The Effect of a Neuroeducation Program on L2 Automatization and Acquisition for Intermediate Young Adult Learners

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Yusuf This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4337380/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Almost without exception, adult second language (L2) learners fail to acquire an L2 to a comparable level of proficiency as their first language (L1). In part, traditional teaching approaches fail to address this issue or adapt to the equipotential brain. In light of this, this research attempted to explore language acquisition from a neuroeducation perspective. Accordingly, a neuroeducation program was developed which draws on concepts from neuroscience. The neuroeducation program was developed based on the LIRRA neuroeducational model which was proposed to automatize L2 and improve language acquisition for late learners. The program hinged on reiterative implicit exposure of lexical phrases in an attention- and reward- stimulating setting. This was to allow for procedural memory processing (like in L1) where L2 becomes automatized. In this regard, in a pre-post design, 30 English intermediate-level undergraduates were enrolled to the study. The program was a computerized online one encompassing 23 sessions and was implemented using a variety of engaging techniques such as games, riddles, and interactive readings. The outcomes were measured using a pre/post-test comprising three sections: a lexical-phrases production test, a reading proficiency test, and a writing test. The results revealed that the students showed a significant speed-up performance and considerable automaticity gains. Additionally, students showed a significant improvement in their acquisition of L2 lexical phrases and their reading proficiency. This research holds a promising future for the implementation of neuroeducation programs in language learning and for self-directed learning. Educational Psychology Linguistics neuroeducation second language acquisition automaticity implicit learning neuroplasticity lexical phrases 1. Introduction The field of neuroeducation has emerged as a prominent area of study within contemporary insights. Neuroeducation broadly encompasses findings in neuroscience with potential application in education (Howard-Jones et al., 2014 ). As a seminal cross-disciplinary approach, it has prompted a surge of investigation across a diverse spectrum of educational domains. Neuroeducation has been investigated in the literature from two main routes, viz. the direct route and the indirect route. On the one hand, the direct route -which is out of the scope of this paper- examines the direct relationship of the biological brain to educational outcomes; in this route, researchers investigated physical fitness, diet and sleep patterns, relaxation and meditation, and other environmental factors including pollution. On the other hand, the indirect route -which is the focus of this paper- proposes new learning techniques to fill the gap between cognitive neuroscience and education; investigations of concern to this route are on executive and cognitive functions, emotion regulation, and social cognition. Across the educational domains, the burgeoning field of neuroeducation holds promising potential to inform more effective methods towards second language acquisition (L2A). By shedding light on the brain's mechanisms for language learning such as neuroplasticity and the equipotentiality of the brain, neuroeducation offers insights for improving L2A. 1.1. Neuroplasticity and the Equipotentiality of the Brain Neuroplasticity, which is the amenability of the brain to change according to stimuli, was misconceived to cease in adulthood while it was contrarily shown to continue into adulthood (Li et al., 2014 ). Counter-plasticity views were challenged by numerous evidence demonstrating ongoing brain plasticity in adults, as new input continues to stimulate growth and formation of fiber connections (Goswami, 2008 ), suggesting that functional and structural changes, albeit with constraints, persist throughout adulthood (Ansari et al., 2017 ). A particularly noteworthy feature of adulthood plasticity is that it differs distinctively from childhood plasticity in the distinction between experience-expectant and experience-dependent plasticity. Experience-expectant plasticity -which is the childhood plasticity- involves inherent brain growth responding to environmental stimuli such as visual and auditory stimuli. Experience-expectant plasticity can be considered as universal inputs which shape the brain of infants in almost identical ways. However, experience-dependent plasticity -the adulthood plasticity- is shaped by individual experiences and varies based on unique life experiences (Goswami, 2008 ). In language acquisition, first language acquisition (L1A) exemplifies experience-expectant plasticity, responding innately to language stimuli for neurotypical children. Conversely, L2A showcases experience-dependent plasticity, with learning experiences depending heavily on the quality of input. In accordance with this, neuroplasticity is an ongoing process that can be induced via training which triggers changes in white and grey matter volume, as well as increases activation in brain regions (Ansari et al., 2017 ). Above all else, neurogenesis -the birth of new neurons- is evidenced to occur in language-related areas during adulthood, particularly in the hippocampus (Howard-Jones, 2007 ). In line with neuroplasiticity, the adult brain is advantaged with its equipotentiality. Yet, regrettably, hemispheric equipotentiality has been ignored in L2A approaches in that there is a paucity of involving the right-hemisphere in the language learning approaches which focus primarily on the left hemisphere abilities in language learning despite the role the