The effects of learning styles and cultural background on understanding the information architectures (IAs) of information rich websites

Cagla Seneler, University of York, UK, Helen Petrie, University of York, UK

Abstract

This paper presents a study that examines learning styles and cultural background to reveal differences in users’ categorizations and mental models of the information architectures (IAs) in two website domains (museum and news sites) by using a card sort methodology. These informal learning domains were selected since people who visit museum websites may well be tourists coming from different cultures, and many people visit news websites almost every day, therefor people would seem to be familiar with their structure. Furthermore, these domains provide rich multimedia information for its users. To evaluate IAs of these websites, a simple, quick, cheap, and reliable method was used that is called the card sort technique. Findings of the study illustrated interesting and meaningful differences between users with different learning styles and among different cultural groups. This study also made a methodological contribution, showing that the card sort methodology could be used to show learning styles and cultural differences.

Keywords: card sort, information architecture, website design, museum websites, news websites, learning styles, cultural background

1. Introduction

Information-rich websites such as museum and news websites try to provide informal learning experiences for a wide range of users who have individual or group differences such as goals, interests, preferences, knowledge, backgrounds, demographic characteristics, experience, learning styles, and culture. To understand and support these users, individual differences can be addressed by focusing on learning styles, and group differences can be addressed by focusing on cultural background. The aim of the study presented in this paper is to investigate learning styles and cultural background to reveal differences in users’ categorizations and mental models of the information architectures (IAs) in two website domains (museum and news sites) by using a card sort methodology. These informal learning domains were selected because people who visit museum websites may well be tourists coming from different cultures, and many people visit news websites almost every day, therefor people would seem to be familiar with their structure. Furthermore, museum and news websites present rich information for their users. In addition, the card sort technique was used since it is a simple, quick, cheap, and reliable method and can be used for numerous grouping tasks, for example, to design and evaluate IAs of a website.

The card sort technique has been widely used in Human Computer Interaction (HCI) research (e.g. Fincher & Tenenberg, 2005; Rugg & McGeorge, 1997). In the card sort technique, participants are typically given a set of cards with items written on them, and they are asked to put them in logical groupings and to find a category name for each grouping. The groupings can be as large or as small as the participant chooses. While the task is simple for the participant, to analyze the results of this exercise can be difficult and time consuming. The technique can be conducted using physical cards (this will be referred to as the “oncard” version) or by using online card sorting software tools (this will be referred as the “online” version) which make the analysis easier for the researchers.

One aim of the UK part of the present study was to investigate whether there were differences between oncard and online administration of card sort studies. This aim was not relevant to the aims of this paper, but was investigated in the UK part of the study. The use of learning style models (LSMs) to enhance online learning systems has become an important subject of research, although there is still a scarcity of positive results. Some studies have indicated the contribution of such systems on improving user experiences (Carver, Howard & Lane, 1999; Popescu, 2010). Therefore, addressing learning style differences on websites may also enhance user experience. In this study, the Felder-Solomon Index of Learning Styles (ILS) and the Turkish version of this questionnaire, the Turkish Index of Learning Styles (T)ILS, were used to measure learners’ learning styles.

Studies on cultural differences in card sort studies have been reported in the literature (Aykin, Quaet-Faslem & Milewski, 2006; Harper, Jentsch, Van Duyne, Smith-Jentsch & Sanchez, 2002). These studies have highlighted numerous differences between cultural groups. For example, Qu, Sun, Nawaz, Plocher, and Clemmensen (2007) found cultural differences in the groupings of wedding-related images between Chinese and Danish participants. Kralisch, Yeu, and Jali (2006) found cultural differences between British, German, Malaysian, and Russian participants in their understanding of medical terms that might be used in health information websites. In addition, Petrie, Power, and Song (2009) used a card sort technique to reveal cultural differences between English and Chinese Web users in preferences for different navigational layouts on websites. However, it is not clear whether this effect would extend to other aspects of the IA on websites.

Two website domains were selected for investigation in this study: museum and news websites. Both these domains provide rich information for users and informal learning experiences. Visitors to major museum websites will include tourists coming from various cultures, so these websites should be addressing multi-cultural audiences. In addition, news websites were chosen because many people visit these websites daily, and people would seem to be familiar with their structure.

