Adaptive Vocabulary Learning Environment for Late Talkers

Mariia Gavriushenko, Oleksiy Khriyenko and Iida Porokuokka

Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland

Keywords: Adaptive, Self-adaptive, Vocabulary Learning, Late Talkers, SLI, Game-based Learning.

Abstract: The main aim of this research is to provide children who have an early language delay with an adaptive way

to train their vocabulary taking into account individuality of the learner. The suggested system is a mobile

game-based learning environment which provides simple tasks where the learner chooses a picture that

corresponds to a played back sound from multiple pictures presented on the screen. Our basic assumption is

that the more similar the concepts (in our case, words) are, the harder the recognition task is. The system

chooses the pictures to be presented on the screen by calculating the distances between the concepts in

different dimensions. The distances are considered to consist of semantic, visual and auditory similarities.

Each similarity factor can be measured with different methods. According to the user’s feedback, the

weights of the factors and similarity distance are adjusted to modify the level of difficulty in further

iterations. The system is designed to attempt to retrieve knowledge about the learners by recognition of

aspects that are difficult for them. Proposed solution could be considered as a self-adaptive system, which is

trying to recognize individual model of the learner and apply it for further facilitation of his/her learning

process. The use of the system will be demonstrated in future work.

1 INTRODUCTION

The focus of our work is in the area of facilitation

learning techniques for children with an early

language delay. In the literature, these children are

often called late talkers, a term also adopted in this

paper. Late talkers are a group of children who learn

to form sentences later and have smaller

vocabularies than their more typical peers who start

putting words together before turning two (Preston

et al., 2010). This early language delay has been

connected with a risk of later difficulties in language

learning such as dyslexia (Lyytinen et al., 2001;

Lyytinen, 2015; Lyytinen et al., 2005). A very recent

finding (Lyytinen, 2015) confirmed earlier

observation (Lyytinen et al., 2005) reveals that if

such an early language delay comprises receptive

language, i.e. comprehension of spoken language

during early years, such children will face serious

difficulties in becoming fully literate. Authors

documented (Lyytinen et al., 2005) the fact which

has been more recently noted also by Nematzadeh et

al. (2011), that many late talkers catch up with their

peers, but some continue with slower learning pace

and are later considered to have a Specific Language

Impairment (SLI) (Thal et al., 1997; Desmarais et

al., 2008).

Every child is different and learns with different

paces and strategies. Information technology has

long been seen as a cost-efficient solution for

meeting the students’ individual needs in learning

(Murray and Pérez, 2015). Learning with mobile

devices (M-learning) has been recognized to

motivate the children to learn as well as to attract

their attention while solving problems (Skiada et al.,

2014). M-learning can provide a stress-free

environment combining ubiquitous learning with

individualisation so that the learner gets to proceed

in their own pace. As well as games are designed to

generate a positive effect in players and are most

successful and engaging when they facilitate the

flow experience (Kiili, 2005). Flow is considered to

be a state of mind where a person forgets his

surroundings and track of time while occupying

themselves with tasks that are neither too easy

(which would lead to boredom), nor too difficult,

(which would lead to anxiousness)

(Csikszentmihalyi, 1990). In order to facilitate a

flow experience meaning to provide sufficient

challenge, a learning game should take into account

learner’s individual needs.

The key to prevent the late talking children from

Gavriushenko, M., Khriyenko, O. and Porokuokka, I.

Adaptive Vocabulary Learning Environment for Late Talkers.

In Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016) - Volume 2, pages 321-330

ISBN: 978-989-758-179-3

321

continuing with slow learning pace is to intervene

with their vocabulary learning as early as possible.

Broadening the vocabulary creates new

opportunities to form sentences. As young children

are the target group of our study, we faced a

challenge of providing them with a simple and

motivating way to learn.

In this research, we decided to elaborate an

adaptive mobile application with gamified elements

such as rewards and animations with actions which

are attractive for children. The application is

developed for touchscreens that are 7 inches or

larger to prevent the effect of motoric skills. The

simple functionality on the surface confirms that the

system is not too complicated even for very young

children.

