ABOUT THE BENEFITS OF EXPLOITING NATURAL

LANGUAGE PROCESSING TECHNIQUES FOR E-LEARNING

Diana Pérez-Marín, Ismael Pascual-Nieto and Pilar Rodríguez

Computer Science Department, Universidad Autonoma de Madrid

Francisco Tomas y Valiente, 11, 28049, Madrid, Spain

Keywords: Natural Language Processing, Adaptive Hypermedia, User Modeling, Open Learner Modeling, Conceptual

Modeling, e-assessment, e-learning.

Abstract: Natural Language Processing (NLP) is a research field that studies how to automatically interpret/generate

information in natural language. Currently, the quality and number of developed NLP resources and

techniques permit their application to educational systems, with the potential of widening access to training

and opening new ways of teaching. In this paper, the benefits of exploiting the current NLP techniques to

improve e-learning systems will be discussed. A brief overview of the state-of-the-art of NLP will be

provided and, some real e-learning and e-assessment applications based on the use of NLP techniques will

be described to illustrate the benefits of using NLP techniques for e-learning.

1 INTRODUCTION

In the past there were only three possible ways to

acquire new information: by attending to lessons in

the school, high-school or university; by

extracurricular activities; or, by self-teaching with

books or manuals.

Since the 80s, with the Multimedia Age this fact

started to change and the creation of CD-ROMs with

static information became a new approach to

learning. These courses on CD-ROM were simply

the textbooks typed into the computer and there

were problems because once the data was written on

the CD-ROM it could not be altered and therefore

updating the course meant delivering new CD-

ROMs with the high cost it implied.

Experts became aware of the possibilities

Internet had as a teaching tool. On-line education

was invented and e-learning started to be used by

more and more teachers and students.

E-learning systems allow students to control

their own learning rhythm. They can connect from

anywhere at anytime. However, the lack of a tutor

can become a problem if the student gets stuck in a

lesson.

In order to approach this and to provide the

benefits of one-on-one instruction (pay attention to

each student’s learning needs, assess and diagnose

problems and provide assistance as needed),

Intelligent Tutoring Systems (ITS) were created.

ITSs are computer-based teaching or training

systems which reduce cost by automating course

work selection, presentation, and student evaluation.

One key factor of any ITS is to keep a student model

with information from the student to tailor strategies

and provide explanations, hints, examples, and

practice as needed.

Inspired by ITSs, in the early 1990s, Adaptive

Hypermedia Systems (AHS) were born. They

combine hypermedia-based systems with adaptive

and user-model-based interfaces (Eklund and

Sinclair, 2000).

They can be used in any situation in which

several users with different learning styles and

backgrounds have to access common information.

The adaptation should be not only static (based on

stereotypes) but also dynamic (based on the

student’s behavior). It has proven to be effective

since learners using such systems have demonstrated

faster learning, more goal-oriented attitude and take

fewer steps to complete a course (Conlan, 2003).

There are several educational AHSs (AEHSs)

that are currently being used both in academic and

commercial environments (Brusilovsky, 2004).

However, all of them rely on objective testing items

to assess the students’ knowledge.

472

Pérez-Marín D., Pascual-Nieto I. and Rodríguez P. (2008).

ABOUT THE BENEFITS OF EXPLOITING NATURAL LANGUAGE PROCESSING TECHNIQUES FOR E-LEARNING.

In Proceedings of the Fourth International Conference on Web Information Systems and Technologies, pages 472-475

DOI: 10.5220/0001532304720475

 SciTePress

On the other hand, Natural Language Processing

(NLP) has studied since the 50s how to

automatically analyze, extract information and

generate natural language. Although the advances in

the field have not been as spectacular as they were

expected at the beginning, the current number of

NLP techniques and resources permit their

application to e-learning systems. For instance,

AEHSs could take advantage of Information

Extraction techniques to improve the student model

and thus, the adaptation to his or her particular

features; or, the evaluation section of e-learning

courses could incorporate open-ended questions by

using free-text Computer Assisted Assessment

techniques.