Due to these reasons, this study was conducted to address the following research question:
Are there any differences in users’ categorizations and mental models of the IAs based on their learning styles and cultural background?

2. Method

2.1 Participants

There were 214 participants in the UK part of the study and 90 participants in the Turkish part of the study. The participants in the UK were students from two undergraduate modules and one graduate module on interactive systems in the Department of Computer Science at the University of York. There were 184 male and 30 female participants, aged between 18 to 35 years, with a mean age of 21.3 years. In addition to British participants, participants came from a number of non-English speaking cultural backgrounds such as Chinese, Indian, and other European backgrounds, as well as other participants from the Rest of the World group (see table 1).
The Turkish participants were 52 male and 38 female participants, aged between 19 to 38 years, with a mean age of 22.1 years. These students were from two Turkish universities: Yeditepe University and Bogazici University.

Group n Men Women Min age Max age Mean age
British 107 100 7 18 35 19.6
Indian 27 20 7 19 31 24.2
Chinese 21 14 7 22 26 23.1
European 40 34 6 18 27 21.2
Turkish 90 52 38 19 38 22.1
Rest of the World 19 16 3 18 35 24.3

Table 1: Number of Participants in each Group

For each analysis, the number of participants with all the data necessary for that analysis were included, while the number of participants in each analysis differs. Therefore, the total number of participants that was used in any analysis is reported in this subsection.

To motivate participants, there was a lottery draw for three £10 gift vouchers for a major online book retailer in UK and for three 25 Turkish Lira gift vouchers for a popular online shop in Turkey for those who completed the study.

2.2 Equipment and materials

To create the words set for the card sorts, 18 museum websites and 10 news websites from countries with a national language of English (Australia, Canada, UK, and the USA) and five museum websites from Turkey were examined. These museum websites were chosen from the major national museums of each country, as these would be ones very likely to be visited by people from different cultures. The news websites were also chosen from the major news organizations in each country, assuming they would lead the way in website organization. A list of these websites can be found in table 2 and table 3.

 

Museum Link
British Museum www.britishmuseum.org
Victoria and Albert Museum www.vam.ac.uk
Natural History Museum www.nhm.ac.uk
Science Museum www.sciencemuseum.org.uk
National Museum of Rural Life www.nms.ac.uk/our_museums/museum_of_rural_life.aspx
National Museum Cardiff www.museumwales.ac.uk/en/cardiff/
National Museum Australia http://www.nma.gov.au
Melbourne Museum http://museumvictoria.com.au/melbournemuseum/
Powerhouse Museum www.powerhousemuseum.com/
The Metropolitan Museum of Art www.metmuseum.org/
The J. Paul Getty Museum www.getty.edu/museum/
National Museum of the American Indian www.nmai.si.edu/
SFMOMA www.sfmoma.org/
The Art Institute of Chicago www.artic.edu/aic/
The Nelson-Atkins Museum of Art www.nelson-atkins.org/
Welcome to Royal Ontario Museum www.rom.on.ca/index.php
Royal Tyrrel Museum www.tyrrellmuseum.com/
Canadian Museum of Civilization www.civilization.ca/cmc/home/cmc-home
Istanbul Archeological Museums http://www.istanbularkeoloji.gov.tr/main_page
Pera Museum http://en.peramuzesi.org.tr/
Topkapi Palace Museum http://www.topkapisarayi.gov.tr/
Sakip Sabanci Museum http://muze.sabanciuniv.edu/main/default.php?bytLanguageID=2
Rahmi M. Koc Museum http://www.rmk-museum.org.tr/english/index.html

Table 2: Museum Websites used in Card Sort Study

 

News Name Link
The Times http://www.thetimes.co.uk
The Guardian http://www.guardiannews.com/
The Independent http://www.independent.co.uk/
The Globe and Mail http://www.theglobeandmail.com/
The National Post http://www.nationalpost.com
The Star http://www.thestar.com/
Washington Post http://www.washingtonpost.com/
Chicago Tribune http://www.chicagotribune.com/
USA Today http://www.usatoday.com/
Daily Mail www.dailymail.co.uk

Table 3: News Websites used in Card Sort Study

First, the top level IA menu for each website was recorded. Next, for each website domain (museums or news), the most frequently occurring top seven menu items were chosen as main categories for collecting the word sets. Next, the IA menu under each of these seven main menu items was recorded from each website. All of these items were then categorized on the basis of their meanings. For example, if material on personal finance was labeled as “Finances” on one news website and “Your money” on another one, these were recorded under one group “Personal Finance” and as occurring on two distinct websites. Finally, the most frequent groups that occurred on most of the websites under the seven categories were selected as words for the card sort. This investigation produced 40 words from museum websites and 50 words from news websites. These groupings of these words will be referred to as the “A priori” groups here (see table 4 and table 5 for a priori groups and words).