This paper consists of 4 sections. Section 2

discusses the related work and Section 3

concentrates on late talkers and on how they could

benefit from adaptive learning technologies. In

Section 4, we describe the proposed system’s

architecture. In Section 5, conclusions are drawn.

Section 6 presents future work and plans for

evaluation.

2 RELATED WORK

Many have recognized the importance and the

benefits of developing digital systems for young

children. It is possible to find lots of game-based

learning solutions for preschool children with

categories “Preschool games”, “Language Arts from

Phonics through Reading”, “Word Games”, “Animal

Games”, “Phonetics”, etc. Unfortunately, they are

not made keeping in mind those who have

difficulties in learning. Research and development of

ICT-supported learning for children with disabilities

has not received as much attention and it is also

difficult to access research findings in this field

(Istenic, Starcic and Bagon, 2014). However, some

contributions have been made and in the next

paragraphs we discuss few of them.

PAL system proposed by Newell, Booth and

Beattie (1991) was created for the children with poor

motor control; the key-saving aspects speeded up

text creation and it was found very useful for

children with spelling problems. Also, it was

observed that children who were on the verge of

being classified as non-readers, showed a significant

improvement in their work.

Skiada et al. (2014) suggested a mobile

application named “EasyLexia” which is built taking

into account the needs of dyslexic children as well as

the usability. EasyLexia included tasks that train

reading skills, memory, concentration and

mathematical logic providing both auditory and

visual stimuli. The results of Skiada et al. showed

that children preferred doing the exercises with

mobile application over the pen and paper version.

Concentrating on the touch screen kept the

children’s focus better, which emphasizes the use of

M-learning as a significant method in today’s

learning. A fun mobile learning application

developed for children with dyslexia certificates the

potential of mobile learning application in such

environments (Saleh and Alias, 2012). In addition,

“Dyseggia” – a game application with word

exercises for children with dyslexia suggested in

(Rello et al., 2012) showed positive results of the

technology use in today’s learning methods.

Singleton and Simmons (2001) proposed the

multisensory drill-and-practice computer program

Wordshark, which is designed to improve children's

spelling and word recognition skills. Wordshark is

presented in a game-format, and it is used to practice

words, learn new words, find out whether children

can read and spell particular words. As a reward for

good work from these tasks, Wordshark enables

earning teaching points. This utility does not have

components for assessment or diagnosis and does

not automatically adjust the task to the individual

learner, but motivates and is a useful reinforcement

resource for pupils with dyslexia and also for others

with special educational needs.

Possibly the most extensive empirical

documentation concerning the efficiency of game

based training of the reading skill is coming from the

research based on the Graphogame (see

graphogame.info). Its effects on the reading skill

among children with dyslexia and also among

typical learners with insufficient reading instruction

in developing countries have been documented in

detail in tens of studies listed in the mentioned

pages.

While M-learning, as well as game-based

learning have proven their benefits in helping

children with learning difficulties, there is still not

much done, especially in the area of adaptive

learning, for the target group of late talkers in

vocabulary learning.

3 LATE TALKERS

The child's vocabulary in preschool age is dependent

on the social conditions of education. Children who

start school with greater literacy skills and

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322

background knowledge have a persisting advantage

over those children who do not have these skills

(Snow et al., 1998).

The connection between an early language delay

and later difficulties in language learning has been

studied extensively. Preston et al. (2010) mention

that longitudinal studies (Scarborough and Dobrich,

1990; Paul et al., 1997; Stothard et al., 1998;

Rescorla, 2002, 2005, 2009) have shown that the

delay predicts later difficulties in, e.g. reading.

Longitudinal study of dyslexia in (Lyytinen et al.,

2001) recognized that late talkers with a familial risk

for dyslexia are more likely to have such problems

than typical children with the same familial risk.

