CALL for Open Experiments
Roman Efimov, Maxim Mozgovoy
*
and John Brine
*
The University of Aizu, Tsuruga, Ikki-machi, Aizuwakamatsu, Fukushima, Japan
Keywords: Computer-assisted Language Learning, Intelligent Systems, Virtual Labs.
Abstract: In this paper, we briefly describe the limitations of present CALL systems, caused both by technological
factors and by the limited agenda of CALL developers, whose design goals tend not to result in software
tools for practical everyday language learning activities. We also note the lack of creative new ways of
using computers in language education and a gradual shift towards traditional teaching and learning
practices, enhanced with common computer technologies such as multimedia content delivery systems and
social media. However, computers can provide more options for interactive learning, as shown by the
emergence of virtual labs or virtual sandboxes that support and encourage open experimentation. Such
systems are well known in natural sciences, but still have had little impact on the world of CALL software.
We believe that the same “free experimentation” approach used in natural sciences can be applied in CALL,
and should have a positive impact on the quality of learning, being consistent with constructivist
perspectives on language education. In the present paper, we briefly introduce our work-in-progress to
develop a system that supports open experiments with words and phrases.
1 INTRODUCTION
When computers became commodities, terms like
“computer-assisted X” lost some significant part of
their initial meaning. We do not refer to “ballpoint
pen-assisted writing” or “car-assisted traveling”, and
yet “computer-assisted language learning,” or
CALL, is still in common use. In regard to CALL,
we should probably imagine dedicated educational
systems that somehow “assist” learning in a
nontrivial technologically-driven way, but ironically
common definitions of CALL simply refer to the use
of computers in language learning activities (Levy,
1997). In particular, using an electronic dictionary or
watching a foreign-language clip on YouTube are
perfect examples of “computer-assisted language
learning”, though neither an electronic dictionary
nor a video-sharing website were explicitly designed
to support language learning.
Furthermore, it also seems to us that such
general-purpose software is the most widely used
and most helpful for the learners. By contrast, there
are hundreds if not thousands of available dedicated
software packages for language acquisition, but
strikingly they are rarely mentioned in numerous
“language learning tips” found online (Leick, 2013;
Hessian, 2012).
In general, computer technology holds a firm
position as a helper within traditional teaching and
learning practices. We learn language by listening,
speaking, reading, writing, and doing (established)
exercises, and computers provide unprecedented
support and convenience in these activities.
However, overall they still fail to provide
fundamentally new teaching and learning practices,
unavailable in traditional paper-and-pencil scenarios.
Even dedicated CALL systems (such as the ones
developed by companies like Eurotalk, Berlitz or
Rosetta Stone) are typically designed as integrated
packages of traditional learning materials —
audio/video clips, pictures, texts, exercises, and
vocabularies. In other words, current CALL systems
can be considered primarily as highly usable and
modernized versions of traditional “book + tape”
self-learning courses. The survey conducted by
Hubbard in 2002 revealed that even the CALL
experts are not convinced about the effectiveness of
educational software. Hubbard notes: “…it is
interesting that questions of effectiveness still tend to
dominate. In fact, the basic questions of "Is CALL
effective?" and "Is it more effective than
alternatives?" remain popular even among those
*
Supported by JSPS KAKENHI Grant #25330410
404
Efimov R., Mozgovoy M. and Brine J..
CALL for Open Experiments.
DOI: 10.5220/0004936704040408
In Proceedings of the 6th International Conference on Computer Supported Education (CSEDU-2014), pages 404-408
ISBN: 978-989-758-020-8
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
who have been centrally involved in the field for an
extended period of time.” (Hubbard, 2002).
We suggest that the reasons are both
technological and psychological: many computer
technologies relevant to language learning are
indeed not mature enough to be used in practical
CALL systems, and our traditional learning habits
make it hard to design fundamentally new systems
that would utilize the full power of today’s
computing hardware.
2 CALL MEETS
TECHNOLOGICAL LIMITS
A number of language learning software
insturuments can do more than merely support
traditional learning activities, but their overall
capabilities are still limited (Hubbard, 2009).
We can add that research efforts in this area are
limited, too. For example, Volodina et al. observe
that only three natural language processing-backed
CALL systems have come into everyday classroom
use (Volodina et al., 2012). Furthermore, as noted in
(Amaral et al., 2011), “the development of systems
using NLP technology is not on the agenda of most
CALL experts, and interdisciplinary research
projects integrating computational linguists and
foreign language teachers remain very rare”.
