1.665	
  
Q15	
   59	
   1	
   7	
   4.63	
   1.484	
  
Q16	
   59	
   0	
   7	
   4.05	
   1.726	
  
Q17	
   59	
   1	
   7	
   3.63	
   1.809	
  
Q18	
   59	
   1	
   7	
   3.49	
   1.696	
  
Q19	
   59	
   1	
   7	
   4.17	
   1.652	
  
Valid	
  N	
  (listwise)	
   59	
   	
  	
   	
  	
   	
  	
   	
  	
  
Table 3. Descriptive Statistics for Self-Reports on the CSUQ Questionnaire
Results and Discussion 4
Question Category	
   Number 	
   Significant Correlation with other questions	
  
Q1	
   All questions	
  
Q2	
   All questions	
  
Q3	
   All questions except Q9	
  
Q4	
   All questions	
  
Q5	
   All questions	
  
Q6	
   All questions	
  
Q7	
   All questions	
  
Q8	
   All questions except Q9	
  
Q9	
   All questions except Q3 and Q8 and Q15	
  
Q10	
   All questions except Q12	
  
Q11	
   All questions except Q12	
  
Q12	
   All questions except Q9, Q10, Q11, Q12	
  
Q13	
   All questions	
  
Q14	
   All questions	
  
Q15	
   All questions except Q9	
  
Q16	
   All questions	
  
Q17	
   All questions	
  
Q18	
   All questions	
  
Q19	
   All questions	
  
Table 4: Significant Correlation between Self-Reports on CSUQ Questionnaire (N = 59)
Results and Discussion 5
Ques&ons	
   Minimum	
   Maximum	
   Mean	
   Std.	
  Devia&on	
  
Q1	
   1	
   9	
   6.12	
   1.848	
  
Q2	
   0	
   9	
   5.64	
   1.989	
  
Q3	
   0	
   9	
   5.25	
   2.411	
  
Q4	
   0	
   9	
   4.75	
   2.279	
  
Q5	
   0	
   9	
   4.97	
   2.304	
  
Q6	
   0	
   9	
   5.03	
   2.573	
  
Q7	
   0	
   9	
   6.24	
   2.254	
  
Q8	
   0	
   9	
   6.25	
   1.944	
  
Q9	
   0	
   9	
   5.73	
   2.148	
  
Q10	
   0	
   9	
   4.42	
   2.183	
  
Q11	
   0	
   9	
   5.69	
   2.053	
  
Q12	
   0	
   9	
   5.71	
   2.407	
  
Q13	
   0	
   9	
   3.93	
   2.684	
  
Q14	
   0	
   9	
   3.81	
   2.549	
  
Q15	
   0	
   9	
   4.73	
   2.211	
  
Q16	
   0	
   9	
   4.64	
   2.517	
  
Q17	
   0	
   9	
   4.27	
   2.180	
  
Q18	
   0	
   9	
   4.37	
   2.189	
  
Q19	
   0	
   9	
   4.14	
   2.583	
  
Q20	
   0	
   9	
   4.81	
   2.467	
  
Q21	
   0	
   9	
   5.51	
   2.176	
  
Q22	
   0	
   9	
   2.12	
   2.841	
  
Q23	
   0	
   9	
   2.76	
   2.654	
  
Q24	
   0	
   9	
   4.69	
   2.541	
  
Table 5: Descriptive Statistics of the Self-reports on the QUIS
Questionnaire
59.4
64.8
54.2 49.2 44.2
0
10
20
30
40
50
60
70
PercentageofAgreementwiththe
Statement
Different Categories on the Questionnaire
Category 1 = Overall Reaction to the Website; Category
2 = Web Page; Category 3 = Terminology and Website
Information; Category 4 = Learning; Category 5 =
Percentage of Positive
Responses on Different
Categories in QUIS
Questionnaire
Percentage of
Positive Response on
Different Categories
Figure 2. Percentage Agreement with the Different Categories in QUIS
Questionnaire
Results and Discussion 6
Question	
   Number 	
   Significant Correlation with other questions	
  
Q1	
   All questions except Q2, Q10, Q14, Q16, Q23, Q24	
  
Q2	
   No questions except Q22	
  
Q3	
   All questions except Q2, Q10, Q11, Q17, Q22, Q23	
  
Q4	
   All questions except Q2, Q7, Q8, Q9, Q10, Q11, Q12, Q15, Q16, Q17, Q18, Q20,
Q24	
  