3.1 How Late Talkers Learn

Many studies have noted that late talkers are using

different strategies in learning. One of these

differences is that some of these children have

difficulties in their general cognitive abilities such as

attention, categorization and memory skills

(Nematzadeh et al. 2011). Another is related to the

connectedness of their semantic network structures

which is intuitively connected with the ability of

forming sentences. Beckage et al. (2010) and

Nematzadeh et al. (2011) noted that the semantic

network structures of late talkers are less connective

compared to their age peers. Beckage et al. (2010)

retrieved the vocabularies from questionnaires filled

by the children’s parents and formed the connections

between using co-occurrence in a corpus of child

directed speech. They also noted that there was more

variance in late talkers’ network structures than the

typical talkers’. Nemantzadeh et al. (2011) studied

the matter by teaching novel words to the children,

and then connected learned words by the similarity

of their meanings – the late talkers learned fewer

words and those that were semantically rather

further than closer.

The results allow us to conclude that these

children require more personalisation during their

learning process. The personalisation should allow

them to learn with their own pace and keep their

attention. In addition, it can support them in forming

the associative connections in their vocabulary in

order to create better understanding of

categorisation.

3.2 Adaptive Learning Systems for

Children with Learning Difficulties

It is known that learning is improved when the

instructions are given to the learner in a personalised

manner (Murray and Pérez, 2015). This knowledge

and background theories of education have been a

decades lasting trend in creating technologically

enhanced learning environments that adapt, one way

or another, to the learners needs. In the literature,

these learning environments are often referred as

“adaptive” or “intelligent” tutoring or learning

systems.

According to (Gifford, 2013), adaptive learning

is a methodology that is centred on “creating a

learning experience that is unique” for every

individual learner through the intervention of

computer software. Adaptive learning systems allow

organising content, identifying the way to learn

according to learner's knowledge and use assessment

result to provide personalised feedback for each

learner (Sonwalkar, 2005).

Adaptive learning systems have a lot of features

and functions (Venable, 2011) that are combined to

provide relevant content, support and to guide the

user through the adaptive learning courses or

modules: pre-test, pacing and control, feedback and

assessment, progress tracking and reports,

motivation and reward.

These systems can be either simple or algorithm-

based (Oxman et al., 2014). Simpler adaptive

learning systems are rule-based, created using a

series of if-then statements. Algorithm-based

systems take advantage of advanced mathematical

formulas and machine learning concepts to adapt

with greater specificity to individual learners. Earlier

research by (Brusilovsky and Peylo, 2003) divides

these systems into Adaptive Hypermedia Systems

and Intelligent Tutoring Systems. By these

technologies we mean different ways to add adaptive

or intelligent functions into learning systems.

Adaptive Hypermedia Systems include adaptive

presentation and adaptive navigation support, and

also adaptive information filtering, which includes

collaborative filtering and content-based filtering.

Intelligent Tutoring Systems include curriculum

sequencing, intelligent solution analysis and

problem solving support and intelligent collaborative

learning, which includes adaptive group formation

and adaptive collaboration support.

Finding an optimal way to present the concepts

to the children during their first years of life might

benefit in diminishing their risk to develop

difficulties in language learning in the future. That is

why we should have adaptive content presentation in

learning system for late talkers. To make it available

we should use the adaptive presentation technology,

which aims to adapt the content of a hypermedia

page to the user's goals, knowledge and other

Adaptive Vocabulary Learning Environment for Late Talkers

323

information stored in the user model. In a system

with adaptive presentation, the pages are not static,

but adaptively generated or assembled from pieces

for each user (Brusilovsky, 1999). Respectively, in

our system, the content shown to the children should

not be predefined, but should have the ability to

adapt according to user’s feedback.

We assume that it is harder to recognize some

particular concept among several other similar ones.

Every child can perceive images and sounds

corresponding to these images differently. For one

child, it is difficult to distinguish between words,

which sound similar. For another child, it could be

difficult to distinguish between words that have a

similar pattern, whether they could have a similar

shape or similar colour. Another child may have

difficulty with differentiating words, which are

semantically close to each other.

