LEARNING-ORIENTED ASSESSMENT OF WIKI CONTRIBUTIONS

How to Assess Wiki Contributions in a Higher Education Learning Setting

Emilio J. Rodr´ıguez-Posada, Juan Manuel Dodero, Manuel Palomo-Duarte

and Inmaculada Medina-Bulo

Superior School of Engineering, University of C´adiz, C/ Chile 1, 11002 C´adiz, Spain

Keywords:

Computer-supported collaborative learning, Wikis, e-Learning assessment.

Abstract:

Computer-Supported Collaborative Learning based on wikis offers new ways of collaboration and encourages

participation. When the number of contributions from students increases, traditional assessment procedures of

e-learning settings suffer from scalability problems. In a wiki-based learning experience, some automatic tools

are required to support theassessment of such great amounts of data. We have studied readily available analysis

tools for the MediaWiki platform, that have complementary input, work modes and output. We comment our

experience in two Higher Education courses, one using HistoryFlow and another using StatMediaWiki, and

discuss the advantages and drawbacks of each system.

1 INTRODUCTION

Collaboration between students is often limited by lo-

cation and time constraints, causing the task to be

divided into a number of almost independent work

packages that are later merged into a ﬁnal handout.

The massive adoption of computers and Internet in

our life has reached the classrooms, where Computer-

Supported Collaborative Learning (CSCL) offers new

ways of real collaboration. In this context, wikis are

appropriate tools to support the dynamic real-time

teacher-student and student-student interactions that

are required to facilitate collaborative learning ex-

periences (Jaksch et al., 2008). A wiki is a web-

site that eases the collaborativecreation of interlinked

web pages. This allows for a massive collaboration

process, where several students located in different

places can modify the same web site simultaneously.

Nowadays, work-group skills are one of the main

goals of e-learning processes. This work is motivated

by the increasing need to implement group-based col-

laborative e-learning experiences, specially in Span-

ish Higher Education institutions that are immersed

in the European Higher Education Area (EHEA), in

which courses aim at achieving transferable compe-

tences and skills for life-long learning (Fallows and

Steven, 2000). However, there are some issues that

emerge when such competences have to be assessed

or evaluated.

Most usual competence assessment procedures

and instruments are based on the detailed inspection

of a number of learning outcomes (i.e. assignments,

reports, deliverables, etc.) that are collaborativelycre-

ated and delivered by learners. Examples of assess-

ment instruments are rubrics, control lists, check lists

and so forth (Walvoord and Anderson, 2009). The

assessment procedure usually involves teachers and

learners in a guided interaction during which they

have to ﬁll-in (or review) a number of evaluation

forms that hold the evaluation criteria, after inspect-

ing (or completing) each learning outcome. In these

procedures is pretty difﬁcult to assess especial aspects

of collaboration such as, among others, the effort and

contribution degree from each learner, how individ-

ual contributions are distributed and how they add to

the overal group work, how efﬁcient the resolution of

conﬂicts is as long as these happen, or what transfer-

able skills (e.g. analytic observation, authority and

leadership, thoroughness) can be elicited from learn-

ers’ contributions.

The main research question here is how a teacher

can assess an e-learning experience that produces a

large amount of data, in particular, when the students’

work is developed on or supported by a wiki. This

issue is known in Computer Science as scalability

(i.e. how well a system can adapt to increased de-

mands). Traditional assessment procedures do not

scale well if the number of students or groups of stu-

dents is too great, or if the number or complexity of

learning outcomes is not easy to handle. Usually,

evaluators’ workload is alleviated by extending the

assessment procedure so that students be part of it,

J. Rodríguez-Posada E., Manuel Dodero J., Palomo-Duarte M. and Medina-Bulo I..

LEARNING-ORIENTED ASSESSMENT OF WIKI CONTRIBUTIONS - How to Assess Wiki Contributions in a Higher Education Learning Setting.

