Learning Analytics for Interpreting

Davide Taibi

, Francesca Bianchi

, Philipp Kemkes

and Ivana Marenzi

Institute for Educational Technology, National Research Council of Italy, Palermo, Italy

University of Salento, Lecce, Italy

L3S Research Center, Hannover, Germany

Keywords:

Technical Vocabulary for Interpreting, Glossary, Learning Analytics Dashboard.

Abstract:

An important activity in the life of interpreters is terminology work. A primary method for learning technical

vocabulary is the creation of personal glossaries. The current paper describes the design and creation of a

system that guides the students in autonomous vocabulary work, supports the students’ learning progress,

and helps the teacher in monitoring the student commitment to and achievements in the creation of personal

glossaries. The system includes a tool for the creation of glossaries, a tracking system that records the students’

actions and the websites they visit while searching the Web for linguistic and content information, and a

learning analytics dashboard. The system was tested on a class of 34 university students training in interpreting

and the paper reports some preliminary results.

1 INTRODUCTION

Interpreter training is a challenging task, especially in

academic institutions, where teachers are required to

train perspective interpreters in a wide range of cog-

nitive, linguistic and practical skills (e.g. P

ochhacker,

2003; Gile, 2009) in a short period of time and of-

ten with fairly large classes. One of the skills that

interpreters need to master is the ability to expand

their vocabulary and content knowledge in technical

areas. A primary and largely used method for ac-

quiring technical vocabulary is the creation of per-

sonal glossaries. Creating glossaries not only favours

terminology acquisition (AIIC, 1999), but is also a

way to learn something about the topic itself (Gile,

2002), content knowledge being a fundamental re-

quirement for the interpreter in all interpreting events

(e.g. Moser, 1978; Seleskovitch, 1978). A range of

existing software programs can be used for creating

glossaries, from MS Word or MS Excel (e.g. Fons i

Fleming, 2009; Jiang, 2015), to programs speciﬁcally

developed by or for interpreters and translators, such

as Interplex

, Lookup

, and SDL Multiterm

. None

of these tools was created for teaching purposes or in-

tegrates a learning analytics system.

http://www.fourwillows.com/interplex.html

http://www.traductorado.edu.ar/lookup/

http://www.sdl.com/it/solution/language/terminology-

management/multiterm/

Within a wider study aiming to provide teachers of

interpreting with an integrated system that may sup-

port them in their teaching needs, the current paper

focuses on vocabulary searches and glossary creation,

and describes a recent release of the LearnWeb en-

vironment integrating a tool that guides students in

the creation of personal glossaries in keeping with the

steps and needs advocated and/or described in the lit-

erature on interpreting (AIIC, 1999; Gile, 2002; Set-

ton, 2010; Fons i Fleming, 2009; Jiang, 2015). The

system also keeps track of the students’ activities in

the glossary, as well as of their searches on the Web,

and provides a dashboard to monitor the student’s ac-

tivities. This system aims not only to support the stu-

dents in the creation of glossaries, but also to help

the teachers monitor the students’ approaches to this

particular task and provide individual remedial feed-

back, if necessary. This paper provides a technical de-

scription of the environment in which the current tools

have been integrated (Section 2) and of the newly de-

veloped features (Section 3). As the system was tested

with a group of university students specialising in in-

terpreting, the paper also brieﬂy describes this exper-

imentation and reports some students’ comments on

the advantages and limitations of the current version

of the system (Section 4). Finally, some conclusions

are offered on the current experiment, and on future

developments of the system (Section 5).

Taibi, D., Bianchi, F., Kemkes, P. and Marenzi, I.

Learning Analytics for Interpreting.