With proposed solution we develop an adaptive

learning system for late talkers, which will take into

account personal qualities of learners’ in their

perception of a concept. The system will learn and

build a personal model of a learner based on his/her

answers while changing the complexity of concept

representation, and will apply this model for further

facilitation of learning process. Therefore, the

system could speed up the vocabulary learning

process individually for each user.

4 SYSTEM DESCRIPTION

As a basis for the learning system improvement, we

have chosen “Graphogame” learning tool

(Richardson and Lyytinen, 2014), which is a

learning environment that teaches children reading

by playing back a sound of a letter and asking the

learner to choose the correct letter from multiple

choices presented on a screen, adapting the given

choices according to similarities between the letters

and user’s feedback. We are going to utilise the

functionality of “Graphogame” tool replacing the

letters with images of words. We are not going to

use predefined sets of images or populate a set with

random images because such strategy lacks an

individual approach and does not take personal

specifics of a learner into account. Therefore, the

functionality of our approach should be able to take

into account differences between visual and auditory

stimuli and personalise further picture selection

based on intelligent analysis of user’s answers.

4.1 Concept Similarity

We make an assumption that concept learning and

recognition depends on individual perception of

several parameters such as sound and visual

representations as well as their semantics. Our

approach is based on manipulation with complexity

level of concept recognition caused by these factors.

Taking into account users’ feedbacks (correctness of

answers), system will automatically increase or

decrease the level of complexity, adapting it to the

individual learning abilities of the users, and in such

a way will facilitate learning process.

We highlighted three main factors that influence

the complexity. These factors are: visual similarity,

similarity of sound-based phonetic representations

and semantic similarity of the concepts. Assuming

that sets of more similar concepts bring more

difficulties for their recognition (distinction), we

automate the process of image selection using

multidimensional concept similarity metric, defined

as an aggregation of similarity values of mentioned

three factors. Now, we are able to personalize our

learning tool via changing the delta (Δ) of concept

similarity as well as “fiddling” with different levels

of influence of the three factors on aggregated

similarity.

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4.1.1 Visual Similarity

Measuring the distance between two images is a

central problem in image recognition and computer

vision and many definitions for the metric have been

suggested (Wang et al., 2005). Considering visual

similarity, image features such as shape of presented

object, colour distribution, brightness, contrast, etc.

are usually acknowledged. The most commonly used

image metric is Euclidean distance due to its

advantage of simplicity. Euclidean distance is

computed by summing the squares of differences

between each pixel in images (Wang et al., 2005).

However, in pattern recognition, it performs poorly

compared to e.g. Tangent distance (Simard et al.,

1993) which succeeds well in tasks such as

recognizing handwritten digits (Wang et al., 2005).

On shape matching tasks, e.g. Latecki and

Lakämper’s, (2000) approach gives intuitive results

basing the metric on correspondence between

object’s visual parts. Image Euclidean Distance

Measure (IMED) metric (Wang et al., 2005) is based

on Euclidean distance. IMED adds the spatial

relationships of pictures into consideration and

outperformed traditional Euclidean distance in face

CSEDU 2016 - 8th International Conference on Computer Supported Education

324

recognition tasks in evaluations performed by the

authors.

Besides shape, colour is a very dominant visual

feature. According to (Deng et al., 2001), distance

between colours in two pictures can be measured by

comparing their colour histograms, which represent

the colour distribution in an image. However, colour

histograms do not consider spatial knowledge and

have high cost in retrieval and search. Authors

proposed a “dominant colour descriptor” which

consists of the representative colours in a region and

their distribution. A similarity measure for the

descriptor and an efficient colour indexing scheme

for image retrieval were also suggested. The method

performed fast and efficiently in the

experimentations. However, the descriptor did not

take into account the spatial relationships between

the colour regions and considering high level

matches, the correspondence was unstable.