right hemisphere contributes to language acquisition. The role of the right hemisphere has been supported by various studies positing that the right hemisphere is responsible for the processing of several other non-analytical language-related functions including the processing of lexical phrases as well as the processing of pragmatics and non-literal language like indirect speech acts, connotative meaning, and figurative language. (Albert & Obler, 1978 ; Damasio et al., 1996 ; Hillis & Caramazza, 1991 ; Paradis, 2004 ; Segalowitz, 1983 , 2014 ; Sperry et al., 1973; Warrington & Shallice, 1984 ). In line with the aforementioned, neuroeducation theories and approaches such as biomodality (Danesi, 2003 ) and the neurolinguistic approach (Netten & Germain, 2012 ), and neuroplasticity-based programs (Merzenich et al., 1996 ; Rogowsky et al., 2013 ) emerged to address L2A challenges and to take advantage of the equipotentiality of the brain and/or neuroplasticity for a more effective L2A. Yet, the investigation of neuroeducation programs in second language learning remains sparse. Despite this, the potential of its results is promising. In light of this, the Neurolinguistic Approach (NLA), for example, emphasizing implicit competence and authentic language engagement, yielded remarkable results in studies by Gal-Bailly ( 2011 ) and Ricordel ( 2012 ). Consistent with these results, other programs emphasizing gamification to promote learning and motivation also showed a promising potential for neuroeducation (Howard-Jones et al., 2014 ). Similar significant results were obtained from programs hinging on cognitive skills training (Merzenich et al., 1996 ; Rogowsky et al., 2013 ); the results of these programs demonstrated significant improvements in language skills for L2 learners and for children with learning difficulties. While these findings highlight the promising prospects of neuroeducation in L2A, further contemporary research was due to particularly address neuroeducation programs with a focus on proceduralizing and automatizing second language learning for adult learners. 1.2. Language Proceduralization and Automaticity Language proceduralization refers to the transformation of linguistic knowledge from reliance on declarative memory to dependence on procedural memory. The procedural memory is postulated to play a factorial role in language learning and underlies implicit and unconscious learning (Ullman, 2005 , 2015 ). In essence, proceduralization entails routing language through the procedural memory instead of the declarative memory. This can occur either through implicit exposure to language, where the procedural memory directly acquires knowledge, or through or by developing the declarative knowledge into a procedural one. Significantly, proceduralized knowledge offers an advantage over declarative knowledge due to its effortless processing, enhancing fluency and automaticity in language use. This is because procedural knowledge does not require a learner to assemble pieces of information into a program for a certain behavior; “instead, that ‘program’ is now available as a ready-made chunk to be called up in its entirety each time the conditions for that behavior are met” (DeKeyser, 2015 , p. 95). In language learning, proceduralization is pivotal for processing language functions in an effortless manner; hence, ultimately achieve language automaticity. Automaticity, in general, refers to a quick process which occurs without much awareness and control; through automatic process, and by sufficient mental practice, a skill is well-developed and performed in a quick as well as an accurate manner (Schneider et al., 1984 ; Shiffrin & Schneider, 1977 ). Language automaticity, in particular, which is achieved through the procedural memory, refers to the efficient use of language where language performance becomes mechanical with minimal effort and minimal errors. 1.3. The LIRRA Model One of the neuroeducational models that was proposed to proceduralize language and achieve automaticity is the LIRRA neuroeducational model (Yusuf, 2024 ). This groundbreaking approach paves the way for the practical application of neuroeducation in second language acquisition, offering promising opportunities for late learners to enhance their language acquisition. The LIRRA neuroeducational model rests primarily on five key brain-adaptive features, namely exposure to L2 lexical phrases, implicit learning of L2, reiterative exposure to L2 lexical phrases, a rewarding/motivating environment, and an attentional-stimulating setting . It is postulated that incorporating a LIRRA neuroeducational model into a neuroeducation program would facilitate L2A process and help proceduralize and automatize L2A. First, the neuroeducation program is postulated to achieve its purpose by virtue of improving the acquisition of lexical phrases. This is due to the grounded speculation that by exposing L2 learners to lexical phrases , the right hemisphere of the brain becomes involved in the learning process; this addresses the equipotentiality of the brain which allows for the proceduralization of L2 knowledge resulting in an improved proficiency and an enhanced automatic language performance. In a similar vein, implicit learning is postulated to address the procedural memory directly, thus internalizing and proceduralizing L2 knowledge as well as storing L2 knowledge in the long-term memory leading to a more-efficient language performance and automatization. In like manner, by a reiterative exposure to L2 phrases, stronger synapsis occurs and neural activation increases, in particular, in the basal ganglia -the core of the procedural memory. This allows for strengthening and proceduralizing L2 knowledge leading to an enhanced L2 performance and automatization. Additionally, a rewarding/motivating environment is key to the learning process since