DOI: 10.5220/0006774801450154

In Proceedings of the 10th International Conference on Computer Supported Education (CSEDU 2018), pages 145-154

ISBN: 978-989-758-291-2

145

2 THE ENVIRONMENT:

LEARNWEB

LearnWeb is a learning and competence development

environment that allows users to share and collabora-

tively work on resources collected from the Web or

user-generated (Marenzi and Zerr, 2012). It provides

users with a search interface for retrieving and shar-

ing resources across various Web 2.0 services, such as

YouTube, Flickr, Ipernity, Vimeo, and Bing, as well

as LearnWeb itself, and can offer a personal Web 2.0

learning space. The system has been continuously de-

veloped at the L3S research center in Hannover, in or-

der to provide a customized environment that supports

learners and educators in sharing, discovering, and

managing learning resources. The platform is charac-

terised by an iterative evaluation-driven design-based

research approach (Marenzi, 2014), i.e. an approach

in which researchers and practitioners collaborate in

real-world settings to cyclically assess and improve

the functionalities of a system (Wang and Hannaﬁn,

2005), tailoring them to the practitioners’ needs. The

current release of the system provides several innova-

tive features designed to support teachers and students

in individual and collaborative learning tasks. These

features include:

1. a personal learning space offering a seamless

overview of the entire set of learning resources

distributed across various Web 2.0 repositories;

2. searching, sharing and aggregation of different

learning resources;

3. annotation facilities, such as tagging and com-

menting, that can help users to discuss and select

the most relevant resources for their task;

4. Website archiving;

5. creative production of new meanings (collabora-

tive writing with GoogleDocs; collaborative pre-

sentation of learning outcomes with the Presenta-

tion Editor);

6. speciﬁc functionalities to support educational ac-

tivities, such as TED talk transcripts annotation

(Taibi et al., 2015);

7. automatic logging of the students’ actions.

The current release also includes features speciﬁcally

designed and developed to support the learning ac-

tivities of students attending a course on interpret-

ing. These features include a tool for the creation

of glossaries, a tracking system for logging the stu-

dents’ searches on the Web, and a learning analytics

system aimed at improving the efﬁcacy and effective-

ness of the processes involved in the creation of per-

sonal glossaries. These features are described in the

following sections.

3 DEVELOPING THE GLOSSARY

STRUCTURE AND INTERFACE

3.1 Rationale and Structure

The Glossary tool was speciﬁcally designed to meet

the needs of students of interpreting. The glossary

structure was inspired by scientiﬁc literature on inter-

preting (AIIC, 1999; Gile, 2002; Setton, 2010; Fons i

Fleming, 2009; Jiang, 2015), and is described below.

In LearnWeb, a glossary is a database and is consid-

ered by the system as a type of resource, on a par with

images, videos and text ﬁles. A glossary may host

a potentially inﬁnite number of entries. Each entry

is composed of at least two terms: the source term

and its translation into the target language. Synonyms

in the source or target language may be added to a

given entry

. Thus, depending on the presence of syn-

onyms, each glossary entry will include two or more

terms. Each term in a glossary entry is accompanied

by a series of attributes, corresponding to given ﬁelds

in the glossary interface; these enrich the description

of the term with optional data. Currently, the glos-

sary is set for the English-Italian language pair, but

the interface can be easily adjusted for other language

pairs.

Figure 1 shows the glossary interface for entering

a new entry. Figure 2 shows a few glossary entries

from a student’s glossary.

Of the given ﬁelds, four are compulsory:

• Topic 1, where the student is invited to select the

general topic under which to classify the given

terms, by selecting from a dropdown menu; in the

current experiment, only one topic was provided:

Medicine.

• Description, where the students are invited to en-

ter a short deﬁnition or description of the concept

that is linguistically realized by the given terms.

This ﬁeld plays multiple roles in the glossary ra-

tionale: 1. it is a way to draw the student’s at-

tention to the meaning of the terms, rather than

the terms themselves; 2. it can be used to retrieve

speciﬁc terms even when one does not remember

them; this ﬁeld in fact is a searchable text box;

3. from a technical point of view, the Descrip-

tion ﬁeld is the element that keeps the source and

Synonyms are frequent in technical and scientiﬁc lan-

guage, as pointed out for example by Rogers (2007).

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146

Figure 1: Glossary interface.

Figure 2: Example of student glossary.

target terms, their synonyms and all the other at-

tributes together to form a single entry.

• Term EN, where the student will enter the English

term.

• Term IT, where the student will enter the Italian

term.