Some of the visual similarity features might be

more valuable than others. For example, take a set of

images created by the same designer: they might be

drawn in the same style using the same colour

palette that makes all images quite similar in spite of

the difference between the objects they represent. In

this case, shape feature might be more valuable in

calculating the actual human perception of visual

similarity. Thus, there might be various automatic

techniques to recognize different levels of the

features relevance, but in our solution, we are going

to use manually defined coefficient for the feature

and leave possible automation of this process for

future work.

4.1.2 Phonetic Similarity

Along with visual similarity, phonetic similarity of

concepts can also affect children’s performance in

distinguishing them. There are many researches and

practical implementations done with respect to

automated voice and speech recognition (Petajan,

1990; Astradabadi, 1998; Potamianos et al., 1997)

Adaptive speech recognition technology is not yet at

the point where machines understand all speech, in

any acoustic environment, or by any person, but it is

used on a day‐to‐day basis in a number of

applications and services (Docsoft Inc., 2009). The

vast difference in anatomy and physiology between

the speech production and perception systems of

humans makes it difficult to analyse (Kessler, 2005).

Unfortunately, these systems are expensive and they

cannot always correctly recognize the input from a

person who speaks with a dialect, accent, and also

they have some problems with recognizing words

from people who are combining words from

different languages by force of habit.

Because the complexity of voice recognition

especially in case of sound samples created by

different persons is considered high, we decided to

calculate phonetic similarity of the concepts as string

based similarity of their phonetic transcriptions. In a

non-orthographic language, the phonetic

representations of the words are more reliable than

the written format of words. However, they do not

take into account e.g. different dialects that would

sound more native to the learner. In spite of these

disadvantages, it was decided that the standardised

language provides enough information on the

phonetic distances. The auditory representation of

the words is provided in such a way that it follows

the patterns of these phonetic representations aiming

to the most standardised way of speaking.

There are several string-based techniques that

could be applied for phonetic transcriptions

similarity matching: Edit Distance – finds how

dissimilar two strings are by counting the minimum

number of operations required to transform one

string into another; Jaro-Winkler measure (Winkler,

1999), N-gram similarity function (Kondrak, 2005),

Soundex (Russell and Odell, 1918) – phonetic

similarity measure, which principle of operation is

based on the partition of consonants in the group

with serial numbers from which then compiled the

resulting value; Daitch-Mokotoff (Mokotoff, 1997)

has much more complex conversion rules than in

Soundex – now shaping the resulting code involved

not only single characters, but also a sequence of

several characters; Metaphone – transforms the

original word with the rules of English language,

using much more complex rules, and thus lost

significantly less information as letters are not

divided into groups (Euzenat and Shvaiko, 2013). In

our solution we allow utilisation of several

measuring functions with further weighted

aggregation of the results (e.g., weighted product or

weighted sum).

4.1.3 Semantic Similarity

Semantic similarity between concepts plays an

important role in semantic sense understanding. The

measuring is not a trivial task. The most used

technique is to measure semantic similarity based on

domain ontology; a conceptual model that describes

the corresponding domain. Ontology-based semantic

similarity can be measured with different methods

(Sanchez, 2012; Bin et al., 2009):

 Edge-counting method (or graph-based)

Adaptive Vocabulary Learning Environment for Late Talkers

325

calculates the minimum path length connecting

the corresponding ontological nodes through

the ‘is-a’ -links (Sanchez et al., 2012). Equally

distant pairs of concepts which belong to the

upper level of taxonomy are counted less

similar than those which belong to a lower

level (Wu and Palmer, 1994). Very often the

shortest path length is combined with the depth

of ontology in a nonlinear function (Li et al.,

2003), sometimes with the overlapping

between the nodes (Alvarez and Lim, 2007). In

(Al-Mubaid and Nguyen, 2006), authors

applied cluster-based measure on top of a

minimum length path and taxonomical depth.

Taxonomical edge-counting method was

extended by including non-taxonomic semantic

links to the notion in the path (Hirst and St-

Onge, 1998).