DOI: 10.5220/0003308700790086

In Proceedings of the 3rd International Conference on Computer Supported Education (CSEDU-2011), pages 79-86

ISBN: 978-989-8425-50-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

either through self-assessment or peer assessment of

learning outcomes (Barootchi and Keshavarz, 2002).

Not discussing the didactic advantage of these evalu-

ation procedures for the formative evaluation during

the learning process (Boud, 2007), the teacher might

sometimes miss important information for an eventual

summative evaluation (Falchikov, 2005).

From the analysis of contributions to a wiki, a

teacher (or even a student) can have worthwhile in-

formation to assess, self-assess or peer-assess the re-

sults of a collaborative learning experience (Cubric,

2007a). The scope of this paper is limited to experi-

menting with software-supported assessment of wiki-

based contributions for traditional teacher-based eval-

uation procedures. Our work is focused on Medi-

aWiki software, the most popular wiki system nowa-

days (it is used in Wikipedia and related projects, like

Wiktionary, Wikibooks or Wikisource).

The rest of this document is organized as fol-

lows: ﬁrst, some techniques for the assessment of

wiki-based learning experiences are discussed and

some support tools are analysed. Third section deals

with two experiences, each one using a different tool,

namely StatMediaWiki and HistoryFlow, and shows

how their role is in the learning process. Finally, some

discussions about the experiences and conclusions are

provided, along with an outline of the future work.

2 WIKI-BASED

COLLABORATIVE LEARNING

EXPERIENCES

A great number of collaborative e-learning processes

are supported by wikis, including Higher Education

(Jaksch et al., 2008) and blended learning experiences

(Cubric, 2007b). Wikis are collaborative tools that

serve multiple purposes in Higher Education, spe-

cially for collaborative writing, assessment (Cubric,

2007a; Liu and He, 2008), software development

(Louridas, 2006) and project management (Trentin,

2008). In the following, a number of wiki-based pro-

cedures and tools are studied to analyse how well they

support the research issue, i.e. how they can help the

evaluator to assess an e-learning experience.

2.1 Assessment of Wiki-based Learning

Experiences

Initial proposals to support assessment in wikis tried

to generate question item models on a wiki for for-

mative assessment (Liu and He, 2008). Similar ap-

proaches have been provided with either formative

or summative evaluation purposes (Cubric, 2007a;

Cubric, 2007b). First proposals that evaluate learning

outcomes on the basis of individual students’ contri-

butions to the wiki deﬁne metrics in terms of generic

wiki-based log indicators (e.g. number and size of

contributions) (Trentin, 2008) or a set of pre-deﬁned

categories for contributions (de Pedro, 2007). Al-

though such analyses are done on the basis of direct

observation and measurement of the wiki log values,

a number of useful tools have been provided to illus-

trate these in a more abstract fashion. Next, some of

these tools are summarized and described.

2.2 Wiki-based Contributions Analysis

Tools

After a bibliographical search, three tools supporting

MediaWiki wiki-based collaborative learning experi-

ences have been found:

HistoryFlow by the MIT Media Lab and the Col-

laborative User Experience Research Group in

IBM (Vi´egas et al., 2004). It is a data analysis

tool that retrieves the history of a given page of a

wiki. It produces diagrams as graphical represen-

tation of differences in sentences between consec-

utive versions of that page throughout time. This

is ﬁner than usual behavior in Wikipedia, which

checks differences between paragraphs. Different

aspects of authorship can be highlighted with His-

toryFlow, i.e. contributions from all authors, con-

tributions from a single author, new contributions

from any author and content persistence.

WikiXRay by LibreSoft Research Group at the Uni-

versity Rey Juan Carlos (Ortega et al., 2007) is

a set of Python and GNU R scripts that makes a

quantitative analysis of public database dumps of

a MediaWiki website. WikiXRay builds an SQL

database with the data obtained from the dump

and creates additional tables with useful quanti-

tative information. A number of scripts are pro-

vided to generate many statistics and graphics,

and new ones can be created to obtained cus-

tomized output.