These four pieces of information were thought

to represent the minimum amount of information to

make up a glossary. The four ﬁelds can be ﬁlled in in

any order, but the system will not save the entry if any

of them is empty. All the other ﬁelds are optional,

but each of them was included in the glossary inter-

face for a speciﬁc reason. Fields Topic 2 and Topic

3 are meant to help the student to classify the entries

according to a logical and functional ontology. In the

current experiment, the students were guided in the

selection of Topic 2 by means of a dropdown menu

containing the following options, inspired by the con-

tents of the module where the experimentation would

take place: Anatomy; Diseases and disorders; Food

and Nutrition; Medical branches; Institutions; Profes-

sions; Other. Topic 3, on the other hand, was an open

text box. These ﬁelds and menus can be customised

to the teachers’ needs, upon request. The ﬁelds Pro-

nunciation, Acronym and Phraseology are simple text

Learning Analytics for Interpreting

147

boxes which accept any type of characters, including

IPA (International Phonetic Alphabet) ones. The size

of the Phraseology ﬁeld in the database automatically

adjusts to the contents entered into the Phraseology

box. This allows the students to use this box freely

to enter as many collocations and colligations as they

want, but - if necessary - to also enter other pieces

of information which have no speciﬁc place in the

glossary interface, such as part-of-speech informa-

tion, links to online images and much more. Finally,

the Uses and Sources ﬁelds are dropdown menus. The

Uses menu includes three options (Technical, Popu-

lar, and Informal), and is meant to draw the student’s

attention to linguistic register; there is also the pos-

sibility to select all the three options at once. The

Sources menu invites the student to specify the pri-

mary source of information used to ﬁll in the glossary

ﬁelds and includes the following options: Wikipedia;

encyclopaedia; monolingual dictionary; bilingual dic-

tionary; glossary; scientiﬁc/academic publication; in-

stitutional website; Linguee or Reverso; patients’

websites and blogs; other. This ﬁeld may seem redun-

dant, given that an automatic tracking feature logs all

the students Web searches (Section 3.3). However, it

was considered important to make the students think

about the type of source(s) they use. Furthermore, it

may happen that a student’s searches are not tracked

because of the student working outside the proxy ser-

vice, of his refusing to be tracked, or of other tech-

nical problems. Finally, at any time the student can

delete an existing entry or term by clicking on the red

round icon in Figures 1 and 2, or he can modify an

existing entry by clicking on the pencil icon in Figure

2. Each student can create one or more glossaries, and

each glossary may include a potentially inﬁnite num-

ber of entries, i.e. of glossary items. For this reason,

search features have also been integrated into the sys-

tem. The student can ﬁlter the glossary by topic, or

retrieve a given term by searching for key terms in the

description ﬁeld or by entering the term itself. Fur-

thermore, the ‘Search all ﬁelds’ box allows students

to perform text searches in all the glossary ﬁelds, thus

making it possible, for example, to retrieve all tech-

nical terms, English terms only, or speciﬁc phrases

entered in the Phraseology ﬁeld. The entire glossary

as well as the ﬁltered results can also be downloaded

in Excel format (by clicking on the Excel icon) and

printed out, if necessary. LearnWeb glossaries can be

ﬁlled in and consulted bi-directionally (e.g. from En-

glish into Italian and viceversa). Furthermore, glos-

saries can be personal and/or collaborative and can be

shared with other students/users. In the current exper-

iment, however, the glossaries were created individu-

ally and were not shared.

3.2 Tracking Data for the Dashboard:

The Logs and the Proxy

Dumais et al. (2014) give a comprehensive overview

of behavioral log data and analysis in HCI. As they

point out, “an important characteristic of log data

is that it captures actual user behavior and not re-

called behaviors or subjective impressions of inter-

actions” (p. 350). Indirect observation methodolo-

gies like questionnaires rely on the self-evaluation of

students and may not reﬂect the actual work done

by the learners. On the other hand, behavioral ob-

servations are increasingly captured at a much larger

scale and can be collected in situ on a client machine

or on remote servers as people interact with appli-

cations, systems, and services. Many studies have

already used log data to analyse learning activities

(Mazza and Dimitrova, 2004; Zhang and Almeroth,

2010; Mazza et al., 2012). Most of these works utilize

only the inbuilt logging facilities of tools like Moo-

dle or WebCT. But many language learning tasks re-

quire students to search for information on websites

outside the tool used in the course. These external

actions cannot be logged by course management sys-

tems such as Moodle. Ceddia et al. (2007) used the

Weblog Analysis Tool (WAT) application to analyse

at activity level the log ﬁle data of learners’ interac-

tions collected within a Web-based learning environ-

ment (a courseware website) in order to gain infor-

mation about the tasks that the learners had engaged

in, and to determine the achievement of educational

objectives.