reward/motivation stimulates the release of dopamine and triggers neuroplasticity; hence, surmounts age-related limitations and improves the learning experience; importantly, reward is a neuroplasticity-based feature which powers automatization. Finally, an attentional-stimulating setting via the use of novel and relevant material is pivotal to inducing attentional neurotransmitters such as acetylcholine and noradrenaline, thus deriving neural change and triggering neuroplasticity which aid automatize the language as well as aid the formation of memory. This proposed neuroeducational model equipped with these brain-adaptive features is predicted to foster a brain-adaptable environment for L2A in a way that mirrors L1A, promoting efficient and accurate language development towards native-like proficiency while bypassing the obstacles and inaccuracies inherent in explicit instructional methods. 1.4. Rationale of the Research According to the best of the researcher’s knowledge, there is a dearth of studies that use a brain-based neuroeducation program to automatize second language in young adults. In particular, there are no studies, according to the best of the researcher’s knowledge, that use a neuroeducation program which encompasses the LIRRA model brain-adaptive features. Accordingly, this is the first study that investigates a neuroeducation program using the LIRRA neuroeducational model for the primary purpose of enhancing L2A through aiding automatize and proceduralize second language in young adults. In essence, this research proposes a computer- LIRRA neuroeducational model-based neuroeducation program to investigate its effect on L2A for intermediate-level young adults. 1.5. Aim and Significance The aim of this research is to investigate the effect of a LIRRA neuroeducational model-based program (LIRRA-NP) -which hinges on five core brain-adaptive features viz. exposure to L2 lexical phrases, implicit learning of L2, reiterative exposure to L2 lexical phrases, a rewarding/motivating environment, and an attentional-stimulating setting- on the acquisition of lexical phrases, reading proficiency improvement, and language automatization for intermediate-level university students. The significance of this research lies in the use of a brain-adaptive program where L2 young adult learners can maximize their brain potential and experience neuroeducation-related benefits to overcome L2A difficulties imposed by traditional explicit learning methods which the learners normally undergo in their early stages of L2A. The LIRRA-NP is expected to positively impact learners’ reading proficiency, acquisition of lexical phrases, and automatization of their second language. The LIRRA-NP is postulated to have the potential to allow for the implicit processing of L2 without conscious effort where the brain analyzes language-related data while focusing on the meaning and the overall structure of the language, potentially mirroring the natural L1 acquisition. This enables for the proceduralization of language knowledge including lexical phrases leading to language automatization. Resultantly, language long-term gains and efficiency in performance could be attained. The potential application of the LIRRA-NP is for probable integration into L2 learning and teaching approaches as well as into curriculum textbooks. Also, this program’s design allows for independent exploration and progress, and empowers individual self-paced development. A key functionality of the LIRRA-NP is that it is self-paced and fully online. On the one hand, the self-paced feature allows learners to manage their time and academic load effectively along with the program’s requirements, learn at their convenience regardless of their colleagues’ asynchronous pace, and enforces autonomy. On the other hand, being a fully online program, the LIRRA-NP offers flexibility and ease of access from any technological device. This also adapts to this generation’s preferences in using their mobile and laptop devices through their daily routines. Simultaneously, the use of electronic devices and online learning offers an interesting setting for learning, particularly when coupled with games and interactive material. 1.6. Research questions and hypotheses In light of the aim of this research, the following questions and hypotheses were proposed: RQ1 What is the effect of the LIRRA-NP on improving the acquisition of lexical phrases for intermediate-level L2 young adults? H1 The LIRRA-NP will improve the acquisition of lexical phrases for intermediate-level L2 young adults. RQ2 What is the effect of the LIRRA-NP on automatizing second language performance for intermediate-level L2 young adults? H2 The LIRRA-NP will have an impact on automatizing second language performance for intermediate-level L2 young adults. RQ3 What is the effect of the LIRRA-NP on improving the reading proficiency of intermediate-level L2 young adults? H3 The LIRRA-NP will improve the reading proficiency of intermediate-level L2 young adults. 2. Methodology 2.1. Research design The study investigated the effect of a LIRRA-based neuroeducation program on automatizing second language, and improving reading proficiency and the acquisition of lexical phrases for intermediate-level young learners. Accordingly, the research design was a quasi-experimental design with pre/post-test measures, including a purposefully developed lexical phrases test to determine the participants’ level of knowledge of lexical phrases, a writing test to determine their production of lexical phrases, and a shortened standardized reading test to determine their reading proficiency level. Response times were recorded to determine the degree of automatization for the participants. In this study, automatization is defined, after Segalowitz and Segalowitz ( 1993 ), in terms of a reduction of coefficient variability of reaction time (CVʀт) from the pre-test to post-test. According to Segalowitz and Segalowitz, a reduction in the CVʀт occurs in response to automaticity while a reduction in the reaction time (RT) would only indicate a speed-up performance. Adding to this, positive correlation between CVʀт and mean RT might further validate automaticity; yet, absence of a positive correlation does not negate it (N. Segalowitz, personal communication, July 4, 2022). In light of this, in this study, accuracy increase, and RT decrease would indicated a speed-up performance while CVʀт decrease would indicate a degree of automatization in response to qualitative change in the cognitive underlying mechanisms. 