 Feature-based measures use taxonomical

features extracted from ontology. The

similarity between two concepts can be

computed as a function of their common and

differential features (assessing similarity

between concepts as a function of their

properties) (Sanchez et al., 2012). Such facet-

based classification could be combined with

similarity of common properties’ values.

 Combined measures which include the edge-

counting based and information content (IC)-

based measures with edge weight (Jiang and

Conrath, 1997).

In our current solution, we have limited our

focus to a domain of animals. Each domain brings

certain specifics to semantic similarity measuring

metric. Semantic similarity could be measured

differently depending on the context. Such context

dependent similarities could be calculated separately

and be further aggregated using different weights for

different contexts. Thus, for the chosen domain, we

may highlight several classifications:

 Biological species-based classification: this

metric is based on subclass hierarchy of animals

classified by biological families of animals. In

this case, the most suitable approaches to

measure similarity are graph-based techniques

(e.g. Jaccard metric, Scaled shortest path, Depth

of the subsumer and closeness to the concepts,

etc.) (Euzenat and Shvaiko, 2013; Bouquet et al.,

2004; Leacock and Chodorow, 1998; Haase e.

al., 2004). Thus, we calculate semantic similarity

of concepts based on locations of corresponding

nodes in the graph, using taxonomy-based

ontology that represents class hierarchy of

animals.

 Geolocation-based classification: here we

distinguished animals by geographical regions

they live in. It is a complex metric that integrates

continent-based, latitude- and climate zone based

clustering. Similarity between the clusters is

calculated based on climate groups’ hierarchy

and similarity of different continents.

 Domestication-based classification: animals

could be also divided to those who are fully

domesticated by human and live at their homes

and farms, those whom we may meet in a zoo,

and those who live only in wild nature and most

probably are only seen via video records and

photos. In this case, distances between the

classes could be predefined. Also, other metrics

that define semantic similarity in other contexts

may exist. Therefore, analogically to other

similarity factors, final semantic similarity

measure could be aggregated by weighted

products/sum.

4.1.4 Concept Similarity Measure

Since all of our concept similarity factors (visual,

phonetic and semantic) could be represented as

weighted functions of various similarity measuring

techniques, we may define a general formula to

calculate similarity between the concepts (Figure 1).

4.2 System Architecture and

Adaptation Logic

The system is presented as game-based learning

environment for mobile phones and tablets. The

system’s aim is to teach recognition of vocabulary

items from multiple choices of their visual and

auditory representations. The task of the learner

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CSEDU 2016 - 8th International Conference on Computer Supported Education

326

Figure 2: General architecture of solution.

is to recognize the word that he or she hears from a

group of pictures presented on the screen. All

pictures except for the one presenting the correct

answer are further referred as distraction items.

General architecture of proposed facilitation solution

is shown in Figure 2.

Based on calculated similarity measures between

all the concepts and the chosen one (“Random

Concept” in the figure), we rank them and select a

group of the most closest to the defined delta (Δ)

value. Initial value could be, for instance, considered

as an average of similarity values of all the measured

concepts. Depending on the amount of distraction

items 



(since their amount also influences the

overall complexity), system chooses the concepts

with similarity value closest to the value of Δ.

Depending on the user’s feedback (answer), the

value of Δ will be changed in the feedback analysis

module. If a user makes a mistake and provides

wrong answers, the Δ value will be increased

(moved to the side of the concepts with lower

similarity). Otherwise, complexity could be

increased (by decreasing value, a group of more

similar concepts will be selected next time).

At the same time, manipulating with coefficients

of visual, phonetic and semantic factors influence

(the vector of weights, 





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

,



,

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) we are

able to recognize levels of difficulties that an

individual factor brings for a particular learner. Once

user provides a wrong answer, next time, when the

same concept will be chosen, the algorithm will

change vector of weights giving more preference to

one of the factors. Therefore, system will collect

statistics on personal learning model of the user,

while trying to already personalize complexity of the

tasks. Whole collected statistics including value of

Δ, vector of weights





, values of similarities

between chosen and other distraction concepts, etc.

is stored in the Log module of the system and is

further used as labelled learning sample to recognize

individual user’s features of concept perception.