StatMediaWiki by the SPI&FM Research Group at

the University of C´adiz (Rodr´ıguez et al., 2010)

is a tool that collects and aggregates information

that help to analyze the status and development

of a MediaWiki installation. StatMediaWiki ver-

sion 1.0 generates CSV ﬁles and static XHTML

1.0 standard-compliant web pages including ta-

bles and graphics, showing timelines for the con-

tent evolution, activity charts for users and pages,

rankings, tag clouds, etc. The anonymous op-

CSEDU 2011 - 3rd International Conference on Computer Supported Education

Table 1: Summary of main features in wiki analysis tools (X: Yes, ✗: No).

Features HistoryFlow WikiXRay StatMediaWiki

Availability

Open source license ✗ X X

Free download X X X

Interaction

Command line ✗ X X

Graphical User Interface X ✗ ✗

Input modes

Connection to MediaWiki database ✗ ✗ X

MediaWiki database dumps ✗ X ✗

Histories exported with Special:Export X ✗ ✗

Analysis modes

Global ✗ X X

Page-by-page X ✗ X

User-by-user ✗ ✗ X

Content evolution X X ✗

Output modes

Optional anonymous ✗ ✗ X

(X)HTML X ✗ X

Tables ✗ X X

Charts (S: Static, D: Dynamic) D S S

CSV (Spreadsheet neutral format) ✗ ✗ X

tional feature allows to hide sensitive information

and the edit user patterns when desired.

All the technical information about these tools is sum-

marized in table 1.

3 CASE STUDIES

In this section we comment our experience using Stat-

MediaWiki and HistoryFlow in a Higher Education

setting. The case study of StatMediaWiki consisted

of two courses, while that of HistoryFlow was devel-

oped in a course.

3.1 Case Studies using StatMediaWiki

StatMediaWiki generates three kinds of analysis, i.e.

global, user-focused and page-focused. That helps in

understanding the dynamics of the wiki and allows to

know about each element in greater depth. The in-

formation is divided into sections, and it is presented

as graphs and tables. All the graphs (lines and bars)

use three different colours to split edits by MediaWiki

standard namespaces: all edits, only main edits (i.e.

edits in articles), and only talk page edits (i.e. discus-

sions about article contents). The delivered report is

a collection of HTML pages, in which every item is

linked to another page that holds more detailed infor-

mation and CSV ﬁles.

StatMediaWiki has been successfully applied to

the assessment of wikis developed in two courses dur-

ing the 2009/10 academic year: WikiHaskell

and

Available at http://osl.uca.es/wikihaskell

WikiRA

. They both used the same technologies, but

different working methodologies. As can be seen

in the StatMediaWiki output (StatMediaWiki, 2010),

this caused differences in results.

On the one hand, WikiHaskell is a wiki where 3-

member teams created Spanish-language documenta-

tion about libraries for the Haskell programming lan-

guage. Every month they had to do a presentation

showing the state of their assignment. That makes ef-

fort to be generally well-balanced during the course

time (see Fig. 1), so the work on the wiki had a very

positive inﬂuence in students’ ﬁnal marks.

Figure 1: WikiHaskell progress timeline.

On the other hand, WikiRA contains lecture notes

about automated reasoning. The notes were freely

inserted by students during the course. Neverthe-

less, the scarce number of groups (that usually makes

Available at http://osl.uca.es/wikira

LEARNING-ORIENTED ASSESSMENT OF WIKI CONTRIBUTIONS - How to Assess Wiki Contributions in a Higher

Education Learning Setting

all members cooperate and work), the loose restric-

tions to their work (i.e. there were no intermedi-

ate presentations) and the hard topic of the course

(automated reasoning) caused not so positive results,

with many students only contributing near the end of

the semester, and other even abandoning the course.

We can see this in Fig. 2, where wiki content grows

sharply only a few days after the end of the chart.

Note that, in June, WikiRA suffered a spambot attack

where the content was blanked and replaced by some

spam links. Later, the admin restored the texts. That

explains the abrupt fall and growth. To preventsimilar

attacks the MediaWiki captcha extension was enabled

since then.