2.2. Participants The present study employed a purposive sampling strategy to recruit a participant pool of 38 Egyptian English learners exhibiting intermediate-level proficiency. Participants were carefully selected from the Department of English within the Faculties of Arts and Education at Ain Shams University. Specific targeting of individuals enrolled in their second or third year of English studies served as the initial criterion for participation, as this academic level typically corresponds to an intermediate proficiency band. To further strengthen the representativeness of the sample in terms of language aptitude, a two-pronged assessment approach was adopted. Firstly, each participant provided a self-rating of their English language proficiency through established scales. Secondly, all individuals completed a standardized placement test specifically designed to gauge intermediate-level language competency. Initially, 57 individuals expressed interest in the program by completing the registration form. However, during the screening process, 19 candidates were excluded due to not meeting the minimum program requirements. Subsequently, during program implementation, an additional eight participants withdrew due to unforeseen commitment conflicts. Consequently, the final sample size consisted of 30 students, encompassing three males (10%) and 27 females (90%). Participants' ages ranged from 18 to 23 years, with a mean age of 19.9 years (SD = 1.124952). Prior to program participation, all subjects confirmed their voluntary participation and informed consent through a designated consent form. 2.3. Instruments The study's main instrument was a custom-designed pre-/post-test built on the Gorilla platform ( https://app.gorilla.sc/ ) to capture reaction time. This self-paced test spanned three key areas: lexical phrase acquisition, language proficiency, and automatization level, with a maximum duration of 2 hours and 45 minutes (including optional breaks). The pre- and post-tests employed a multi-faceted approach to assess the impact of the LIRRA-NP: Section One (A Lexical-phrases Production Test) : This test comprised 50 questions of randomly chosen phrases out of the target phrases incorporated into the LIRRA-NP. This test was based on deliberate deletion of specific words in lexical phrases to test the learners’ knowledge and acquisition of lexical phrases. Section Two (A shortened TOEFL Reading Proficiency Test) : This section, a shortened version of a standardized reading test, assessed participants' general English reading comprehension abilities. Section Three (A Writing Test) : Participants responded to two open-ended prompts in paragraph form. This ensured a focused evaluation of how participants incorporated learned lexical phrases into their language production. This comprehensive test battery, encompassing pre- and post-assessments, enabled a nuanced evaluation of the LIRRA-NP’s effectiveness in enhancing participants' lexical phrase knowledge, and reading skills, and examining language automatization. For internal consistency, section one of the test was assessed for its reliability by employing Pearson Coefficient and Spearman Brown Correction formulae, which revealed a high reliability level of 0.985 and 0.993, respectively. The test was further assessed in terms of its validity by seeking the insights of a panel of experts in the field. 2.4. The Treatment 2.4.1. Phase I: Developing The LIRRA-NP In accordance with the LIRRA framework, the LIRRA-NP was developed according to the key features of the LIRRA model which was speculated to derive neural change. To this end, the LIRRA-NP practically translated the LIRRA model as follows: Exposure to lexical phrase which are the core of the program in that target phrases are incorporated along with other existent ones through the learning material (targeting right hemisphere functions; thus, automatizing L2A). Reiterative exposure of lexical phrases in that the target lexical phrases are repeated eight times across the program in different context. (increasing and strengthening the synaptic connections of neurons; thus, automatizing L2A) . Implicit method of presenting lexical phrases in that the lexical phrases are incorporated into authentic context that is presented without explicit instruction (targeting directly the procedural memory; thus, automatizing L2A) . Motivating/Rewarding structure of the program to spark curiosity via three means: 1) interesting readings and input; 2) motivationally-designed interfaces, games, challenges and interactive tasks; 3) rewarding points for achievements ( stimulating the release of Dopamine; powering neuroplasticity, thus automatizing L2A). An attentional-stimulating setting by providing relevant and novel input through targeted language and memory tasks as well as accentuated emphasis achieved through bolding the lexical phrases ( inducing the release of the neurotransmitters Acetylcholine and Noradrenaline, thus automatizing L2A). 