Furthermore, this will allow the system to

personalize the strategy for individual learning

process and to develop ability of the learner to

overcome individual difficulties in vocabulary

learning.

For the current research we used breadth-first

search to calculate the semantic similarity from the

graph of used concepts, Levenhstein distance for

phonetic similarity and Euclidean distance for the

visual similarity. In future work, we will use other

algorithms, as well as combine and compare them to

conclude which are the most relevant.

5 CONCLUSIONS

In this work, we concentrated on developing an

adaptive vocabulary learning system for late talkers

who have difficulties in learning language due to

individual reasons. The system is a mobile learning

application which aims to provide an optimal way of

vocabulary presentation for young children.

The functionality behind the system is based on

different similarity factors (visual, phonetic and

semantic) of the learning objectives, words, in our

case. We assumed that the more similar the words

are, the more difficult it is to distinguish them. Thus,

as the learners improve, the system is able to provide

them with more challenging tasks. In addition to

using the information of learners’ mistakes in the

system’s adaptation, the data is collected and further

analysed from angles of semantic network

connectivity, emphasis of the dimensions and overall

Conce

Concept

Visual

representation

Sound &

Phonetical

transcription

Random

Concept













Initial value

()

Function

Selected Set

of Concepts













Initial weights of factors

Feedback

Analyser

(Modifies values of

similarity factors’

Answer

New vector of

factors’ weights

and new

∆ value

∆

Adaptive Vocabulary Learning Environment for Late Talkers

327

system’s educational effectivity.

Existing approaches to study the characteristics

of semantic networks are dependent on either

parent’s knowledge of their children’s vocabularies

(cf. Beckage et al, 2010). Our approach has the

advantage of not only impartiality but automaticity,

which makes it possible to collect larger amount of

data with less effort and bias. However, some

disadvantages in the methodology exist. Firstly, the

system does not require the children to form the

words, only to recognize them, and therefore it

cannot be considered as a way of training word

composition. Secondly, it is likely that all the

children’s answers are not equally valuable since

multiple-choices questions can be answered

randomly and also wrong answers could be given

intentionally.

6 FUTURE WORK

To evaluate the system’s efficiency, it would be

tested with a group of children consisting of both 1-

3-year-old late talkers and other children with more

typical language proficiency. The testing group of

children will be split in halves. For every child a

filtration process of the concepts will be made. By

testing, the system will exclude the words that child

already knows from the provided sample of the

animal words and will only work with those words

child does not know. For the first group of children,

the minimum delta (lowest difficulty), which is not

changed during the learning and testing process,

would be used. And for the second group, the delta

will be changing for every learner personally

according to the adaptation logic of the system.

After some period of learning and testing there will

be a final test, which would check the amount of

words the children learned. Knowing the percentage

of the learned concepts of each group, we can make

a conclusion if the proposed adaptive system is

effective. After the evaluation we will use the

gathered data for further development of the

algorithm. As the current system relies on metrics

made by adults, it is possible that they do not reflect

the children’s associations. The gathered association

networks can be further used as a base for the

algorithm instead of the adult-made ontologies. The

evaluation results could also show if recognizable

patterns exist in the mistakes the children make.

Also, we will extend the knowledge base,

including also other domains of vocabulary. The

data like user’s answers could be automatically sent

to a database on a cloud server. The algorithm could

be extended to consider several users’ feedback,

which could be used to form more reliable user

models. Applied to a larger set and multiple

vocabulary domains, analysis of the children’s

answers in the system can result in valuable

information of children’s semantic networks.

The system could also be modified to be more

intuitive and motivating. For example, it could be

extended to provide interactive storybook-type of

tasks, where a child could listen to a story illustrated

on the screen and then answers to multiple-choice

questions about the context of the story.

ACKNOWLEDGEMENTS

We sincerely thank Professor Heikki Lyytinen for

the inspiration, guidance and encouragement in

finishing this part of work, as well as helping in

future development.

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