Q5	
   All questions except QQ2, Q5, Q11, Q12, Q19, Q20, Q22, Q23	
  
Q6	
   All questions except Q2, Q6, Q10, Q13, Q14, Q15, Q16, Q17, Q22, Q23, Q24	
  
Q7	
   All questions except Q2, Q4, Q10, Q13, Q16, Q17, Q22, Q23, Q24	
  
Q8	
   All questions except Q2, Q4, Q13, Q14, Q16, Q19, Q20, Q22, Q23, Q24	
  
Q9	
   All questions except Q2, Q4, Q13, Q14, Q19, Q20, Q22, Q23	
  
Q10	
  
All questions except Q1, Q2, Q3, Q4, Q6, Q7, Q12, Q13, Q14, Q19, Q20, Q22, Q23	
  
Q11	
   All questions except Q2, Q3, Q4, Q5, Q11, Q13, Q21, Q22, Q23, Q24	
  
Q12	
   All questions except Q2, Q4, Q5, Q10, Q12, Q13, Q15, Q16, Q19	
  
Q13	
   All questions except Q2, Q6, Q7, Q8, Q9, Q10, Q11, Q12, Q13, Q15, Q16, Q17, Q18,
Q20, Q21, Q24	
  
Q14	
   All questions except Q1, Q2, Q6, Q8, Q9, Q10, Q14, Q15, Q16, Q17, Q19, Q20, Q22,
Q23, Q24	
  
Q15	
  
All questions except Q1, Q2, Q4, Q6, Q12, Q13, Q14, Q15, Q19, Q20, Q22, Q23	
  
Q16	
   All questions except Q1, Q2, Q4, Q6, Q7, Q8, Q12, Q13, Q14, Q16, Q20, Q21, Q22,
Q23	
  
Q17	
  
All questions except Q2, Q3, Q4, Q6, Q7, Q13, Q14, Q17, Q19, Q20, Q22, Q23, Q24	
  
Q18	
   All questions except Q2, Q4, Q13, Q18, Q22	
  
Q19	
  
All questions except Q2, Q5, Q8, Q9, Q10, Q12, Q14, Q15, Q17, Q19, Q24	
  
Q20	
  
All questions except Q2, Q4, Q5, Q8, Q9, Q10, Q13, Q14, Q15, Q16, Q17, Q20, Q24	
  
Q21	
   All questions except Q2, Q11, Q13, Q16, Q21, Q23	
  
Q22	
   All questions except Q1, Q3, Q5, Q6, Q7, Q8, Q9, Q10, Q11, Q12, Q14, Q15, Q16,
Q17, Q18, Q22, Q24	
  
Q23	
   No questions except Q4, Q13, Q18, Q19, Q20, Q22, Q24	
  
Q24	
   No questions except Q3, Q5, Q9, Q10, Q15, Q16, Q18, Q21, Q23	
  
Table 6: Significant Correlation between Self-Reports on QUIS Questionnaire (N = 59)
Results and Discussion 7
	
  	
  
Mean	
  
Rank	
  
Chi-­‐
Square	
  
Asymp.	
  
Sig.	
  
Q1	
   11.19	
  
126.626	
   .000	
  
Q2	
   11.62	
  
Q3	
   9.54	
  
Q4	
   8.3	
  
Q5	
   8.88	
  
Q6	
   10.4	
  
Q7	
   10.14	
  
Q8	
   7.2	
  
Q9	
   8.14	
  
Q10	
   7.08	
  
Q11	
   12.19	
  
Q12	
   11.83	
  
Q13	
   11.12	
  
Q14	
   10.4	
  
Q15	
   13	
  
Q16	
   10.95	
  
Q17	
   8.69	
  
Q18	
   7.71	
  
Q19	
   11.62	
  
Table 7: Friedman Test Values for the 19 Questions in CSUQ Questionnaire
Ques%on	
   Categories	
   Mean	
  Rank	
   Chi-­‐Square	
   Asymp.	
  Sig.	
  
Q1	
  
Comprehensive	
  	
  
evalua%on	
  
3.690	
  
21.532	
   .000	
  
Q2	
   3.070	
  
Q3	
   2.960	
  
Q4	
   2.690	
  
Q5	
   2.590	
  
Q6	
  
Web	
  Page	
  Design	
  
1.580	
  
24.818	
   .000	
  Q7	
   2.260	
  
Q8	
   2.160	
  
Q9	
  
Terminology	
  	
  
and	
  	
  
Website	
  Informa%on	
  
4.260	
  
60.670	
   .000	
  
Q10	
   2.920	
  
Q11	
   4.110	
  
Q12	
   4.220	
  
Q13	
   2.860	
  
Q14	
   2.640	
  
Q15	
  
Learning	
  
3.210	
  
5.769	
   .217	
  
Q16	
   3.210	
  
Q17	
   2.760	
  
Q18	
   2.810	
  
Q19	
   3.000	
  
Q20	
  
Website	
  Capabili%es	
  
3.490	
  
75.683	
   .000	
  
Q21	
   3.900	
  
Q22	
   1.950	
  
Q23	
   2.350	
  
Q24	
   3.310	
  
Results and Discussion 8
Table 8: Friedman Test Statistics for the 5 Different Categories in the QUIS Questionnaire
Results and Discussion 9
Words	
  with	
  Maximum	
  Frequency	
  (Top	
  10)	
   Frequency	
  