2.4.1.1. Lexical Phrases Extraction The LIRRA-NP targeted three types of lexical phrases categorized after Lewis ( 1993 ): fixed expressions, semi-fixed expressions, and collocations. To foster motivation and introduce these phrases in an engaging context, a corpus tailored to the participants' interests was constructed. Utilizing Sketch Engine ( https://www.sketchengine.eu/ ), a two-million-word corpus ("LIRRA-NP-specific corpus") was compiled from various sources identified as age-appropriate and engaging for the participants. This comprehensive corpus included materials such as jokes, riddles, hypothetical scenarios, moral stories, fascinating scientific facts, and informative content related to everyday life, health, success, diet, career, popular music, movies, celebrities, gender differences, personality traits, and even psychology tricks. Upon compiling the LIRRA-NP-specific corpus, n-gram lexical phrases were extracted. Frequency alone was not the sole determinant of selecting a target phrase. This decision stemmed from the observation that high-frequency phrases often fall within the lower proficiency range, often dominated by prepositions, pronouns, articles, and quantifiers. To ensure targeting for intermediate-level learners, the following criteria were considered. Inclusion criteria included: 1) LIRRA-NP-specific corpus-based phrases; 2) High frequency of use in the language; 3) High frequency of occurrence in the LIRRA-NP-specific corpus; 4) Sketch Engine-analyzed phrases as multi-word units; 5) level K2 words as indicated by Tom Cobb’s website ( www.lextutor.ca ) 1 . The exclusion criteria included: 1) Exclusion of pronouns, relative pronouns, articles, conjunctions, quantifiers, intensifiers, superlatives, modal verbs, modifiers, not, verb.[be], verb.[have], interrogative words (wh-question words) for 2–3 grams; 2) Exclusion of specific structure combinations: [(PREP + PREP, PREP + ART, CONJ + ART, Noun.+PROP/PRON + verb.[be] + ART + noun] for 4–6 grams. Beyond the lexicon of the LIRRA-NP-specific corpus, the program sought to enhance proficiency by incorporating additional phrases from NTC’s Pocket Dictionary of Words and Phrases. These phrases were then meticulously integrated into a context through manual search using Sketch Engine and WebCorp ( https://www.webcorp.org.uk/live/ ) for natural and authentic contexts. Finally, to ensure feasibility, the total pool of target phrases was capped at 300. Following phrase extraction, LIRRA-NP prioritized context integration into an engaging and stimulating format. Recognizing motivation as a cornerstone of the program and a presumed catalyst for neural change, diverse techniques were employed to sustain motivation. Gamification took center stage, featuring online interactive games like word searches, crosswords, puzzles, memory games, and card matching. These were joined by captivating animated PowerPoints, interactive flipbooks, and picturesque documents. Further engagement stemmed from interactive Google Forms for scenarios, experience exchange, and riddles, alongside compelling videos. In some instances, personality and psychology surveys were also incorporated. This rich tapestry of activities sought to foster a dynamic and enjoyable learning environment, maximizing motivation and potentially driving the program's effectiveness in neural change. Maximizing reiterative exposure to target phrases was central to the program's design. Each phrase was strategically presented eight times: five within its designated session and three scattered throughout as engaging revision games. This dual approach ensured both initial immersion and spaced repetition for optimal retention. Additionally, a two-pronged approach catered to the need for an attention-stimulating environment. Firstly, novel and relevant contexts were meticulously curated to introduce the phrases in a meaningful way. Secondly, all target phrases were consistently bolded throughout the materials, prompting implicit learning through heightened visual salience. This emphasis extended to other encountered phrases, further enriching the learners' lexical phrase exposure. 2.4.2. Phase II: Implementing The LIRRA-NP Learners, who expressed initial interest through an online registration form outlining the program's overview, objectives, and expected outcomes, were oriented via a comprehensive Zoom webinar with the program's significance and its unique neuroeducation features. Following this, to ensure appropriate participant proficiency, a placement test was administered to those who expressed further interest. Finally, all participants engaged in a pilot pre-test for baseline assessment before embarking on the 14-week program. The program unfolded throughout the participants' academic year, incorporating flexibility and focus within its structure. Each week featured one engaging session, strategically pausing during mid-year exams and intensifying during the mid-year vacation. The program, estimated to encompass an average of 50 hours of learning, aimed to maximize engagement while accommodating academic commitments. The material was uploaded to Google Classroom. The classroom comprised 23 sessions divided based on the recurrence of the target lexical phrases. Each session was structured to include five elements: 1) Material; a compilation of the various activities mentioned above; 2) Question(s); supported by readings and/or videos for interaction and free discussions; 3) Checkpoint; to check for the comprehension and acquisition of target lexical phrases and collect rewarding points accordingly; 4) My learning log; a log for taking note of the lexical phrases of the learners’ interest; 5) A Satisfaction form; an exit ticket to follow up on the learners’ motivation and satisfaction of the program. Each session functioned as a self-paced learning module. Students were encouraged to explore the diverse material at their convenience, engaging with the games, discussions and various activities, and culminating in a checkpoint activity to assess comprehension and progress. Reward points, awarded based on performance, and unlimited checkpoint retakes fostered a supportive and engaging learning environment. Notably, learners were prompted to focus on bolded lexical phrases, read for deeper context, and log new or intriguing phrases in their personal learning logs. These logs were accessible to all participants, enriching the learning experience through peer-to-peer knowledge exchange. 