In this paper, the benefits of exploiting the

current NLP techniques and resources to improve e-

learning systems will be discussed. In order to do

that, the paper is organized in four sections: Section

2 gives a brief overview of the state-of-the-art of

NLP: Section 3 describes some real e-learning

applications that rely on some kind of NLP

technique or resource; and, finally Section 4 ends

with the conclusions.

2 OVERVIEW OF THE

STATE-OF-THE-ART OF NLP

Linguistics is the field that studies language.

Computational linguistic researches linguistics

phenomena that occur in digital data. Natural

Language Processing (NLP) is a subfield of

Computational Linguistics that focuses on how to

build automatic systems able to interpret/generate

information in natural language (Volk, 2004).

A brief historical review would start in the

fifties. It is important to highlight Chomsky as the

provider of the theoretical basis for the following

systems. In general, it was a time of big expectations

in which several classes of grammar were

characterized, the Probability theory of Shannon was

formalized and the first NLP application (Machine

Translation) was explored. However, the systems

that were created were very simple. They just

translated word by word accessing dictionaries. The

task became more complex than originally thought.

This culminated with the ALPAC (Automatic

Language Processing Advisory Committee) report at

the end of the sixties that stated that the automatic

translation of scientific texts did not exist and that it

was not expected soon. This implied reducing the

funding in NLP not only in USA but also in other

countries.

The following decades were characterized by the

realism. In fact, two important ways of processing

were treated: symbolic and statistic. The former

relies on methods of qualitative analysis such as

hand-crafted rules and the latter uses the distribution

of quantitative text features to draw conclusions.

To build the rules of analysis is complex and

takes time. On the other hand, in order to achieve a

good performance, the statistical approach needs of

a big amount of annotated texts (corpora). Some

researchers advocate the possibility of having hybrid

systems relying on a combination of statistical and

symbolic techniques (Hermet and Szpakowicz,

2006).

A typical NLP system is represented in Figure 1.

It can be seen how in order to process natural

language, several linguistics levels should be

covered:

– Phonological: Sounds processing to detect

expression units in speech.

– Morphological: Extracting information about the

words such as their gender, number and part-of-

speech. Besides, considering suffixes, prefixes

and other derivational, inflectional and

compositional issues.

– Syntactical: Using parsers to detect valid

structures in the sentences. These structures are

usually represented with graphs or trees. It

serves as basis for semantic interpretation.

– Semantic: Finding the most suitable knowledge

formalism to represent the meaning of the text.

– Pragmatic: Interpreting the meaning of the

sentence in a context to react accordingly.

Figure 1: Typical NLP pipeline.

ABOUT THE BENEFITS OF EXPLOITING NATURAL LANGUAGE PROCESSING TECHNIQUES FOR

E-LEARNING

473

Many resources have been developed in order to be

able to cover as many as possible levels. Some of

them are electronic dictionaries, thesauri, ontologies

(one of the most relevant is WordNet for English

and EuroWordNet for European languages),

morphology and spelling rules, grammar rules,

semantic interpretation rules and discourse

interpretation templates.

Some NLP tools are tokenizers to break the text

in tokens (a unit of processing), part-of-speech

taggers, chunkers, sentence splitters, parsers and

logic translators. They can be applied to solve

different problems such as:

– Machine Translation, e.g. Babelfish (Alta Vista).

– Question Answering, e.g. Ask Jeeves (Ask).

– Language Summarization, e.g. MEAD

(U.Michigan) .

– Automatic Essay evaluation, e.g. E-Rater (ETS).

The main challenges are now in the semantics and

pragmatics levels. For them it is still necessary more

research on general versus domain-specific

resources and algorithms, the interplay between

prosody, syntax, and semantics, new means of

communication and new types of discourse.