 

 

Exhibition Education Events Museum
Current exhibitions

Future exhibitions

Past exhibitions

Travelling exhibitions

Schools

Teachers

Online resources

Adult learners

Learning centre

Events calendar

Events for the family

Talks and lectures

Courses and demonstrations

Jobs

Contact us

Press Room

Management

History of the Museum

Mission statement

Volunteering at the museum

The Collection Visit Shop  
Our collections

Search the collection

Conservation

Online collection

Collections management

Highlights of the collection

Activities for families and children

Finding the museum

Opening times

Eat and drink

Access for disabled visitors

Booking tickets

Family visits

Books and media

Prints and posters

Jewellery

Fashion and accessories

Home wares

Stationery

Shops

 

 

 

 

Table 4: Museum Priori Groups

 

Art News Lifestyle Sport
Films

Books

Music

Stage and Dance

TV & Radio

Comics

Visual Arts

National

World

Politics

Education

Science

Local

Technology

Food and Drink

Fashion and Style

Health

Family

Homes

Relationships

Puzzles and games

Football

Golf

Tennis

Motor Sport

Ice Hockey

Baseball

Basketball

Business Opinion Money  
Economics

Careers

Small Business

Industries

Personal Finance

Markets

Columnists

Letters to the Editor

Blogs

Cartoons

News Discussions

Editorials

Corrections

Leading articles

Commentators

Savings

Property

Taxes

Investments

Pensions

Borrowing

Insurance

 

 

 

 

 

 

 

Table 5: News Priori Groups

In the UK part of the study and the first phase of the Turkish part of the study, the card sorts were conducted by using both physical cards (oncard version) and an online program (online version) to compare oncard and online versions of the card sort technique. In the oncard version, each word was printed on cards sized 89mm x 51mm and each word had an associated number (to facilitate data analysis) printed on the back of the card. Two different oncard sets were printed, one for one for museum websites and one for news websites. In the online version, the online card-sorting package WebSort was used to present the “cards” to participants. Two different sets of online cards, one for museum websites and one for news websites, were created.

A questionnaire concerning the participants’ demographic information was prepared in the online survey package QuestionPro. A lottery number was used to match participants’ online sort with respective oncard sort and their responses to the questionnaire, and also to enter them in the lottery draws for the motivational prizes. In the second phase of the Turkish part of the study, only an online version was used for gathering card sorts, since the UK part of the study found that the use of different versions for gathering card sort data did not affect the results (Petrie et al., 2011). However, in this instance, the questionnaire was paper-based rather than an online survey.

To detect participants’ learning styles, UK participants took the online ILS and Turkish participants took the paper based (T)ILS.

2.3 Procedure

In the UK part of the study, data were collected during practical sessions of the modules. Lottery numbers and a sheet of instructions were provided to participants at the beginning of the session. One of the researchers also verbally presented the instructions to the participants. First, participants were asked to do ILS and then complete the questionnaire about their demographic information. Then, participants were worked in pairs and asked to undertake two sorts: one sort on the museum words and one sort on the news words. Half of the participants completed the museum sort first and the other half completed the news sort first. Half of the participants did the museum sort oncard and the other half did it online; the same division was made for the news sort. Participants were provided with a counterbalancing sheet which informed them of the order they should do the sorts in, and which sort they should undertake online and which sort they should undertake oncard.

For the oncard sorts, participants were given an envelope that contained a set of cards for the appropriate sort: museum or news. Participants were also given a set of blank slips of paper to write down category names in which they were grouping cards. Participants were asked to shuffle the cards at the beginning to ensure randomness of the set of cards (as several participants used each set). In addition, participants were asked to:
• Sort cards into categories and label these categories by means of blank slips of paper;
• They were assured that there was no correct answers or number of categories. However, they were asked to have more than one category and fewer categories than the maximum number of cards;
• One person in the pair timed how long it took to do the sort but participants were asked not to try to be fast. However, they should also not go too slowly to avoid indecision about categories.