Possibly, the only “intelligent” technology that
has made its way into some retail CALL systems is
automated speech analysis, which is used to evaluate
the quality of student pronunciation. Such an
instrument is implemented, e.g., in commercial
Rosetta Stone software, but its resulting quality is
sometimes criticized (Santos, 2011).
We have to state that future development of
ICALL systems crucially depends on significant
achievements in the underlying technologies.
Language learning is a sensitive area, where
misleading computer-generated feedback may harm
students. So it is impossible to expect any rise of
intelligent CALL systems before the related natural
language processing technologies improve vastly.
3 THE PROBLEM OF LIMITED
AGENDA
However, computers can significantly improve
learner experience even without advanced AI
technologies, and provide “killer features” that are
inherently computer-backed and cannot be easily
reproduced in traditional environments. A good
example of such an “inherently computer” system is
any electronic dictionary, as it can implement a
number of unique capabilities that create new use
cases:
approximate word search;
partial search (find a word fragment);
full-text search (find example phrases);
arbitrary word form search;
handwritten characters input.
Surprisingly, most popular dictionaries
implement only a fraction of this list. It should be
noted that none of the mentioned functions require
the use of any immature research-stage technologies,
and can be implemented with established methods.
Another example is spaced repetition-based
flashcards software such as Anki (Elmes, 2013) or
SuperMemo (Wozniak, 2013). While in spaced
repetition can be exercised without a computer, it is
a laborious process, hardly tolerable for most
learners. So despite being relatively simple, these
tools are efficient learning aids (as spaced repetition
practices are proven to be effective (Caple, 1996)),
and yet seldom mentioned in CALL-related papers.
So, it seems that CALL experts have not paid
much attention to the development of everyday
language learning tools. This situation is
unfortunate, as it is inconsistent with the current
trend of seamless integration of technologies into
existing learning activities and with declarations of a
preference for a student-centered approach that
should presumably allow learners to follow their
preferred learning styles or at least to ensure higher
flexibility of the learning process.
4 VIRTUAL SANDBOXES
Such a technology-backed, student-centered
approach is already implemented in a number of
educational systems for the disciplines such as
physics, chemistry, and computer science. Notably,
there are sandbox-like environments (or “virtual
labs”) that do not restrict their users and do support
open experimentation.
For example, Open Source Physics project
(Christian et al., 2013) collects together a vast
amount of interactive physical simulations with
user-adjustable parameters. The 2D physics sandbox
Algodoo is positioned by its authors as “the perfect
tool for learning, exploring, experimenting and
laborating [sic] with real physics” (Algoryx, 2013).
The ChemCollective collection (Yaron et al., 2013)
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405
includes a number of ready setups for chemical
experiments as well as a virtual lab for open
exploration. The JFLAP environment (Rodger,
2013) allows students to create, analyze and test
finite-state machines — the devices that constitute
the basis of computer science.
We consider such systems as great examples of
well-grounded uses of computer technology in
education. Virtual labs provide safe and controlled
environments in which students can test their ideas,
and in this sense they can be likened to flight
simulation software, used to train pilots: the students
perform predefined training routines, but also can
experience the outcome of any arbitrary maneuver.
Furthermore, virtual labs contribute to the modeling
of the problem domain in the learner’s mind, and
thus are consistent with constructivist views on
educational process.
It is interesting to note that from the
technological point of view, virtual labs are not
necessarily complex systems. The possibility of
open experimentation outweighs many technical
limitations and constraints.
Unfortunately, environments for open
experiments are barely provided by the existing
CALL systems. This perhaps can be attributed to the
unclarity of the notion of an “experiment” in
language learning. It is evident, however, that a large
portion of active language learning is related to the
process of combining words and phrases into
meaningful sentences, and the analysis of the
subsequent feedback. We learn a language both by
comprehending other people’s speech and writing,
and by creating our own phrases that are to be tested
for admissibility by our interlocutors.
Within such a concept of experiments, even a
feature-rich electronic dictionary can be a powerful
experimental tool in the hands of an avid learner.
Indeed, with full-text search it is possible to check
actual word use, test the correctness of certain word
combinations, the compatibility of certain prefixes
with certain stems, etc.
The ways in which students could do
“experiments with the language” are still to be
identified. Here we can only quickly introduce our
own work-in-progress system that is intended to help
language learners master basic grammatical rules.