Convenient	
   32	
  
Clean	
   29	
  
Dull	
   29	
  
Slow	
   28	
  
Helpful	
   27	
  
Useful	
   26	
  
Accessible	
   24	
  
Engaging	
   23	
  
Fun	
   23	
  
Usable	
   23	
  
Table 9. Words chosen with Maximum Frequency
Conclusion and Future Work
• Production
-L2 readers have wide variability in the efficiency scale, when
analyzing an English website.
• Future Work
- This will help us judge their levels of proficiency and the types
of English websites they could be exposed to for various kinds of
assignments, task-based language learning etc.
Thank you

Más contenido relacionado

Destacado

IVI Presentation At Rusnano Conference
IVI Presentation At Rusnano ConferenceIVI Presentation At Rusnano Conference
IVI Presentation At Rusnano ConferenceThomas Nastas
 
New Directions In Russian Private Equity
New Directions In Russian Private EquityNew Directions In Russian Private Equity
New Directions In Russian Private EquityThomas Nastas
 
20130425 mexicobrasil
20130425 mexicobrasil20130425 mexicobrasil
20130425 mexicobrasilSteveScheibe
 
Proiect 1000 - Stefan Szakal
Proiect 1000 - Stefan SzakalProiect 1000 - Stefan Szakal
Proiect 1000 - Stefan SzakalGeekMeet
 
Catalogo tony tallarin
Catalogo tony tallarinCatalogo tony tallarin
Catalogo tony tallarinAndres Garces
 
Path to Commercialization, Nastas Presentation to Winner of Grant Competition
Path to Commercialization, Nastas Presentation to Winner of Grant CompetitionPath to Commercialization, Nastas Presentation to Winner of Grant Competition
Path to Commercialization, Nastas Presentation to Winner of Grant CompetitionThomas Nastas
 
Mobile UX Research: Travel Consumer Preferences for Mobile and Tablet
Mobile UX Research: Travel Consumer Preferences for Mobile and TabletMobile UX Research: Travel Consumer Preferences for Mobile and Tablet
Mobile UX Research: Travel Consumer Preferences for Mobile and TabletUserZoom
 

Destacado (10)

Connexions Roy 2013
Connexions Roy 2013Connexions Roy 2013
Connexions Roy 2013
 
IVI Presentation At Rusnano Conference
IVI Presentation At Rusnano ConferenceIVI Presentation At Rusnano Conference
IVI Presentation At Rusnano Conference
 
New Directions In Russian Private Equity
New Directions In Russian Private EquityNew Directions In Russian Private Equity
New Directions In Russian Private Equity
 
20130425 mexicobrasil
20130425 mexicobrasil20130425 mexicobrasil
20130425 mexicobrasil
 
Proiect 1000 - Stefan Szakal
Proiect 1000 - Stefan SzakalProiect 1000 - Stefan Szakal
Proiect 1000 - Stefan Szakal
 
Nozawa thesis
Nozawa thesisNozawa thesis
Nozawa thesis
 
Drenajes en cirugia biliopancreatica
Drenajes en cirugia biliopancreaticaDrenajes en cirugia biliopancreatica
Drenajes en cirugia biliopancreatica
 
Catalogo tony tallarin
Catalogo tony tallarinCatalogo tony tallarin
Catalogo tony tallarin
 
Path to Commercialization, Nastas Presentation to Winner of Grant Competition
Path to Commercialization, Nastas Presentation to Winner of Grant CompetitionPath to Commercialization, Nastas Presentation to Winner of Grant Competition
Path to Commercialization, Nastas Presentation to Winner of Grant Competition
 
Mobile UX Research: Travel Consumer Preferences for Mobile and Tablet
Mobile UX Research: Travel Consumer Preferences for Mobile and TabletMobile UX Research: Travel Consumer Preferences for Mobile and Tablet
Mobile UX Research: Travel Consumer Preferences for Mobile and Tablet
 

Similar a Yoshida presentation

2013 07 05 (uc3m) lasi emadrid jgzubia deusto learning analytics primeras exp...
2013 07 05 (uc3m) lasi emadrid jgzubia deusto learning analytics primeras exp...2013 07 05 (uc3m) lasi emadrid jgzubia deusto learning analytics primeras exp...
2013 07 05 (uc3m) lasi emadrid jgzubia deusto learning analytics primeras exp...eMadrid network
 
Using ExamSoft Data to Prepare For and Ease the Accreditation Process
Using ExamSoft Data to Prepare For and Ease the Accreditation ProcessUsing ExamSoft Data to Prepare For and Ease the Accreditation Process
Using ExamSoft Data to Prepare For and Ease the Accreditation ProcessExamSoft
 