After sorting cards, participants used a spreadsheet to record the following information:
• Label of each category;
• Number of cards in each pile and the cards associated with this pile;
• Lottery number;
• The time taken to complete the sort.

For the online sorts, participants were asked to go the Web address that was provided. The website presented similar instructions as those described for the oncard sorts, and participants were asked to provide their lottery number. The website automatically recorded the labels of the categories, the number of cards in each pile and pile names, the lottery number, and the time taken to complete the sort.

During the whole process, participants were aware that they could withdraw at any time without any academic penalty.
In the Turkish part of the study, data were collected during several modules in Yeditepe University and Bogazici University. The procedure for the Turkish part of the study was same as that followed in the UK except that the questionnaire used for detecting learning styles which in Turkey was the paper based (T)ILS. For the card sorts, only the online procedure was used.

2.4 Data preparation and analysis for learning styles

The distributions of scores on the four dimensions of the ILS and (T)ILS for the participants in the UK and Turkey respectively were inspected. For the Visual-Verbal (Vis-Ver) dimension, scores were heavily skewed towards the Visual end of the scale. To create appropriate groups on each dimension for analysis, participants were divided into three approximately equal sized groups on each dimension. An Excel spreadsheet of the card groupings for each ILS/(T)ILS dimension was created. That means 12 (4 dimensions x 3 groups per dimension) spreadsheets were prepared for the data from all the participants for the UK and Turkish parts of the study for museum and news cards.

Cluster analyses were performed for each ILS/(T)ILS group for both the museum and news cards, and separately for the UK and Turkish participants.

Clusters were compared calculating the minimum edit distances (MEDs) between the different groups within each ILS/(T)ILS dimension and with the a priori card groupings. As was defined before, the a priori groups refer to the groupings used on the actual museum and news websites. To calculate MEDs, a distance function is used to measure the distance between two card sorts. Edit distance is the minimum number of operations needed to adapt one card sort into another. For adaption, the basic operation is to move one card from a group to another (Deibel, Anderson & Anderson, 2005). For instance for two sorts, X and Y have the following groups:

X: X1={1,2}, X2={3,4,5,6}, X3={7,8}, X4={9,10}
Y: Y1={1,2,3,9}, Y2={4,5,6,7}, Y3={8}, Y4={10}

The MED between sort Y and sort X can be calculated by moving several cards. “3” should be moved from Y1 to Y2; “9” should be moved from Y1 to Y4 and “7” should be moved from Y2 to Y3. So, a minimum of three moves is needed to convert sort Y to sort X.
To investigate how participants’ learning style affected their groupings of the menu items for museum and news websites, I compared the cluster analyses of the participants on each of the four ILS/(T)ILS dimensions. This analysis was conducted separately for the UK and Turkish participants, as they had undertaken the ILS in different languages and viewed the cards in different languages.

2.5 Data preparation and analysis for cultural differences

To analyze the cultural differences, the groupings of the museum and news cards by the participants in the UK and Turkish parts of the study were compared. For this the following steps were taken:
• Excel spreadsheets were created for both the museum and news cards (two spreadsheets for participants in UK (who comprised British, Chinese and Indian cultural groups) and two spreadsheets for participants in Turkey).
• Cluster analysis was performed for these four groups.

3 Results

3.1 Effects of learning styles on card groupings

Tables 6-13 present the MED results for the UK participants and tables 14-21 present the MED results for the Turkish participants. Several abbreviations are used in the tables for the ILS/(T)ILS dimensions. Act-Ref stands for Active-Reflective, Sen-Int stands for Sensing-Intuitive, Vis-Ver stands for Visual-Verbal and Seq-Glo stands for Sequential-Global dimensions of the Felder-Silverman LSM.
Table 6 and table 7 show the groupings of UK participants on the Act-Ref dimension for the museum and news card sets, respectively. As illustrated in table 6, participants who are Act in learning style on average group 27.5% of the cards differently from the a priori groups for the museum card set. Furthermore, participants who are Act in learning style on average group 18.0% of the cards differently from the a priori groups for the news card set. Therefore, the UK Act participants’ groupings for both museum and news card sets are more distinctive from the a priori groups than the Balanced Act-Ref and Ref groups. In the museum card set, the Balanced Act-Ref participants’ groupings are closer to the a priori groups than the Ref participants’ groupings (15.0% for the Balanced Act-Ref, 17.5% for the Ref) whereas in the news sets, both the participants’ groupings in Balanced Act-Ref and the Ref groups are closer to the a priori groups (16.0% for both the Balanced Act-Ref and the Ref).