5 TOWARDS WORDBRICKS
One of the most basic aims of language learning is
to train the ability to formulate grammatically
correct sentences with known words. Unfortunately,
traditional exercises lack active feedback
mechanisms: learners are unable to “play” with
language constructions to find out which word
combinations are admissible and which are not. The
best (and maybe the only) way to train active writing
skills is to write (essays, letters…), and to get the
writings checked by the instructor. Some intelligent
CALL systems, such as Robo-Sensei (Nagata,
2009), can assess students’ writings by using natural
language processing technologies, but the success of
these instruments is limited.
We suggest that active skills of sentence
composition can be improved by forming a
consistent model of language in the learner’s mind.
Metaphorically speaking, the difference between a
“consistent model” and a set of declarative grammar
rules in this context is the same as the difference
between a Lego construction kit and a lengthy
manual describing which Lego bricks can be
connected and in which ways. A child does not need
manuals to play Lego: the rules of brick linkage can
be easily inferred from brick shapes and with some
trial-and-error process. Unfortunately, there is no
such way to easily check whether it is correct to
combine certain words in a sentence.
The idea of modeling syntactic rules with shaped
bricks was implemented in the educational
programming environment Scratch (Resnick et al.,
2009). In Scratch, individual syntactic elements of a
computer program are represented with shaped
bricks that have to be combined to constitute a
program (Figure 1a). While Scratch code may have
logical errors, syntactically it is always correct, since
it is impossible to combine mismatching bricks.
Scratch’s graphical editor is not just a simpler
way to write computer programs, helpful for the
beginners. It can be treated as a construal (Gooding,
1990) that forms a model of a programming
language in the learner’s mind, though this aspect is
not explicitly emphasized in Scratch.
In our research, we are working towards
implementation of a similar scheme for natural
language sentences. Undoubtedly, natural language
grammar is much more complex and less formal
than the syntax of any programming language.
However, for the purposes of novice language
learners, it is reasonable to teach restricted grammar
(as it happens in traditional language teaching),
which is technologically feasible.
Even in the case of Scratch, the design of brick
linkage principles is not trivial. One important
problem is to make sure that the links between the
bricks reflect actual structure of the corresponding
computer program. For example, a loop control
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Figure 1: a) A fragment of Scratch program; b) Dependency tree of the phrase “I like my funny dog.”;
c) Dependency tree of the same phrase in the form of 2D puzzle.
structure can be represented with the separate
“Begin Loop” and “End Loop” bricks that surround
bricks that constitute the loop body; however, such a
design would make a false impression that “Begin
Loop” and “End Loop” are independent program
elements. Instead, a loop in Scratch is represented
with a single C-shaped brick that embraces the loop
body.
It is much harder to identify a consistent set of
rules that control such linking principles of a natural
language-based system. However, they are actually
considered in a number of linguistic theories. In
particular, we base our rules on the principles of
dependency grammars (Nivre, 2005). Existing
guidelines, such as the Stanford Typed
Dependencies Manual (Marneffe & Manning, 2008)
describe in detail how the words in the given
sentence should be linked to form a structure
consistent with the ideology of dependency
grammars. For example, a subject and an object
should be directly connected to their head verb; an
adjective should be directly connected to its head
noun (Figure 1b).
The resulting structure of a sentence is
represented with an n-ary tree. While this structure is
linguistically correct (according to the theory of
dependency grammars), it arguably might be
difficult for learners to master it. Therefore, it is our
challenge to represent such trees as two-dimensional
brick puzzles. Furthermore, dependency grammars
do not express word order, while it has to be
reflected in the resulting brick structure (Figure 1c).
The proposed learning environment can be used
in different scenarios, but we would emphasize
again the possibility to perform open experiments.
Learners will be able to test which word
combinations are admissible and why.
We should also note that it is an open question
whether language learners (at least in the early
stages of learning) should study sentence structure.
However, we believe that some gentle exposure is
fruitful, especially for learning languages with rich
morphology, where a single change in one word may
trigger changes in several of its dependent words.
6 CONCLUSIONS
Computer technologies are widespread in modern
language education. Some directions in CALL
research, such as intelligent systems, have not yet
been as fruitful as anticipated, while other
developments, such as multimedia and networking
capabilities, have surpassed our expectations.
It seems that the present agenda of CALL research is
primarily focused on exploring recent technologies
such as ubiquitous computing or Web 2.0. However,
we see that even basic language learning tools, such
as electronic dictionaries or flashcard software,
would benefit from greater attention by CALL
developers. Ubiquitous and mobile computing
technologies stimulate learner’s independence, but
language learners still lack tools that support
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independent language exploration and make use of
computing hardware not just as a platform for the
delivery of multimedia data.
We would especially favor more developments
in open experimentation language software. This
direction has promising advancements in a variety of
scientific fields, but not yet in CALL.
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