Charles Cotter's PhD research findings & recommendations_Strategic L&D
Charles Cotter's PhD research findings & recommendations_Strategic L&DCharles Cotter's PhD research findings & recommendations_Strategic L&D
Charles Cotter's PhD research findings & recommendations_Strategic L&DCharles Cotter, PhD
 
Collecting managing and assessing data using sample surveys
Collecting managing and assessing data using sample surveysCollecting managing and assessing data using sample surveys
Collecting managing and assessing data using sample surveysĐức Nhiên Trần
 
Forecasting QuestionsStudent NameUniversity Affiliate.docx
Forecasting QuestionsStudent NameUniversity Affiliate.docxForecasting QuestionsStudent NameUniversity Affiliate.docx
Forecasting QuestionsStudent NameUniversity Affiliate.docxalisoncarleen
 
The Moodle quiz at the Open University
The Moodle quiz at the Open UniversityThe Moodle quiz at the Open University
The Moodle quiz at the Open UniversityTim Hunt
 
Descriptive Statistics, Numerical Description
Descriptive Statistics, Numerical DescriptionDescriptive Statistics, Numerical Description
Descriptive Statistics, Numerical Descriptiongetyourcheaton
 
FedCASIC 2019: On Using Cognitive Computing and Machine Learning Tools to Imp...
FedCASIC 2019: On Using Cognitive Computing and Machine Learning Tools to Imp...FedCASIC 2019: On Using Cognitive Computing and Machine Learning Tools to Imp...
FedCASIC 2019: On Using Cognitive Computing and Machine Learning Tools to Imp...Lew Berman
 
ISETL presentation: Cafeteria Style Grading 10/13/16
ISETL presentation: Cafeteria Style Grading 10/13/16ISETL presentation: Cafeteria Style Grading 10/13/16
ISETL presentation: Cafeteria Style Grading 10/13/16Anne Arendt
 
Outcome Based Education and Assessment
Outcome Based Education and AssessmentOutcome Based Education and Assessment
Outcome Based Education and AssessmentVijay Kumar Jadon
 
Identifying the Root Cause of Failures in IT Changes: Novel Strategies and Tr...
Identifying the Root Cause of Failures in IT Changes: Novel Strategies and Tr...Identifying the Root Cause of Failures in IT Changes: Novel Strategies and Tr...
Identifying the Root Cause of Failures in IT Changes: Novel Strategies and Tr...Ricardo Luis dos Santos
 
Hisd Elem Math 2009 2010
Hisd Elem Math 2009 2010Hisd Elem Math 2009 2010
Hisd Elem Math 2009 2010emengist
 
Kick-off Meeting of the Advisory Group for the OECD Guidelines for Measuring ...
Kick-off Meeting of the Advisory Group for the OECD Guidelines for Measuring ...Kick-off Meeting of the Advisory Group for the OECD Guidelines for Measuring ...
Kick-off Meeting of the Advisory Group for the OECD Guidelines for Measuring ...StatsCommunications
 
Multimodal Residual Learning for Visual Question-Answering
Multimodal Residual Learning for Visual Question-AnsweringMultimodal Residual Learning for Visual Question-Answering
Multimodal Residual Learning for Visual Question-AnsweringNAVER D2
 
The Moodle Quiz at the Open University: how we use it & how that helps students
The Moodle Quiz at the Open University: how we use it & how that helps studentsThe Moodle Quiz at the Open University: how we use it & how that helps students
The Moodle Quiz at the Open University: how we use it & how that helps studentsTim Hunt
 

Similar a Yoshida presentation (20)

2013 07 05 (uc3m) lasi emadrid jgzubia deusto learning analytics primeras exp...
2013 07 05 (uc3m) lasi emadrid jgzubia deusto learning analytics primeras exp...2013 07 05 (uc3m) lasi emadrid jgzubia deusto learning analytics primeras exp...
2013 07 05 (uc3m) lasi emadrid jgzubia deusto learning analytics primeras exp...
 
Using ExamSoft Data to Prepare For and Ease the Accreditation Process
Using ExamSoft Data to Prepare For and Ease the Accreditation ProcessUsing ExamSoft Data to Prepare For and Ease the Accreditation Process
Using ExamSoft Data to Prepare For and Ease the Accreditation Process
 
Charles Cotter's PhD research findings & recommendations_Strategic L&D
Charles Cotter's PhD research findings & recommendations_Strategic L&DCharles Cotter's PhD research findings & recommendations_Strategic L&D
Charles Cotter's PhD research findings & recommendations_Strategic L&D
 
Collecting managing and assessing data using sample surveys
Collecting managing and assessing data using sample surveysCollecting managing and assessing data using sample surveys
Collecting managing and assessing data using sample surveys
 