A priori

 

Act

(n = 38)

Balanced Act-Ref

(n = 38)

Ref

(n = 36)

A priori 27.5 15.0 17.5
Act 27.5 12.5 15.0
Balanced Act-Ref 15.0 12.5 2.5
Ref 17.5 15.0 2.5

Table 6: Minimum Edit Distance (MED) (%) for Museum Card Set for UK Data (Act-Ref Dimension)

  A priori Act

(n = 31)

Balanced Act-Ref

(n = 38)

Ref

(n = 25)

A priori 18.0 16.0 16.0
Act 18.0 10.0 10.0
Balanced Act-Ref 16.0 10.0 4.0
Ref 16.0 10.0 4.0

Table 7: MED (%) for News Card Set for UK Data (Act-Ref Dimension)

The UK participants’ groupings for museum and news card sets on the Sen-Int dimension are illustrated in table 8 and table 9. In the museum card set, participants who are in the Balanced Sen-Int group on average group 27.5% of the cards differently from the a priori groups. In the news card set, participants in the Sen group on average cluster 20.0% of the cards differently from the a priori groups. In the museum card set, the Sen participants’ groupings are closer to the a priori groups than the participants’ groupings in Balanced Sen-Int and the Int groups (17.5% for the Sen, 27.5%, for the Balanced Sen-Int and 20.0% for the Int). In the news sort, both participants’ groupings in Balanced Sen-Int and Int groups are closer to the a priori groups than the participants’ groupings in the Sen group (16.0% for both the Balanced Sen-Int and Int).

  A priori Sen

(n = 47)

Balanced Sen-Int

(n = 35)

Int

(n = 30)

A priori 17.5 27.5 20.0
Sen 17.5 15.0 5.0
Balanced Sen-Int 27.5 s15.0 12.5
Int 20.0 5.0 12.5

Table 8: MED (%) for Museum Card Set for UK Data (Sen-Int Dimension)

  A priori Sen

(n = 41)

Balanced Sen-Int

(n = 25)

Int

(n = 28)

A priori 20.0 16.0 16.0
Sen 20.0 6.0 6.0
Balanced Sen-Int 16.0 6.0 2.0
Int 16.0 6.0 2.0

Table 9: MED (%) for News Card Set for UK Data (Sen-Int Dimension)

Table 10 and table 11 show the groupings of UK participants on the Vis-Ver dimension for museum and news card sets, respectively. As demonstrated in table 10, participants who are Very Strong Vis in learning style on average group 27.5% of the cards differently from the a priori groups for the museum card set. Nevertheless, in news card set participants who are in the Strong & Moderate Vis group on average group 22.0% of the cards differently from the a priori groups. In the news card set, the Balanced Vis-Ver & Ver participants’ groupings are closer to the a priori groups than the Very Strong Vis and the Strong & Moderate Vis participants’ groupings (20.0% for the Very Strong Vis, 22.0% for the Strong & Moderate Vis) whereas in the museum set both Strong & Moderate Vis and the Balanced Vis-Ver & Ver participants’ groupings are closer to the a priori groups (22.5% for both the Strong & Moderate Vis and the Balanced Vis-Ver & Ver).