Figure 2: WikiRA progress timeline.

Using StatMediaWiki we have been able to derive in-

formation about the following items:

• The number of updates and what pages are af-

fected by updates. This information allows us

to know the average number of contributions per

student, besides identifying which sections of the

wiki suffered from a weaker development effort,

as well as providing valuable help when thinking

about how to foster this effort.

• A potential ranking of students. We can classify

our students by taking into account the number of

contributions, at what hours and days of the week

they do their work, and their participation level.

In the case of WikiHaskell, we have observed in

the global ranking of contributions that the 10

most proliﬁc students (about 20% of the students

in the course) have added 50% of the contents to

the wiki. Also, students work harder on the previ-

ous days to the lecture (i.e. Wednesday), and the

edit rate falls abruptly on the next day (i.e. Thurs-

day), as seen in Fig. 3.

• The potential student work proﬁles. In particular,

Figure 3: WikiHaskell activity graph by day of the week.

both in WikiHaskell and WikiRA we have identi-

ﬁed ﬁve different proﬁles:

– Continuous: This is the optimal proﬁle. The

student is contributing continuously during the

whole project lifetime.

– Stepwise: This is still a good proﬁle, though

unlike the previous there are some short regu-

lar periods when the student is not making any

progress.

– Early peak. This is the proﬁle of abandonment.

A genuine effort is done right at the beginning,

but soon the number of contributions vanishes.

– Middle peak: This proﬁle ﬁts the behaviour of

a majority of our students. Most of the work

is done at the middle of the project. It seems

that one day, they realize that they do have to

contribute to the wiki to pass the course, decide

to do it as soon as possible and then forget about

it.

– Late peak: This proﬁle reﬂects the common sit-

uation of some students trying to push forward

their contributions near the deadline.

Since StatMediaWiki analyses all the wiki, we

have had a wider comprehension of the dynamics of

the wiki and discovered unexpected patterns, such as:

how students work together in different pages; users

who usually ﬁx errors, add improvements or leave

suggestions to other groups’ pages; how coordination

happens by using talk pages; pages that signiﬁcantly

attract more attention, etc. For example, table 2 shows

the ranking of pages edited by one of the most active

students. We can see that she contributed to different

“Main” pages, so helping other groups. Additionally,

she also contributed to a “User talk” page, showing

communication with other student. There is also an-

other editing to a “Talk” page to provide a comment

about other group’s “Main” page.

CSEDU 2011 - 3rd International Conference on Computer Supported Education

Table 2: Ranking of pages edited by a certain user.

# Page Namespace Edits

... ... ... ...

12 Biblioteca ... Gtk2Hs Main 4

13 Biblioteca ... Cabal Main 4

14 Primeros ... Gtk2Hs Main 3

... ... ... ...

42 User talk:...Jose User talk 1

43 Talk:Biblioteca ... Cabal Talk 1

3.2 Case Study using HistoryFlow

HistoryFlow provides a graphical view of users’ edit-

ing activity throughout several versions of a certain

wiki page. It enables visualizing cooperation and con-

ﬂicts between wiki authors (Vi´egas et al., 2004). The

following HistoryFlow facilities can be used for that

aim:

• Display contributions of all authors in different

colors

• Highlight the contributions of a single authors

throughout all versions

• Highlight new text content in each page version

• Show the persistence of different contributions

over time

HistoryFlow diagrams are similar to Inserlberg’s

parallel coordinates (Inselberg and Dimsdale, 1990),

in which the horizontal axis represents a timeline for

all versions of a wiki page, and the lenght of each

vertical line is proportional to the page version size.

Each vertical line consists of a number of colored

parts that represent adjacent text passages, using a dif-

ferent color for each contributing user.

We have used HistoryFlow diagrams and visual-

ization facilities to illustrate competences drawn by

learners. The learning experience entails an 8-week

project-oriented learning course in which a number of

software projects are developed by 3-member teams.