Forecasting QuestionsStudent NameUniversity Affiliate.docx
Forecasting QuestionsStudent NameUniversity Affiliate.docxForecasting QuestionsStudent NameUniversity Affiliate.docx
Forecasting QuestionsStudent NameUniversity Affiliate.docx
 
The Moodle quiz at the Open University
The Moodle quiz at the Open UniversityThe Moodle quiz at the Open University
The Moodle quiz at the Open University
 
Descriptive Statistics, Numerical Description
Descriptive Statistics, Numerical DescriptionDescriptive Statistics, Numerical Description
Descriptive Statistics, Numerical Description
 
FedCASIC 2019: On Using Cognitive Computing and Machine Learning Tools to Imp...
FedCASIC 2019: On Using Cognitive Computing and Machine Learning Tools to Imp...FedCASIC 2019: On Using Cognitive Computing and Machine Learning Tools to Imp...
FedCASIC 2019: On Using Cognitive Computing and Machine Learning Tools to Imp...
 
Training Module
Training ModuleTraining Module
Training Module
 
ISETL presentation: Cafeteria Style Grading 10/13/16
ISETL presentation: Cafeteria Style Grading 10/13/16ISETL presentation: Cafeteria Style Grading 10/13/16
ISETL presentation: Cafeteria Style Grading 10/13/16
 
Outcome Based Education and Assessment
Outcome Based Education and AssessmentOutcome Based Education and Assessment
Outcome Based Education and Assessment
 
Identifying the Root Cause of Failures in IT Changes: Novel Strategies and Tr...
Identifying the Root Cause of Failures in IT Changes: Novel Strategies and Tr...Identifying the Root Cause of Failures in IT Changes: Novel Strategies and Tr...
Identifying the Root Cause of Failures in IT Changes: Novel Strategies and Tr...
 
Hisd Elem Math 2009 2010
Hisd Elem Math 2009 2010Hisd Elem Math 2009 2010
Hisd Elem Math 2009 2010
 
Discrimination index
Discrimination indexDiscrimination index
Discrimination index
 
Kick-off Meeting of the Advisory Group for the OECD Guidelines for Measuring ...
Kick-off Meeting of the Advisory Group for the OECD Guidelines for Measuring ...Kick-off Meeting of the Advisory Group for the OECD Guidelines for Measuring ...
Kick-off Meeting of the Advisory Group for the OECD Guidelines for Measuring ...
 
Multimodal Residual Learning for Visual Question-Answering
Multimodal Residual Learning for Visual Question-AnsweringMultimodal Residual Learning for Visual Question-Answering
Multimodal Residual Learning for Visual Question-Answering
 
TCSion PrepTest - Iqureka.com
TCSion PrepTest - Iqureka.comTCSion PrepTest - Iqureka.com
TCSion PrepTest - Iqureka.com
 
The Moodle Quiz at the Open University: how we use it & how that helps students
The Moodle Quiz at the Open University: how we use it & how that helps studentsThe Moodle Quiz at the Open University: how we use it & how that helps students
The Moodle Quiz at the Open University: how we use it & how that helps students
 
Refund policy
Refund policyRefund policy
Refund policy
 
Sequential Query Expansion using Concept Graph
Sequential Query Expansion using Concept GraphSequential Query Expansion using Concept Graph
Sequential Query Expansion using Concept Graph
 

Más de Debopriyo Roy

ETLTC2020-Virtual: CFP
ETLTC2020-Virtual: CFPETLTC2020-Virtual: CFP
ETLTC2020-Virtual: CFPDebopriyo Roy
 
Watanabe presentation
Watanabe presentationWatanabe presentation
Watanabe presentationDebopriyo Roy
 
Design Thinking in EFL Context
Design Thinking in EFL ContextDesign Thinking in EFL Context
Design Thinking in EFL ContextDebopriyo Roy
 
Certificateof completion roy, norma 3.1.13
Certificateof completion roy, norma 3.1.13Certificateof completion roy, norma 3.1.13
Certificateof completion roy, norma 3.1.13Debopriyo Roy
 
Katie greetings letter
Katie greetings letterKatie greetings letter
Katie greetings letterDebopriyo Roy
 
Introduction to Business Emails
Introduction to Business EmailsIntroduction to Business Emails
Introduction to Business EmailsDebopriyo Roy
 
Technical Communication Lab Projects
Technical Communication Lab ProjectsTechnical Communication Lab Projects
Technical Communication Lab ProjectsDebopriyo Roy
 
Design thinking in efl context
Design thinking in efl contextDesign thinking in efl context
Design thinking in efl contextDebopriyo Roy
 
Circuit design presentation
Circuit design presentationCircuit design presentation
Circuit design presentationDebopriyo Roy
 

Más de Debopriyo Roy (20)

JALTCALL2021 Talk
JALTCALL2021 TalkJALTCALL2021 Talk
JALTCALL2021 Talk
 
ETLTC2020-Virtual: CFP
ETLTC2020-Virtual: CFPETLTC2020-Virtual: CFP
ETLTC2020-Virtual: CFP
 