  A priori Very Strong Vis

(n = 47)

Strong & Moderate Vis

(n = 34)

Balanced Vis-Ver & Ver

(n = 31)

A priori 27.5 22.5 22.5
Very Strong Vis 27.5 10.0 12.5
Strong & Moderate Vis 22.5 10.0 12.5
Balanced Vis-Ver & Ver 22.5 12.5 12.5

Table 10: MED (%) for Museum Card Set for UK Data (Vis-Ver Dimension)

  A priori Very Strong Vis

(n = 25)

Strong & Moderate Vis

(n = 44)

Balanced Vis-

Ver & Ver

(n = 25)

A priori 20.0 22.0 14.0
Very Strong Vis 20.0 16.0 10.0
Strong & Moderate Vis 22.0 16.0 16.0
Balanced Vis-Ver & Ver 14.0 10.0 16.0

Table 11: MED (%) for News Card Set for UK Data (Vis-Ver Dimension)

The UK participants’ groupings for museum and news card sets on the Seq-Glo dimension are illustrated in table 12 and table 13. Participants who are Seq in learning style on average group 22.5% of the museum cards and 28.0% of the news cards differently from the a priori groups. Moreover, in both card sets, Glo participants’ groupings are closer to the a priori groups than the Balanced Seq-Glo and the Seq participants’ groupings (15.0% for the museum card set, 18.0% for the news card set).

  A priori Seq

(n = 43)

Balanced Seq-Glo

(n = 39)

Glo

(n = 30)

A priori 22.5 17.5 15.0
Seq 22.5 5.0 7.5
Balanced Seq-Glo 7.5 5.0 2.5
Glo 15.0 7.5 2.5

Table 12: MED (%) for Museum Card Set for UK Data (Seq-Glo Dimension)

  A priori Seq

(n = 43)

Balanced Seq-Glo

(n = 29)

Glo

(n = 22)

A priori 28.0 20.0 18.0
Seq 28.0 10.0 12.0
Balanced Seq-Glo 20.0 10.0 2.0
Glo 18.0 12.0 2.0

Table 13: MED (%) for News Card Set for UK Data (Seq-Glo Dimension)

Table 14 and table 15 show the groupings of Turkish participants on the Act-Ref dimension for the museum and news card sets, respectively. As illustrated in table 14, participants who are Ref in learning style on average group 52.5% of the cards differently from the a priori groups for the museum card set. Furthermore, participants who are Ref in learning style on average group 10.0% of the cards differently from the a priori groups for the news card set. In addition, participants who are Balanced Act-Ref in learning style on average group 10.0% of the cards differently from the a priori groups for the news card set. In the museum card set, the Act participants’ groupings are closer to the a priori groups than the Balanced Act-Ref participants’ groupings (17.5% for the Act, 40.0% for the Balanced Act-Ref). In the news set, the Act participants’ groupings are closer to the a priori groups (8.0% for the Act) as well.

  A priori Act

(n = 27)

Balanced Act-Ref

(n = 38)

Ref

(n = 20)

A priori 17.5 40.0 52.5
Act 17.5 35.0 42.5
Balanced Act-Ref 40.0 35.0 37.5
Ref 52.5 42.5 37.5

Table 14: MED (%) for Museum Card Set for Turkish Data (Act-Ref Dimension)

  A priori Act

(n = 26)

Balanced Act-Ref

(n = 41)

Ref

(n = 20)

A priori 8.0 10.0 10.0
Act 8.0 10.0 6.0
Balanced Act-Ref 10.0 10.0 8.0
Ref 10.0 6.0 8.0

Table 15: MED (%) for News Card Set for Turkish Data (Act-Ref Dimension)

The Turkish participants’ groupings for museum and news card sets on the Sen-Int dimension are illustrated in table 16 and table 17. In the museum card set, participants who are Sen in learning style on average group 47.5% of the cards differently from the a priori groups. In the news card set, both Sen and Int participants on average group 8.0% of the cards differently from the a priori groups. In the museum card set, the Int participants’ groupings are closer to the a priori groups than the Sen and the Balanced Sen-Int participants’ groupings (37.5% for the Balanced Sen-Int). In the news sort, the Balanced Sen-Int participants’ groupings are closer to the a priori groups than the Sen and the Int participants’ groupings (8.0% for both the Sen and 35.0% for the Int).