3. Results 3.1. Overview Demonstrating dedication, all 30 participants met the program's expectations by completing at least 90% of LIRRA-NP tasks. This included diligently engaging with the session materials (material, questions, checkpoints, and learning logs) and actively participating in revisiting key phrases through the revision games. This unwavering commitment yielded a robust dataset for analysis. Both the pre- and post-tests were administered to all participants, and their results were subjected to rigorous statistical analyses to address the core research questions outlined in the study. 3.2. The Effect of the LIRRA-NP on Lexical phrases’ Acquisition Addressing the first research question about LIRRA-NP's impact on lexical phrase acquisition, the analysis focused on responses from the Lexical-phrases production test. A paired-samples t-test revealed a significant increase in participant scores from pre-test (M = 9.97, SD = 8.79) to post-test (M = 37.27, SD = 10.53), t (29) = 14.67, p < 0.0001. This statistically significant improvement demonstrates the program's effectiveness in enhancing the familiarity with and acquisition of target lexical phrases. Further examining writing samples for evidence of lexical phrase acquisition in a free production format, the analysis revealed a marked increase in participants' acquisition and use of lexical phrases. Compared to their pre-writing, where lexical phrases were inaccurate and scarce, the post-writing showcased a significant integration of these phrases. A dependent t-test confirmed this observation, indicating a statistically significant difference between the mean number of correctly used lexical phrases in pre-writing (M = 2.7, SD = 2.24) and post-writing (M = 7.33, SD = 4.16), t (59) = 8.7, p < .0001. This improvement extended to error reduction, with participants demonstrating a notable decline in lexical phrase errors from pre-test (M = 0.55, SD = 0.8) to post-test (M = 0.22, SD = 0.5), t (59) = 2.82, p = 0.003. These findings together provide compelling evidence of the LIRRA-NP’s effectiveness in enhancing participants' ability to readily and accurately acquire and integrate lexical phrases into their writing. The combined evidence from the Lexical-phrases production test and the Writing test paints a clear picture of LIRRA-NP's success. Participants demonstrated a statistically significant improvement in both their acquisition and production of target lexical phrases. Not only did their accuracy increase, but they also incorporated them more effectively into their writing, displaying heightened awareness of appropriate usage. These findings provide robust support for Hypothesis one, confirming the program's effectiveness in enhancing learners' acquisition of lexical phrases. 3.3. The Effect of the LIRRA-NP on Language Automatization To answer the second research question, the coefficient variability of the participants’ reaction time (CVʀт) in the Lexical-phrases production posttest was calculated. Accordingly, the reaction time of the students’ responses was recorded and statistical analyses were conducted to calculate their speed-up performance as well as to calculate the difference in the CVʀт between the pre and post Lexical-phrases production test; additionally, the correlation between RT and CVʀт were further examined. 3.3.1. Speed-up Performance Analysis To assess the program's impact on the speed-up performance of the participants, a within-subject analysis was performed. A dependent t-test compared participants' mean reaction times in the pre- and post-test. The results from the pre-test (M = 9,481,983.41, SD = 12,124,386.09) and post-test (M = 1,497,509.86, SD = 644,086.54) show a significant reduction in the reaction time (in milliseconds) of the participants’ performance, t (29) = 3.5, p = 0.001. This substantial improvement strongly suggests a speed-up effect attributable to the LIRRA-NP. 3.3.2. Automaticity Analyses Investigating potential automaticity gains involved two parallel analyses. First, the CVʀт was calculated for both pre- and post-test data, allowing for a direct comparison of variability in response times. Second, correlation analyses were conducted to examine the relationship between RT and CVʀт at both time points. 3.3.2.1. Co-efficient of Variability Reduction Analysis First, to assess potential automaticity gains, the CVʀт was analyzed for the entire pre- and post-test data. Notably, a substantial reduction in CVʀт was observed, from 1.28 in the pre-test to 0.43 in the post-test, representing a decrease of 0.85. This significant decrease aligns with theoretical suggestions that a decrease in CVʀт, independent of mere speed-up, can indicate a shift towards automaticity in cognitive processing. Second, seeking further insight into potential automaticity gains, a within-subject analysis examined individual changes in CVʀт. While the overall reduction in CVʀт suggests potential automaticity gains, the within-subject analysis presents a seemingly contradictory picture (M = 0.7, SD = 0.2 pre-test, M = 1.09, SD = 0.349 post-test) showed no reduction of CVʀт, t (29) = 5.94, p < 0.01. Accordingly, such potential inaccuracies within those aggregated values required further scrutiny. This discrepancy highlighted the importance of examining individual-level data beyond raw mean comparisons. Therefore, data cleaning was crucial to remove outliers. In this regard, reaction times exceeding three standard deviations (3SD) above the