3 APPLICATIONS

In this section, some applications will be described

to illustrate the potential of using NLP techniques

for e-learning systems. The goal is to provide a

representative list, highlighting several advantages

of using NLP techniques for different applications:

- CarmelTC (Rosé et al. 2003): Carmel is a Virtual

Learning Environment system that has been

incorporated the free-text assessment module called

CarmelTC. It is able to give a score to the student

and, to find out which set of correct features are

present in student essays by using the Carmel’s

linguistic analysis of the text. The advantage of

using NLP techniques is to allow the assessment of

higher cognitive skills than just limiting the

evaluation to objective testing such as Multiple

Choice Questions or fill-in-the-blank exercises.

- Welkin (Alfonseca, 2003): It is a web-based

application based on the wraetlic NLP toolkit to

automatically select the contents of a text according

to a user profile. The advantage of using NLP in

Welkin is to allow the user to focus just on the

snippets of texts that are interesting for him or her.

For instance, if the profile of the user indicates that

s/he is only interested in historical events,

information about non-historical events will not be

shown to him or her.

- E-tester (Guetl et al., 2005): It is a computer-based

system that identifies the main concepts in a text and

generates questions from these concepts such as

“What is xxx?” or “Explain yyy”. Next, it waits for

the students’ answers in free-text to compare them

with the e-learning content that the system has and

treats as model answer. The comparison is based on

the free-text scoring system Markit (Williams and

Dreher, 2005). The advantage of using NLP is to

avoid the necessity of asking the teacher to introduce

the questions as they are generated by the system.

Besides, the students are able to see a histogram of

frequencies indicating how well they have used each

concept in comparison to the number of times the

concept appears in the model answers.

- AutoTutor (Graesser et al. 2007): It is a web-based

intelligent tutoring system able to engage the student

in a dialogue by using NLP techniques. It appears as

an animated agent that acts as a dialog partner with

the student. The advantage is that the student is more

motivated to continue studying the subject and find

the task not only interesting but amusing as s/he

feels that s/he is talking to a person.

- Computing similarity between users (Lops et al.

2007): It is a system that clusters users’ profiles

using WordNet. It has the advantage of making the

comparison and clustering of good and bad aspects

of the users’ profiles easier.

- Knowledge Tracing (Corbett et al. 2007):

Traditionally, Intelligent Tutoring Systems have

incorporated student explanations of problem

solutions in menus. The advantage of using

knowledge tracing of typed student explanations is

that the model created is able to predict better the

student performance.

- Question answering (Heiner, 2007): It is a system

that is being developed within a Ph.D. thesis to

allow students to ask questions to the e-learning

system. The advantage of using NLP is that the

system is able to classify the questions and try to

give an adequate answer.

- TAGARELA (Amaral and Meurers, 2007): It is a

Computer Assisted Language Learning (CALL)

system, which is being developed within the project

with the same name, and the advantage is to be able

to provide individualized language instruction.

- Will Tools (Pérez-Marín, 2007): It is a set of web-

based applications that are able to automatically

generate a student’s conceptual model from his or

her answers in free-text. The Will Tools consist of:

Willed, the authoring tool to create the questions to

WEBIST 2008 - International Conference on Web Information Systems and Technologies

474

ask the students (each question has a statement,

maximum score and several correct answers written

by the teachers); Willow, the system to ask the

questions introduced in Willed to the students (the

core idea is to compare the student’s answer to the

teachers’ answers and the more similar they are, the

bigger the score provided to the student); Willov, the

conceptual model viewer (a conceptual model can be

defined as a network of concepts that can be visually

displayed as a concept map, conceptual diagram,

table, graph or textual summary); and, Willoc, the

configuration tool. The advantage of using NLP in

the Will Tools is not only to automatically score

free-text students’ answers, but also to permit the

estimation of a confidence-value for each concept

used by the student in his or her answer and thus, to

provide feedback to students and teachers about how

well each concept is known.

4 CONCLUSIONS

In this paper, it has been claimed that e-learning

systems should take advantage of the currently

available NLP techniques and resources. In

particular, there have been reviewed several

educational applications in which NLP techniques

have been applied and, in each of them, the

advantage of using NLP to improve its functionality

has been highlighted. More applications of NLP to

e-learning can still be explored to open new ways to

distance education.