Yoshida thesis
Yoshida thesisYoshida thesis
Yoshida thesis
 
Watanabe thesis
Watanabe thesisWatanabe thesis
Watanabe thesis
 
Watanabe presentation
Watanabe presentationWatanabe presentation
Watanabe presentation
 
Nozawa presentation
Nozawa presentationNozawa presentation
Nozawa presentation
 
Ishii thesis
Ishii thesisIshii thesis
Ishii thesis
 
Ishii presentation
Ishii presentationIshii presentation
Ishii presentation
 
Arai thesis
Arai thesisArai thesis
Arai thesis
 
Arai presentation
Arai presentationArai presentation
Arai presentation
 
Design Thinking in EFL Context
Design Thinking in EFL ContextDesign Thinking in EFL Context
Design Thinking in EFL Context
 
Certificateof completion roy, norma 3.1.13
Certificateof completion roy, norma 3.1.13Certificateof completion roy, norma 3.1.13
Certificateof completion roy, norma 3.1.13
 
Katie greetings letter
Katie greetings letterKatie greetings letter
Katie greetings letter
 
Email etiquette
Email etiquetteEmail etiquette
Email etiquette
 
Email etiquette
Email etiquetteEmail etiquette
Email etiquette
 
Introduction to Business Emails
Introduction to Business EmailsIntroduction to Business Emails
Introduction to Business Emails
 
Technical Communication Lab Projects
Technical Communication Lab ProjectsTechnical Communication Lab Projects
Technical Communication Lab Projects
 
Design thinking in efl context
Design thinking in efl contextDesign thinking in efl context
Design thinking in efl context
 
Ieeej 2010
Ieeej 2010Ieeej 2010
Ieeej 2010
 
Circuit design presentation
Circuit design presentationCircuit design presentation
Circuit design presentation
 

Último

The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxLoriGlavin3
 

Último (20)