The study by P

erez-Paredes et al. (2011) goes be-

yond this and uses the log ﬁles of a proxy server

to analyze which websites students visited to ful-

ﬁl their task. But this requires a controlled envi-

ronment, like a classroom, where the course man-

ager can fully control the Internet access or has the

privilege to install logging software on the students’

computers. Other studies have used screen-capturing

software like Camtasia or Adobe Connect to record

students’ learning activities (Bortoluzzi and Marenzi,

2017). But these recordings have to be analyzed man-

ually, thus the number of subjects is limited by the

number of evaluators. Such a method can also be con-

sidered more obtrusive than server side logging.

We have developed a novel tracking framework

that allows researchers to track all pages a student

views during a learning session without requiring

changes on the student’s computer. Our system

tracks also external websites, such as Wikipedia.org

or Google.com. Furthermore, it is not limited to class-

room use, so students can access it from home and use

online resources as they normally would. This makes

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148

Figure 3: List of all the pages viewed by a user.

Figure 4: Playback view.

our system very unobtrusive compared to previous ap-

proaches. Furthermore, we track mouse movements

and keyboard input by users, to detect when they ac-

tively interact with a web page. Previous studies did

at most take into account how long a user stayed on

a website. To this end, they often used a very rough

guess, like the timespan between two subsequent web

requests. Such a simple measure cannot recognize in-

active times (for example when the user has left the

computer to get a cup of tea). We call our system Web

Analytics Proxy Service and we make it available to

other researchers under the domain www.waps.io.

3.3 Tracking Users’ Actions

Proxy servers generally create logs that contain sim-

ple information, such as the browser model, the URL

and the date when a speciﬁc website was visited by a

user. Many of the aforementioned studies are based

on such data. In order to gather more ﬁne-grained

information, we use JavaScript to record all input ac-

tions made by the users. This includes mouse move-

ment, scrolling, clicking and typing. We also record

when the browser window loses the focus. This hap-

pens when the user switches to another browser tab or

puts the whole browser window into background. For

each input action we record the time and the position

on the page.

To limit the log size and the transferred data, we

record up to 3 mouse positions per second while

the mouse is moved. The log data is accumulated

into batches and sent asynchronously to our tracking

server; this way the browsing experience is not inﬂu-

enced by the tracker. This data is used to calculate

Learning Analytics for Interpreting

149

how active a user was on a page. To this end, we treat

all subsequent actions within a sliding window of 5

seconds as one continuous action and assume that the

user was active during the whole time-span between

the ﬁrst and last action.

The interface shows all the pages viewed by the

user in chronological order (Figure 3). In this par-

ticular case, the user ﬁrst searched for “small intes-

tine” on the LearnWeb platform. Next he opened

www.linguee.com and searched for the Spanish and

Italian translations of this term. Afterwards he re-

turned to LearnWeb and added his ﬁndings to his

glossary. Finally, he opened a Wikipedia article.

For each log entry the system can show statis-

tics, including how much time the user spent on the

speciﬁc site and how long he was active (moved the

mouse, scrolled, clicked or typed). It has also the op-

tion to “playback” the user’s actions (Figure 4).

For this purpose, the system displays the page at

exactly the same window size the user viewed it; fur-

thermore, it can also visualize the mouse movements

recorded. At the top, a timeline is displayed show-

ing when the user was active during his session on

that page. Yellow indicates that the user moved the

mouse, and red that he scrolled or clicked. This time-

line is shown only to developers and researchers, not

to students.

3.4 The Dashboard

The dashboard is a graphic and interactive repre-

sentation of the students’ data, based on a selection

of log data from the glossary activities. The data

were speciﬁcally selected to monitor the following: 1.