All project-related documents and deliverables are

hosted in a single wiki page, so it can be eventu-

ally subject to analysis with the help of HistoryFlow.

Fig. 4 depicts some diagrams as they are generated

from a set of project-oriented learning wiki-based de-

liverables

. From these diagrams, we made the fol-

lowing considerations on some observable learning

competences:

• Overall effort: The amount of contributions

throughtout time can be observed as the growth

of vertical lines on the timeline. For instance,

Fig. 4 shows that activity of all projects occurs

Available at http://mediawiki.uca.es/

within the 8 weeks of the course, showing a con-

siderable amount of work (all timelines are quite

ﬁne-grained). The curve shape indicates whether

such work has been equally distributed through-

out time (i.e. the curve is approximately straight,

as in Fig. 4(a)) or there have been work intervals

having intense or scarce activity —i.e. there are

sharp shapes or drops of the curve, as in Fig. 4(b).

A proportional spacing of the timeline representa-

tion can show up such intervals.

• Distribution of effort: since each learner’s contri-

bution is colored differently, the distribution of in-

dividual contributions to the wiki text is clear. Be-

sides, a numeric percentage for learners’ amount

of contributions is shown next to the user name.

For instance, the diagram of Fig. 4(c) shows a

balanced distribution of work (45%-27%-28%)

among the team members. Instead, the distribu-

tion of work of Fig. 4(a) is more biased towards

two members (11%-58%-31%).

• Work organization: Pieces of text can be writ-

ten and moved on a wiki page. Reorganization

and movements of text can be observed as slashed

line patterns that cross the diagram. For instance,

the Fig. 4(b) depicts this pattern in the middle of

the diagram; the same happens at the end of the

project on Fig. 4(a).

• Conﬁct resolution: An interesting opportunity of

wikis is to check conﬂicts that might emerge dur-

ing text editing. This can be analysed on the wiki

logs, but then it is a hard task. HistoryFlow dia-

grams show editing conﬂicts as a zig-zag pattern

throughout the timeline. Unfortunately, such con-

ﬂicts did not appear in our project-oriented learn-

ing experience.

• Other transferable skills: The analysis of Histo-

ryFlow diagrams may be indicative of some other

skills that learners demonstrate during the learn-

ing process. For instance, leadership can be seen

as ﬁrst-mover events (i.e. to strike while the

iron is hot) when a text part that was early pro-

vided persists along older versions of the page.

HistoryFlow provides a special visualization that

plots older parts with darker colors, as depicted

in Fig. 4(a). The darker parts of the older (i.e.

righter) side of the timeline is provided by user

Mangel.vera. Along with the colored group con-

tribution view showing the provenance of text,

this user can be reasonably thought of behaving

as a leader of the team. Other transferable skills,

such as analytic observation abilities or thorough

attention to details can be analysed on these dia-

grams, as described in (Dodero et al., 2009).

LEARNING-ORIENTED ASSESSMENT OF WIKI CONTRIBUTIONS - How to Assess Wiki Contributions in a Higher

Education Learning Setting

(a) Project e-Tare@s text age diagram

(b) Project LEARN group contribution diagram (c) Project CadiBlog group contribution diagram

Figure 4: HistoryFlow visual representation of wiki deliverables of different projects.

4 DISCUSSION

We have reviewed some e-learning applications of

available MediaWiki analysis tools. They are summa-

rized in table 3. Several conclusions can be extracted

from this study.

As for HistoryFlow and StatMediaWiki, both pro-

vide a simple user output, but their behavior is quite

different. On the one hand, HistoryFlow operatesonly

on single wiki pages. This limits its capability of ana-

lysis, as it does not consider the wiki interlinking na-

ture. Anyway, it is interesting for isolated analysis of

critical pages or learning outcomes that can take up

only one page. It focuses on changes to the page text

contents, not so deeply considered in StatMediaWiki.

It provides an usable graphical information but a lim-

ited numerical output.