ACKNOWLEDGEMENTS

This work has been sponsored by Spanish Ministry

of Science and Technology, project TIN2007-64718.

REFERENCES

Alfonseca, E. 2003. An Approach for Automatic

Generation of on-line Information Systems based on

the Integration of Natural Language Processing and

Adaptive Hypermedia techniques, PhD thesis,

Computer Science Department, Universidad

Autónoma de Madrid, Spain.

Altavista, http://babelfish.altavista.com/.

Amaral, L. and Meurers, D. 2007. Designing Learner

Models for Intelligent Language Tutors. CALICO

2007 May 22-26, Texas, U.S.A.

Ask, http://www.ask.com/.

Brusilovsky, P. 2004. Adaptive educational hypermedia:

From generation to generation, in Proceedings of the

4th Hellenic Conference on Information and

Communication Technologies in Education.

Conlan, O. 2003. State of the art: Adaptive hypermedia,

Technical report, Waterford Institute of Technology.

Corbett, A., Wagner, A., Lesgold, S., Ulrich, H. and

Stevens, S. 2007. Modeling Students’ Natural

Language Explanations, in Proceedings of the User

Modeling international conference, Greece.

Eklund, J. and Sinclair, K. 2000. An empirical appraisal of

adaptive interfaces for instructional systems,

Educational Technology and Society Journal, 3.

ETS, http://www.ets.org/

Graesser, A.C., Jackson, G.T., and McDaniel, B. 2007.

AutoTutor holds conversations with learners that are

responsive to their cognitive and emotional states,

Educational Technology, 47, 19-22.

Graham, C. 2006. The Handbook of Blended Learning:

Global Perspectives, Local Designs, Pfeiffer, chapter

Blended Learning Systems.

Guetl, C., Dreher, H. and Williams, R. 2005. E-tester: A

computer-based tool for auto-generated question and

answer assessment, E-Learn journal.

Heiner, C. 2007. Towards A Virtual Teaching Assistant to

Answer Questions Asked by Students in Introductory

Computer Science, in Proceedings of the Artificial

Intelligence for Education international conference.

Hermet, M. and Szpakowicz, S. 2006. Symbolic

assessment of free text answers in a second-language

tutoring system, in Proceedings of the 10th Computer

Assisted Assessment conference, Loughborough,

United Kingdom.

Lops, P., Degemmis, M. and Semeraro, G. 2007.

Improving social filtering techniques through

WordNet-based user profiles, in Proceedings of the

User Modeling international conference, Greece.

Pérez-Marín, D. 2007. Adaptive Computer Assisted

Assessment of free-text students’ answers: an

approach to automatically generate students’

conceptual models, PhD thesis, Computer Science

Department, Universidad Autónoma de Madrid, Spain.

Rosé, C.P., Roque, A., Bhembe, D. and VanLehn, K.

2003. A hybrid text classification approach for

analysis of student essays. In HLT-NAACL Workshop

on Building Educational Applications Using Natural

Language Processing, pages 68–75.

Shute, V. and Torreano, L. A. 2002. Formative evaluation

of an automated knowledge elicitation and

organization tool, Authoring Tools for Advanced

Technology Learning Environments: Toward Cost-

Effective Adaptive, Interactive, and Intelligent

Educational Software .

U. Michigan, http://www.summarization.com/mead/

Volk, M. 2004. Introduction to natural language

processing. Course CMSC 723 / LING 645 in the

Stockholm University, Sweden.

Williams, R. and Dreher, H. 2005. Formative assessment

visual feedback in computer graded essays, in

Proceedings of the Issues in Informing Science and

Information Technology (INSITE), Arizona, U.S.A.

ABOUT THE BENEFITS OF EXPLOITING NATURAL LANGUAGE PROCESSING TECHNIQUES FOR

E-LEARNING

475