General activity on the glossary task: the dashboard

shows general information about the students’ activi-

ties (e.g. periods of activity/inactivity; Figure 5), the

number of glossary items entered and the number and

types of sources (Figure 6); 2. Ability to organize the

glossary: the dashboard provides information about

whether, to what extent, and how the student com-

pleted the compulsory ﬁelds and the optional ﬁelds;

this includes summary and percentage data about the

number and types of ﬁelds ﬁlled in, a list of the de-

scriptions entered and their lengths (Figure 8), and the

language used in the description ﬁeld; 3. Ability to

search the Web for information: the dashboard shows

the preferred sources of reference as declared by the

student (glossary ﬁeld Source; Figures 6, 7), as well

as a list of all the web addresses that were actually

opened and tracked by the proxy.

We currently offer a teacher dashboard, and a stu-

dent dashboard. The teacher dashboard opens on a

general view of the group, with particular attention to

the activities on the glossary (Figure 5) and their pri-

mary sources of information (Figure 6). The teacher

can then zoom in on individual students, by clicking

on the student ID, and access similar views of the stu-

dent activities, as well as views of the student’s ability

to organize the glossary, and of his ability to search

the Web for information (Figure 7). The teacher can

select a given period of time, and all graphs and ta-

bles will be updated accordingly. Furthermore, she

can ﬁlter out speciﬁc students from the general group.

The student dashboard provides a range of views of

the data. It has been designed to allow individual stu-

dents to view their standing and progress with respect

to the group’s average. In the example in Figure 8,

small symbols in red, green and yellow indicate re-

spectively if the student progress is under, over or

equal to the average of the whole group. These sym-

bols are used not as forms of assessment of a student’s

learning progress, but as mere indications of the stu-

dent’s relative status as regards the task.

4 USE OF THE GLOSSARY TOOL

From 2 March 2017 to 30 May 2017, the Learn-

Web Glossary tool was used at the University of

Salento (Lecce, Italy) in an MA module training stu-

dents in consecutive interpreting without notes for the

English-Italian language pair. The module focused on

medical language. The class included 34 students;

of these, 32 were Italian native speakers; 1 was an

Italian-German bilingual, and 1 was an Erasmus stu-

dent from Poland with B2 level Italian. As regards

English, they were all B2+ or above.

The students were introduced to the use of the

Glossary tool in LearnWeb and instructed to cre-

ate personal medical glossaries. From the very be-

ginning, the students were informed that the system

would track all their actions, as well as all the web-

sites they visited; and they were asked for permission.

No one denied permission.

The ﬁrst few items of the glossary were to be taken

from a speciﬁc TED talk on medicine, suggested by

the teacher, and from other material used in class.

This was meant to provide the students with a con-

crete starting point for the identiﬁcation of possible

candidates for their glossaries. After a couple of hours

during which each student created a personal glossary

and worked on it in class, the students were invited to

continue building up their glossaries from home, as

part of their individual preparation for the ﬁnal exam.

After 20 days, the students were presented with a

short questionnaire in order to collect data about the

user-friendliness of the platform (9-point Likert scales

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150

Figure 5: Teacher dashboard - Class view of the students’ activities.

Figure 6: Teacher dashboard - Class view of the students’ source of information.

measuring 7 dimensions) and of the Glossary tool (9-

point Likert scales measuring 3 dimensions, including

the research-by-proxy tracking feature). The ques-

tionnaire also invited the students to freely indicate

negative and positive aspects of the Glossary, and to

add further comments. The questionnaire was created

using Limesurvey and was ﬁlled in online by the stu-

dents

. The results of the students’ answers are sum-

marized in Section 4.2.

The template is available at:

http://194.119.209.93:8088/limesurvey2/index.php/148734

4.1 Using the Dashboard to Provide

Personalized Feedback

Throughout the course, the teacher monitored the stu-

dents’ activities through the dashboard, and about

four weeks after the students started to build the glos-

saries she had collected enough data to be able to

give personalized feedback to the students. In par-

ticular, the dashboard showed that a few students

were systematically underusing the Phraseology sec-

tion and/or resorting primarily or almost exclusively

to dictionaries as reference resources for the glos-

sary entries. Furthermore, not all students showed the

same level of commitment to the task, measured by

balancing several indicators, including overall num-

ber of ﬁelds ﬁlled in, number of entries, and number

Learning Analytics for Interpreting

151

Figure 7: Teacher dashboard - View of a student’s ability to search the Web for information.