  A priori Sen

(n = 36)

Balanced Sen-Int

(n = 32)

Int

(n = 17)

A priori 47.5 37.5 35.0
Sen 47.5 22.5 22.5
Balanced Sen-Int 37.5 22.5 15.0
Int 35.0 22.5 15.0

Table 16: MED (%) for Museum Card Set for Turkish Data (Sen-Int Dimension)

  A priori Sen

(n = 36)

Balanced Sen-Int

(n = 34)

Int

(n = 17)

A priori 8.0 6.0 8.0
Sen 8.0 10.0 10.0
Balanced Sen-Int 6.0 10.0 10.0
Int 8.0 10.0 10.0

Table 17: MED (%) for News Card Set for Turkish Data (Sen-Int Dimension)

Table 18 and table 19 show the groupings of the Turkish participants on the Vis-Ver dimension for the museum and news card sets, respectively. As illustrated in table 18, participants who are Strong & Moderate Vis in learning style on average group 45.0% of the cards differently from the a priori groups for the museum card set. Furthermore, participants who are Strong & Moderate Vis in learning style on average group 10.0% of the cards differently from the a priori groups for the news card set. Therefore, the groupings of Strong & Moderate Vis Turkish participants for both museum and news card sets are more distinct from the a priori than the Very Strong Vis and Balanced Vis-Ver & Ver participants’ groupings. In the museum card set, the Very Strong Vis participants’ groupings are closer to the a priori groups than the Balanced Vis-Ver & Ver participants’ groupings (37.5% for the Very Strong Vis, 40.0% for the Balanced Vis-Ver & Ver) whereas in the news set both the Very Strong Vis and the Balanced Vis-Ver & Ver participants’ groupings are closer to the a priori groups (8.0% for both the Very Strong Vis and the Balanced Vis-Ver & Ver).

  A priori Very Strong Vis

(n = 20)

Strong & Moderate Vis

(n = 38)

Balanced Vis-Ver & Ver

(n = 27)

A priori 37.5 45.0 40.0
Very Strong Vis 37.5 27.5 17.5
Strong & Moderate Vis 45.0 27.5 30.0
Balanced Vis-Ver & Ver 40.0 17.5 30.0

Table 18: MED (%) for Museum Card Set for Turkish Data (Vis-Ver Dimension)

  A priori Very Strong Vis

(n = 20)

Strong & Moderate Vis

(n = 38)

Balanced Vis-Ver & Ver

(n = 29)

A priori 8.0 10.0 8.0
Very Strong Vis 8.0 12.0 10.0
Strong & Moderate Vis 10.0 12.0 12.0
Balanced Vis-Ver & Ver 8.0 10.0 12.0

Table 19: MED (%) for News Card Set for Turkish Data (Vis-Ver Dimension)

Table 20 and table 21 show the groupings of Turkish participants on the Seq-Glo dimension for museum and news card sets, respectively. As illustrated in table 20, participants who are Glo in learning style on average group 47.5% of the cards differently from the a priori groups for the museum card set. Furthermore, participants who are Balanced Seq-Glo and Glo in learning style on average group 12.0% of the cards differently from the a priori groups for the news card set. In the museum card set, the Seq participants’ groupings are closer to the a priori groups than the Balanced Seq-Glo participants’ groupings (32.5% for the Seq, 37.5% for the Balanced Seq-Glo) whereas in the news set the Seq participants’ groupings are closer to the a priori groups (8.0% for the Seq).

  A priori Seq

(n = 19)

Balanced Seq-Glo

(n = 38)

Glo

(n = 28)

A priori 32.5 37.5 47.5
Seq 32.5 27.5 40.0
Balanced Seq-Glo 37.5 27.5 15.0
Glo 47.5 40.0 15.0

Table 20: MED (%) for Museum Card Set for Turkish Data (Seq-Glo Dimension)

  A priori Seq

(n = 18)

Balanced Seq-Glo

(n = 41)

Glo

(n = 28)

A priori 8.0 12.0 12.0
Seq 8.0 6.0 10.0
Balanced Seq-Glo 12.0 6.0 12.0
Glo 12.0 10.0 12.0

Table 21: MED (%) for News Card Set for Turkish Data (Seq-Glo Dimension)

3.2 Effects of cultural background on card groupings

To investigate the part of the first research question that is related to cultural background, MEDs between the card sorts from the British, Chinese, Indian, and Turkish participants were compared. The card sorts from the different cultural groups were also compared with the a priori. Tables 22 and 23 present the results for the MEDs for the various cultural groups in the study, for the museum and news cards respectively.

