IEEE Systems, Man and Cybernetics Magazine - January 2021 - 17

3)	 Vocabulary interventions were overlooked; this is more
problematic for opaque orthographies in which decoding depends a lot on making analogies based on the current vocabulary knowledge.
Furthermore, the currently reported models in the literature that target creating content for reading instructions
are rudimentary and not adaptive. For example, one way is
to randomly present the words to the user and then omit
the correct answers from the pool of the possible words,
and the other method is to keep correct answers in the
pool and omit the mistakes [15], [16]. Nevertheless, these
approaches are basic and do not consider the difficulty
level of the words, and, in addition, the values of all the
words in the pool are always considered the same or as
binary. Finally, the COVID-19 pandemic, which has led to
unprecedented global lockdowns, has highlighted the
value of intelligent home-based educational tools [17].
The Proposed Approach
The sections that follow describe the implemented
approach, starting with the system architecture.
The Architecture of the System
For mitigating the issues raised in the " Related Works and
Problem Statement " section, and to propose a solution for
the reading acquisition of languages with opaque orthographies, a gamified intelligent approach for the remediation
of dyslexia is proposed. It focuses on fostering the automaticity of decoding and recognition at the lexical level
(explicitly) and sublexical level (implicitly) as well as providing a basis for extensive vocabulary instruction. A
home-based approach is proposed to provide sufficient
training time required for the development of automaticity
in decoding and the extensive vocabulary instruction needed for influencing generalized vocabulary knowledge.
Gamification is proposed to increase motivation,
engagement, and adherence of the users to the training
program, which is supposed to be used extensively. Finally, an intelligent learning system based on an optimization
model is developed to make the home-based intervention
possible without the need for the physical presence of the
instructor. This model will maximize the value of each
training session while respecting the ability level of the
user. Each of these proposed aspects of the training program is explained in more detail.
Reading Tasks Designed for the Intervention
In this study, four different reading tasks were created to
address the problems raised in the previous sections.
These tasks were divided into the two modules of fluency
and vocabulary. The fluency module contains three tasks.
The first two tasks are called accelerated word decoding
(AWD) and accelerated word-sound recognition (AWSR).
These two reading tasks act as the opposite of each other.
In the first task (AWD), the word is presented whereupon
the user should decode it and read it aloud as fast as
	

possible. Speech recognition technology is used to decide
whether the pronunciation was accurate or not. Speech
recognition engines provide a parameter called the " confidence level. " Different levels allow for a looser or stricter
recognition. As choosing the right level is a matter of trial
and error, this parameter was put on the cloud to be able
to change it immediately in accordance with the feedback
from users.
However, in the second task (AWSR), the user hears the
pronunciation of a word through text to speech (TTS)
technology, and then he/she should recognize the pronounced word among four possible choices. Depending on
the level of the users, the three distractors can be either
random words for novices or orthographically similar
words for more advanced users. Despite the higher clarity
of human recorded speech, TTS is an agile and practical
tool for dealing with thousands of words.
The term accelerated is used to signify the time pressure aspect that pushes the user to decode or recognize
the words increasingly faster. Time pressure is one of the
important attributes of automaticity training [18], [19].
However, one key factor in automaticity is the consistency of the components, rules, or stimuli responses that
have to be automatized [20], [21]. In languages with
opaque orthography, the consistency between letters and
sounds does not always exist; hence, the reader should
find consistency at larger levels, and, in some cases, consistency can be found only at the whole word level or,
even worse, in the cases of heteronyms, context is
required to cue that consistency.
Another key factor in automaticity acquisition is the
contiguity of stimulus response or rules or regularities
[22]. This matters a lot in implicit learning, where the
learner should detect the rules or regularities [23]. It
means that consistent rules and regularities should be
proximal to each other in terms of time and space to be
detected by the individual. Finally, another key attribute in
automaticity acquisition is to start with blocked and
massed practice.
In the two games that were designed to target automaticity, all of the aforementioned factors were taken into
account. The words were chosen from 334 word lists of
phonograms (extracted from the book Phonics from
A to Z: A Practical Guide [24]). The words with the same
phonograms were grouped together to be practiced in
blocks. This also increases the contiguity of sublexical regularities, which in turn increases the probability of implicit
acquisition. In addition, the time pressure technique was
used to push the user to decode increasingly faster, which
can accelerate the process of automaticity acquisition.
Practicing these two tasks in the massed format are
expected to foster lexical automaticity explicitly and sublexical automaticity implicitly.
The third fluency task is called accelerated phrase reading (APR), which focuses on increasing the reading rate.
This task is based on a text-fading method developed by
Ja nu a r y 2021

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IEEE Systems, Man and Cybernetics Magazine - January 2021

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