Figure 8: Student dashboard - Extract.

and variety of sources consulted. Personalized feed-

back was given by talking individually to the students

in need and gradually guiding them to understanding

the importance of the ﬁelds or types of resources they

were disregarding. In the weeks after personalized

feedback was given, the dashboard registered change

in the behaviours of those particular students.

4.2 Using the Student Feedback to

Further Develop the System

According to the data collected with the question-

naires, the glossary user-friendliness was rated fairly

high. On a 9-point scale, the average rate was 8 as

regards the easiness to create a new glossary, and 7.5

as regards the easiness to ﬁll in the glossary. Slightly

lower (6.5), but still positive, is the average rate of the

easiness of searching the Web without exiting from

LearnWeb. The platform in general scored on aver-

age around 7 in all the dimensions; these were: ter-

rible/wonderful; difﬁcult/easy; frustrating/satisfying;

inadequate power/adequate power; dull/stimulating;

rigid/ﬂexible.

All in all, the platform and the Glossary tool seem

to have been appreciated. The students’ feedback to

our request to list three positive features did not show

a dominant response, but rather clariﬁed that all the

existing features were appreciated, by one or another

CSEDU 2018 - 10th International Conference on Computer Supported Education

152

student, and that the system had stimulated the stu-

dents to create and expand their glossaries.

Finally, manual analysis of the students’ lists of

negative points and comments suggest integrating the

following features into future releases of the glossary:

1. Adding a ﬁeld for images.

2. Allowing the creation of glossaries on topics other

than medicine.

3. Adding a third language to the glossary, thus mak-

ing it multilingual, rather than bilingual.

4. Adding the possibility to order glossary terms al-

phabetically.

5. Adding an explicit indication of the number of en-

tries in the glossary.

Features 1, 2, and 3 had already been planned by the

authors of this paper, but could not be implemented

before the beginning of the experiment due to time

constraints. These will be considered in future re-

leases of the Glossary, along with the other points

suggested by the students.

5 CONCLUSIONS

The current paper has described the development and

integration of a system for the creation and man-

agement of bilingual glossaries into an existing e-

learning environment (LearnWeb). This system in-

cludes automatic tracking of the students’ actions in

the glossary, automatic tracking of their searches in

the Web while working on the glossary, and a dash-

board that analyses the student’s interaction with the

glossary and displays the results in graphic format for

the beneﬁt of both teachers and students. The glos-

sary and the dashboard were speciﬁcally developed to

support students in the creation of glossaries, and to

help teachers monitor the students’ approaches to this

particular task and provide individual remedial feed-

back, if necessary.

The system proved user-friendly and highly use-

ful. The Glossary interface stimulated the students to

create and expand a personal medical glossary. The

teacher dashboard helped the teacher to monitor the

student commitment to the study of vocabulary, their

understanding of the importance of content and con-

text information, and their Web search strategies; fur-

thermore, it guided the teacher in identifying students

in need and in providing personalized remedial feed-

back. The student dashboard could not be tested in

this experiment. The proxy system provided useful

information about the students’ searches on the Web.

From a research perspective, our experiment has

analysed only part of the data tracked and logged by

the system. The next steps in the research will in-

clude: analysing the time spent by students ﬁlling in

the glossary and the time spent searching the Web for

information, at different times during the experiment;

analysing the type of data the students entered in the

Phraseology ﬁeld; and outlining different proﬁles of

student behaviour by means of cluster analysis tech-

niques. These analyses should provide greater insight

into student approaches to the creation of glossaries.

Finally, further extensions of this project are being en-

visaged and they include: 1. correlating glossary data

to students’ exam results; for this purpose, however,

data from a much wider number of students should be

collected; and 2. asking professional interpreters to

use our system and comparing their data to students’

data.

ACKNOWLEDGEMENTS

The LearnWeb team.

The students in Lecce.

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