mean, corresponding to 65,000 milliseconds, were discarded. This threshold targeted excessively slow responses potentially indicative of attention lapses, thereby mitigating the influence of exceptionally long response times from individual participants on the overall analysis. Analysis of cleaned data revealed a reduction in the CVʀт for the pre-test scores (M = 0.70, SD = 0.205) compared to post-test scores (M = 0.657, SD = 0.134), t (29) = 1.17, p = 0.12. Although this decrease did not reach statistical significance, it suggests some degree of potential automaticity gains through the LIRRA-NP. Notwithstanding, individual results diverged. While 56.67% of participants exhibited evidence of automaticity, the remaining 43.33% demonstrated performance speed-up, indicating no degree of automaticity as indicated by the CVʀт. 3.3.2.2. Co-efficient of Variability Correlation Analyses The initial investigation into the correlation between CVʀт and mean RT yielded no significant positive correlation (r = 0.066, p = 0.73). Further examination focused on the correlation between CVʀт and overall RT for the post-test data. This analysis revealed a statistically significant, albeit weak, positive correlation ( r = 0.32, p < 0.001). Notably, despite a weak correlation, this positive correlation stands in contrast to the negative correlation observed in the pre-test data ( r = -0.34, p < 0.001). This shift towards a positive correlation suggests a promising impact of the LIRRA-NP on lexical phrase automaticity, indicating that speed gains might be accompanied, to some extent, by automaticity gains. Further analyses showed that if the participants were divided into relatively fast learners and relatively slow learners according to their RT, a moderate positive correlation can be detected between RT and CVʀт in the fast-students group, r = 0.55, p = 0.027 compared to the slow-students group r = 0.36, p = 0.19. This is additional evidence to the more significant impact the LIRRA-NP has on automatizing L2 in relatively fast learners. Similarly, by dividing participants into relatively high-achievers and relatively low-achievers based on their performance in the proficiency test, a strong positive correlation r = 0.82, p = 0.0019 can be drawn between the high-achievers’ CVʀт and RT. Contrarily, a correlation between the low-achievers’ CVʀт and RT demonstrates a weak correlation, r = 0.02, p < 0.001. The writing task provided further evidence of both performance speed-up and automaticity gains. Despite producing longer texts in the post-test, participants' writing speed, as measured by reaction time, significantly improved. Additionally, their entire CVʀт decreased from 0.5 in the pre-test to 0.42 in the post-test. This reduction in variability further suggests that participants achieved greater consistency and automaticity due to the LIRRA-NP. In sum, analyzing reaction times across pre- and post-tests reveals a modulating effect of the LIRRA-NP on automating L2 lexical phrases. Learners with faster RTs or higher proficiency levels exhibited stronger positive correlations between pre- and post-test CVʀт and RT values, suggesting greater automaticity gains within these subgroups. While this partially supports the initial hypothesis of LIRRA-NP's effect on automaticity, the findings also suggest that individual differences in learning speed and proficiency play a crucial role in determining the degree of automatization achieved. 3.4. The Effect of the LIRRA-NP on Language Reading Proficiency To answer the third research question regarding potential proficiency improvements, a dependent-samples paired t-test was conducted. Analyses revealed a significant increase in participants' proficiency scores from pre-test (M = 9.7, SD = 3.23) to post-test (M = 11.53, SD = 4.4), t (29) = 1.81, p = 0.04. This significant difference supports the third hypothesis, providing evidence that LIRRA-NP can contribute to enhanced L2 proficiency. 4. Discussion 4.1. Overview The current study investigated the effect of the LIRRA-NP on language automatization, the acquisition of lexical phrases, and reading proficiency. The LIRRA-NP was developed in accordance with the LIRRA neuroeducational model which consists of brain adaptive features in that it adapts to the equipotentiality of the brain and takes advantage of neuroplasticity. The LIRRA-NP was postulated to bring stronger neuronal synaptic connections and to proceduralize L2 into the procedural memory. The LIRRA-NP hinged on five main features: Exposure to lexical phrases; reiterative nature of lexical phrases presentation; implicit method of presenting lexical phrases; motivational/rewarding nature an attentional-stimulating setting . The LIRRA-NP yielded promising results in that the participants experience a considerable degree of language automatization particularly for relatively faster and high-achieving students. Moreover, the participants’ familiarity and acquisition of lexical phrases as well as their reading proficiency improved. 