The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
 

Yoshida presentation

  • 1. EFL Student’s Ability for Website Information Comprehension and Perceptions on Website Usability s1170173 Makoto Yoshida Supervised by Prof. Debopriyo Roy
  • 2. Overview • Abstract • Research Workflow • Question what We Set (to Participants) • Results and Discussion (1-9) • Conclusion and Future Work
  • 3. Abstract The purpose of this study is to observe and consider the impression of L2 readers during website analysis in target language (English). At first, participants completed an extensive website analysis using established web design models with a closed-ended website information comprehension questionnaire (testing readers’ understanding of the text content in the website). In the second stage, they answered three usability questionnaires (QUIS, CSUQ and MPRC questionnaires), recording their impression about the interface, navigational efficiency, content comprehension, learning aspects of the interface, basic comfort level with text-graphics content etc. I used Pearson Correlation and non-parametric Friedman Test to analyze user data. The self-reports on the questionnaires (QUIS, CSUQ and MPRC) provided a general outlook about the website content and navigation and might not have been specific to the accuracy scores (related to the text content of the website). This exploratory analysis as discussed in this article could help us obtain initial data on how EFL readers in a typical context as this would perform with English website information and the type of impression they have about the website.
  • 4. Research Workflow • 59 participants completed the website information comprehension questionnaire. Accuracy scores on the questionnaire were measured. •  Following the interaction with the website, readers completed the three questionnaires on self-reporting. • Collection their answers • Data Analysis • Consider the Results
  • 5. Questionnaires The website information comprehension questionnaire focused on the efficiency with which L2 readers are able to search through information from the Belize tourism website. The focus at this stage was on readers’ ability to navigate through the pages, based on cues from the questionnaire. The questionnaire was designed on the basis of the following: 1.  multiple-choice questions asking readers to accurately pinpoint the information available in the website. 2.  readers’ ability to sequence order steps in the correct order when searching for an information from the webpage. (Q1 with 8 steps to be correctly ordered). 10 questions were asked and each question (except Q1) could be scored as 1(correct) or 0 (incorrect) (binary scale). The total accuracy scores for each student were measured. QUIS, CSUQ and MPRC questionnaires are standard software usability questionnaires used for self-reporting users’ preference for the website.
  • 6. Results and Discussion Ques%on N Minimum Maximum Mean Std.  Devia%on Q1 59 0 10 8.66 2.496 Q2 0 2 1.97 .260 Q3 2 2 2.00 .000 Q4 0 2 1.19 .991 Q5 0 2 1.19 .991 Q6 0 2 1.73 .691 Q7 0 2 1.90 .443 Q8 0 2 1.64 .663 Valid  N (List  wise)         Table 1: Descriptive Statistics for the 8 Website Information Comprehension Questions 0 0.5 1 1.5 2 2.5 1 2 3 4 5 6 7 MeanAccurateScore Question Number (1 = Q2; 2 = Q3; 3 = Q4; 4 = Q5; 5 = Q6; 6 = Q7; 7 = Q8) Mean Accuracy Score (Maxium Value = 2) Mean Accuracy Score (Maxium Value = 2) Figure 1. Mean Accuracy Scores for Q2 – Q8 (Wrong Answer = 0; Correct Answer = 2)
  • 7. Results and Discussion 2 Ques%on   Pearson   Correla%on   Sig.(2-­‐ tailed)   N=59   Q2-­‐Q7      .567**   .000   59   Q2-­‐Q8   .328*   .011   Q7-­‐Q4   .280*     .032   Q8-­‐Q7   .344*   .008   **.  Correla%on  is  significant  at  the  0.01  level  (2-­‐tailed)   *Correla%on  is  significant  at  the  0.05  level  (2-­‐tailed).         Table 2: Pearson Correlation Values (Statistically Significant Values only) among the Website Information Comprehension Questions
  • 8. Results and Discussion 3 Ques%on   N   Minimum   Maximum   Mean   Std.  Devia%on   Q1   59   0   7   4.10   1.658   Q2   59   1   7   4.29   1.521   Q3   59   0   7   3.81   1.503   Q4   59   0   7   3.61   1.576   Q5   59   0   7   3.69   1.664   Q6   59   1   7   4.03   1.575   Q7   59   1   7   3.97   1.553   Q8   59   0   7   3.32   1.824   Q9   59   0   7   3.29   2.205   Q10   59   0   7   2.92   2.020   Q11   59   0   7   4.36   1.689   Q12   59   1   7   4.37   1.376   Q13   59   1   7   4.20   1.412   Q14   59   0   7   3.95   1.665   Q15   59   1   7   4.63   1.484   Q16   59   0   7   4.05   1.726   Q17   59   1   7   3.63   1.809   Q18   59   1   7   3.49   1.696   Q19   59   1   7   4.17   1.652   Valid  N  (listwise)   59                   Table 3. Descriptive Statistics for Self-Reports on the CSUQ Questionnaire
  • 9. Results and Discussion 4 Question Category   Number   Significant Correlation with other questions   Q1   All questions   Q2   All questions   Q3   All questions except Q9   Q4   All questions   Q5   All questions   Q6   All questions   Q7   All questions   Q8   All questions except Q9   Q9   All questions except Q3 and Q8 and Q15   Q10   All questions except Q12   Q11   All questions except Q12   Q12   All questions except Q9, Q10, Q11, Q12   Q13   All questions   Q14   All questions   Q15   All questions except Q9   Q16   All questions   Q17   All questions   Q18   All questions   Q19   All questions   Table 4: Significant Correlation between Self-Reports on CSUQ Questionnaire (N = 59)