  A priori British

(n = 100)

Indian

(n = 24)

Chinese

(n = 16)

Turkish

(n = 85 )

A priori 30.0 32.5 25.0 52.5
British 30.0 12.5 5.0 52.5
Indian 32.5 12.5 10.0 52.5
Chinese 25.0 5.0 10.0 50.0
Turkish 52.5 52.5 52.5 50.0

Table 22: MED between Card Sorts by British, Chinese, Indian, and Turkish Participants for the Museum Card Set

For the museum card set, sorts by the Turkish participants are more distinct from the a priori than other sorts by any of the other cultural groups (52.5%) and Chinese the sort is closer to the a priori groups than the other cultural groups (although the number of participants in this group is only 16, so less confidence can be placed in this cultural group than the others, for which the numbers are higher). In addition, the Indian sorts are more distinct from the a priori than the other cultural sorts in the news card set (30.0%) and the Turkish and Chinese sorts is closer to the a priori groups than the other cultural sorts for the news card set (10.0%).
For the museum card set, the number and general nature of the groups produced by the British, Indian, and Chinese sorts are very similar. However, the particular cards grouped together are rather different for some of the groups. In particular, the cards in the groups “Collection” differ considerably among these three cultures. The Indian sorts typically produced a larger group for “Collection” with a number of cards from the a priori group “Shop” included in the group. Although the number of groups produced by the Turkish culture is the same with the other cultures, the general nature of the groups are very different. For example, the cards related to the “Shop” group are typically spread around other groups. This makes sense, since Turkish museums have only recently opened shops, so the concept of a museum shop is still very new in Turkey.

  A priori British

(n = 82)

Indian

(n = 21)

Chinese

(n = 20)

Turkish (n = 87 )
A priori 18.0 30.0 20.0 10.0
British 18.0 16.0 8.0 16.0
Indian 30.0 16.0 14.0 22.0
Chinese 10.0 8.0 14.0 20.0
Turkish 10.0 16.0 22.0 20.0

Table 23: MED between Card Sorts by British, Chinese, Indian, and Turkish Participants for the News Card Set

In the news card set, the number and nature of the groups produced by the four cultural groups varied considerably. The Turkish sorts produced the highest number of groups: seven. In addition, the British and Chinese sort produced six groups each, whereas the Indian sort produced only five groups. All sorts produced almost the same groups for “Opinion” and “Sports.” For “Money and Business,” the British, Chinese and Indian sorts were also very similar. However, the Turkish sort splits this category into two as “Money” and “Business.” The British, Turkish, and Chinese sorts also produced “Life style” with rather different groupings of particular cards whereas the Indian sort does not even have this group. The British and Turkish sorts produced “Entertainment” with almost the same cards, whereas the Chinese and Indian sorts produced this group with more cards. The British and Chinese sorts produced “News” with almost the same cards whereas the Turkish and Indian sorts produced this group with more cards. Nonetheless, the British sort produced groups for “Opinion,” and “News,” whereas the Indian and Chinese sorts produced “Entertainment” and “Non-factual” with rather different groupings of particular cards.

4 Discussion and conclusions

This study illustrated interesting and meaningful differences between users with different learning styles and among different cultural groups. Firstly, interesting and meaningful differences were found between British, Chinese, Indian, and Turkish participants in their average groupings of card relating to IAs of both museum and news websites. Particularly, in the museum domain, Turkish participants’ groupings produced substantially different groups compared to the a priori groups, and in the news domain, Indian participants produced substantially different groups compared to the a priori groups. Therefore, it might be useful if website designers study their audiences more carefully based on their cultural differences, possibly by using card sort studies to extract the mental models of these audiences.

A further valuable result demonstrated that participants with different learning styles produced different groups compared to the a priori groups. According to the results, a minimum 6.0% of the cards were grouped differently. This study also made a methodological contribution, showing that the card sort method could be used to show learning styles and cultural differences.
From these results, it is clear that there are differences in users’ categorizations and mental models of the IAs of websites based on their learning styles and cultural backgrounds. Therefore, there is a value to investigating the impact of learning styles and cultural background on website in more detail.

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Cite as:
. "The effects of learning styles and cultural background on understanding the information architectures (IAs) of information rich websites." MW17: MW 2017. Published February 6, 2017. Consulted .
https://mw17.mwconf.org/paper/the-effects-of-learning-styles-and-cultural-background-on-understanding-the-information-architectures-ias-of-information-rich-websites/


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