Unfortunately, HistoryFlow remains the same

since 2005, therefore no future features are expected

to be added in the near future. This, coupled with the

lack of source code available, makes us not to rec-

ommend it as the only tool to support a course, as any

internal change in MediaWikiexport format can make

it obsolete.

On the other hand, StatMediaWiki quantitatively

analyzes the evolution of the whole wiki, providing

information on the overall content, contributions of

every user to the whole wiki and page-by-page ana-

lysis. This way, the information provides a wider

analysis of the work that a user has developed in the

whole wiki, not just one page. The information is

summarized in tables and charts and some options

CSEDU 2011 - 3rd International Conference on Computer Supported Education

Table 3: Summary of skills assessed by wiki analysis tools (X: Yes, ✗: No, i.p.: restricted to an isolated page of the wiki).

Skill / Tool HistoryFlow WikiXRay StatMediaWiki

Work effort X(i.p.) X X

Work distribution/collaboration X(i.p.) X X

Work organization X(i.p.) X ✗

Conﬂict resolution X(i.p.) X ✗

Transferable skills (e.g. leadership) X(i.p.) X X

are available like tag clouds, user rankings and anony-

mous output (which are specially interesting for pub-

lic wikis). Nevertheless, the analysis is quantitative,

so the software is not able to show some inner situa-

tions, such as conﬂict resolution.

Among the new features desirable for StatMedia-

Wiki, there is a categorical analysis feature on the

way. Using it, if a group of students works horizon-

tally in a set of properly categorized pages, the teacher

can obtain a better view of their work. Also adding

some dynamism to graphics (like those of History-

Flow), could improve its usability.

5 CONCLUSIONS AND FUTURE

RESEARCH

In this paper we have presented the main needs for

a correct assessment of wiki contributions and some

tools that support it. We have compared e-learning

applications of available MediaWiki analysis tools.

First, we can see a great difference between Wiki-

XRay and the other two tools. WikiXRay is by far the

most powerful tool, but requires specialized knowl-

edge to use it. It builds an SQL database with the

data from a MediaWiki dump and creates additional

tables with useful quantitative information. A num-

ber of default scripts can be used, or new ones be cre-

ated to generate customized analyses (but in this case,

GNU R and/or MySQL skills are desirable). Up to

date no information of any academic e-learning case

study has been found, so it remains as a future work.

Secondly, we can conclude that HistoryFlow and

StatMediaWiki can provide an acceptable support for

wiki contributions analysis in collaborative and co-

operative learning processes, specially if used com-

plementarily. StatMediaWiki gives a general picture

of the wiki, so students’ effort, work distribution and

other skills can be easily assessed. For critical pages,

HistoryFlow can provide a deeper look of some in-

ternal situations concerning content persistence, like

conﬂicts.

The analysis of these two case studies intent is not

to be comparative. Each of them present a different

scenario (problem-based versus project-based learn-

ing), but can share a common assessment method.

There is a long way ahead, but usual skills demanded

in collaborative and cooperative learning process can

be assessed, like work effort, distribution and collab-

oration, authority or conﬂict.

Next academic year, the two experiences will be

repeated again, developing new projects. We will try

to use StatMediaWiki to detect students with an early

peak in contributions and help them not abandoning

the course. Besides, a course on Operating Systems

Administration is planned to include a double-level

peer-assessment on a wiki (i.e., students assess each

other and those assessments are in turn assessed by

teachers). It is a compulsory course, so the number of

students can be quite high, and it can produce inter-

esting results.

ACKNOWLEDGEMENTS

This work has been funded by the PAIDI programme

of the Andalusian Government, ASCETA project

(P09-TIC-5230); and Proyectos de Innovaci

on Ed-

ucativa Universitaria del Personal Docente e Inves-

tigador programme of the University of C´adiz and

the Andalusian Government, project “Empleo de tec-

nolog´ıas colaborativas Web 2.0 para fomentar el tra-

bajo en equipo del alumnado” (code PIE-101).

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