  • 10. Results and Discussion 5 Ques&ons   Minimum   Maximum   Mean   Std.  Devia&on   Q1   1   9   6.12   1.848   Q2   0   9   5.64   1.989   Q3   0   9   5.25   2.411   Q4   0   9   4.75   2.279   Q5   0   9   4.97   2.304   Q6   0   9   5.03   2.573   Q7   0   9   6.24   2.254   Q8   0   9   6.25   1.944   Q9   0   9   5.73   2.148   Q10   0   9   4.42   2.183   Q11   0   9   5.69   2.053   Q12   0   9   5.71   2.407   Q13   0   9   3.93   2.684   Q14   0   9   3.81   2.549   Q15   0   9   4.73   2.211   Q16   0   9   4.64   2.517   Q17   0   9   4.27   2.180   Q18   0   9   4.37   2.189   Q19   0   9   4.14   2.583   Q20   0   9   4.81   2.467   Q21   0   9   5.51   2.176   Q22   0   9   2.12   2.841   Q23   0   9   2.76   2.654   Q24   0   9   4.69   2.541   Table 5: Descriptive Statistics of the Self-reports on the QUIS Questionnaire 59.4 64.8 54.2 49.2 44.2 0 10 20 30 40 50 60 70 PercentageofAgreementwiththe Statement Different Categories on the Questionnaire Category 1 = Overall Reaction to the Website; Category 2 = Web Page; Category 3 = Terminology and Website Information; Category 4 = Learning; Category 5 = Percentage of Positive Responses on Different Categories in QUIS Questionnaire Percentage of Positive Response on Different Categories Figure 2. Percentage Agreement with the Different Categories in QUIS Questionnaire
  • 11. Results and Discussion 6 Question   Number   Significant Correlation with other questions   Q1   All questions except Q2, Q10, Q14, Q16, Q23, Q24   Q2   No questions except Q22   Q3   All questions except Q2, Q10, Q11, Q17, Q22, Q23   Q4   All questions except Q2, Q7, Q8, Q9, Q10, Q11, Q12, Q15, Q16, Q17, Q18, Q20, Q24   Q5   All questions except QQ2, Q5, Q11, Q12, Q19, Q20, Q22, Q23   Q6   All questions except Q2, Q6, Q10, Q13, Q14, Q15, Q16, Q17, Q22, Q23, Q24   Q7   All questions except Q2, Q4, Q10, Q13, Q16, Q17, Q22, Q23, Q24   Q8   All questions except Q2, Q4, Q13, Q14, Q16, Q19, Q20, Q22, Q23, Q24   Q9   All questions except Q2, Q4, Q13, Q14, Q19, Q20, Q22, Q23   Q10   All questions except Q1, Q2, Q3, Q4, Q6, Q7, Q12, Q13, Q14, Q19, Q20, Q22, Q23   Q11   All questions except Q2, Q3, Q4, Q5, Q11, Q13, Q21, Q22, Q23, Q24   Q12   All questions except Q2, Q4, Q5, Q10, Q12, Q13, Q15, Q16, Q19   Q13   All questions except Q2, Q6, Q7, Q8, Q9, Q10, Q11, Q12, Q13, Q15, Q16, Q17, Q18, Q20, Q21, Q24   Q14   All questions except Q1, Q2, Q6, Q8, Q9, Q10, Q14, Q15, Q16, Q17, Q19, Q20, Q22, Q23, Q24   Q15   All questions except Q1, Q2, Q4, Q6, Q12, Q13, Q14, Q15, Q19, Q20, Q22, Q23   Q16   All questions except Q1, Q2, Q4, Q6, Q7, Q8, Q12, Q13, Q14, Q16, Q20, Q21, Q22, Q23   Q17   All questions except Q2, Q3, Q4, Q6, Q7, Q13, Q14, Q17, Q19, Q20, Q22, Q23, Q24   Q18   All questions except Q2, Q4, Q13, Q18, Q22   Q19   All questions except Q2, Q5, Q8, Q9, Q10, Q12, Q14, Q15, Q17, Q19, Q24   Q20   All questions except Q2, Q4, Q5, Q8, Q9, Q10, Q13, Q14, Q15, Q16, Q17, Q20, Q24   Q21   All questions except Q2, Q11, Q13, Q16, Q21, Q23   Q22   All questions except Q1, Q3, Q5, Q6, Q7, Q8, Q9, Q10, Q11, Q12, Q14, Q15, Q16, Q17, Q18, Q22, Q24   Q23   No questions except Q4, Q13, Q18, Q19, Q20, Q22, Q24   Q24   No questions except Q3, Q5, Q9, Q10, Q15, Q16, Q18, Q21, Q23   Table 6: Significant Correlation between Self-Reports on QUIS Questionnaire (N = 59)
  • 12. Results and Discussion 7     Mean   Rank   Chi-­‐ Square   Asymp.   Sig.   Q1   11.19   126.626   .000   Q2   11.62   Q3   9.54   Q4   8.3   Q5   8.88   Q6   10.4   Q7   10.14   Q8   7.2   Q9   8.14   Q10   7.08   Q11   12.19   Q12   11.83   Q13   11.12   Q14   10.4   Q15   13   Q16   10.95   Q17   8.69   Q18   7.71   Q19   11.62   Table 7: Friedman Test Values for the 19 Questions in CSUQ Questionnaire
  • 13. Ques%on   Categories   Mean  Rank   Chi-­‐Square   Asymp.  Sig.   Q1   Comprehensive     evalua%on   3.690   21.532   .000   Q2   3.070   Q3   2.960   Q4   2.690   Q5   2.590   Q6   Web  Page  Design   1.580   24.818   .000  Q7   2.260   Q8   2.160   Q9   Terminology     and     Website  Informa%on   4.260   60.670   .000   Q10   2.920   Q11   4.110   Q12   4.220   Q13   2.860   Q14   2.640   Q15   Learning   3.210   5.769   .217   Q16   3.210   Q17   2.760   Q18   2.810   Q19   3.000   Q20   Website  Capabili%es   3.490   75.683   .000   Q21   3.900   Q22   1.950   Q23   2.350   Q24   3.310   Results and Discussion 8 Table 8: Friedman Test Statistics for the 5 Different Categories in the QUIS Questionnaire
  • 14. Results and Discussion 9 Words  with  Maximum  Frequency  (Top  10)   Frequency   Convenient   32   Clean   29   Dull   29   Slow   28   Helpful   27   Useful   26   Accessible   24   Engaging   23   Fun   23   Usable   23   Table 9. Words chosen with Maximum Frequency
  • 15. Conclusion and Future Work • Production -L2 readers have wide variability in the efficiency scale, when analyzing an English website. • Future Work - This will help us judge their levels of proficiency and the types of English websites they could be exposed to for various kinds of assignments, task-based language learning etc.