4.2. Findings of the Study and Previous Research The initial hypothesis postulating that LIRRA-NP would significantly enhance the L2 lexical phrase acquisition of intermediate-level young adults was firmly upheld by the study's outcomes. Analyses revealed a demonstrably positive impact on learner performance, manifesting in both the Lexical-Phrases production test, where post-test scores exceeded pre-test scores significantly, and the writing assessments, showcasing increased frequency and accuracy of lexical phrase utilization compared to pre-program compositions. These findings resonate with existing research for brain adaptability of lexical phrases (Yusuf, 2020 ) and with the endeavors exploring effective methodologies for L2 lexical phrase acquisition, such as Lin's (2022) CALL tool employing formulaic sequence generation based on YouTube content, Puimège and Peters' (2020) audiovisual documentary approach fostering incidental learning of formulaic sequences, and Kim's (2020) animated movie-based program incorporating explicit and implicit lexical phrase activities. The consistent positive outcomes across diverse pedagogical approaches, alongside the compelling data from the current study, not only solidify the efficacy of LIRRA-NP in promoting L2 lexical phrase acquisition but also underscore the pivotal role such acquisition plays in overall L2 development. The second hypothesis regarding LIRRA-NP's potential for automatizing L2 in adult learners received partial confirmation. Analyses revealed a measurable impact for speed-up performance as well as automatization, demonstrated by decreased RT and CVʀт, respectively. Interestingly, the degree of automatization varied, with some individuals exhibiting marked RT decreases while others showed primarily CVʀт reduction. These findings align with established neuroscientific and linguistic literature on automaticity (Diaz & McCarthy, 2007 ; Frost et al., 2000 ; Garrod & Pickering, 2004 ; Morgan-Short et al., 2014 ; Näätänen, 2001 ; Shtyrov & Pulvermüller, 2007 ; Acamatsu, 2008; Dekeyser, 1997 , 2007 ; Elgort, 2011 ; Elgort & Warren, 2014 ; Ellis & Schmidt, 1997 ; Fukkink et al., 2005 ; Li, 2021; Lim & Godfroid, 2015 ; Logan, 1990 ; Ma & Zhang, 2017; N. S. Segalowitz et al., 1995 ; Pellicer-Sánchez, 2015 ; Pili-Moss et al., 2020 ; Segalowitz & Freed, 2004 ; Snellings et al., 2002 ; Van Gelderen et al., 2004 ). Practice and repeated exposure to lexical phrases as the core element of LIRRA-NP appear to be key drivers of automaticity gains. This finding resonates with research by Bybee ( 2008 ), N. C. Ellis ( 1996 , 2002 ), Gatbonton ( 1994 ), and Wray ( 2002 ), who emphasize the crucial role of lexical phrases in L2 automatization. It is worth noting that the results revealed differential impacts of LIRRA-NP on automaticity based on individual learning speed and proficiency level. High achievers and faster learners exhibited greater automicity gains, as evidenced by their stronger positive correlations between RT and CVʀт post-intervention. These findings echo those of Segalowitz and Segalowitz ( 1993 ), who observed similar patterns in a year-long study of English-speaking French learners. They attributed greater automaticity in faster performers to potentially requiring less effort for learning. In the present study, higher achievers and faster learners' lower learning demands within the program might explain their enhanced automaticity. Conversely, low achievers and slower learners may require more focused and intensive support, as suggested by Abutalebi and Green ( 2007 ) and Perani et al. ( 2003 ). In this regard, it can be assumed that learners with lower proficiency and slower performance may require extended exposure to activate the neural mechanisms underlying language automaticity. Accordingly, increased exposure to lexical phrases through an extended program with multiple phases/levels might be beneficial for this group to fully harness LIRRA-NP's potential for automaticity gains. It is suspected that the considerable difference between the performance of some slow participants ( r = 0.02) in comparison with their faster counterparts ( r = 0.82) may have played a role in preventing an overall positive correlation between RT and CVʀт for all participants; which would have further validated automaticity gains. Unequal test item lengths likely impacted the SD and CVʀт calculations, inflating post-test values. Shorter items would naturally elicit faster responses compared to longer ones, further contributing to the variation in reaction times and potentially inflating the overall SD. Consequently, dividing the inflated SD by the reduced mean reaction time may erroneously increase the CVʀт, leading to misleading conclusions about individual-level automaticity gains. The varying lengths, coupled with a whole-test time limit instead of time control within individual test trials, may have introduced extraneous factors influencing response times and misleadingly suggesting greater automaticity gains. The hypothesis that LIRRA-NP would enhance L2 reading proficiency in adult learners was confirmed. Pre- and post-test scores revealed statistically significant improvement in overall reading proficiency. This finding aligns with a growing body of research highlighting the crucial role of lexical phrases in language learning and achieving native-like performance (Arnon et al., 2017; Biber et al., 2007 ; N. C. Ellis, 1996 ; Hatami, 2015 ; Mohammadi & Enayati, 2018 ; Nattinger & DeCarrico, 1992; Rafieyan, 2018 ). Participants initially reported increased self-awareness of their enhanced comprehension and the ability to process L2 texts more efficiently, leading to improved confidence in thinking, writing, and producing L2 output. These subjective experiences were corroborated by significant gains in both the reading proficiency and writing tests. These results resonate with prior studies by Arnon and Snider ( 2010 ), Boers et al. ( 2006 ), Chandra ( 2014 ), Hoang & Bores (2016), and Wood ( 2009 , 2010 ), who demonstrated the positive impact of lexical phrases on L2 learners' fluency and proficiency levels. Notably, this is supportive of the lexical approach (Lewis, 1993 ) which posits that prioritizing multi-word expressions over individual words is key to achieving native-like L2 performance. 4.3. Insights and Implications This study's findings reveal several profound implications for brain-adaptive L2 learning environments. LIRRA-NP demonstrably fosters qualitative change in L2 skill development, impacting lexical phrase familiarity, reading proficiency, and automaticity. This adaptability to the human brain resonates with neuroeducation (Danesi, 2003 ; Netten & Germain, 2012 ) and neuroplasticity research (Chang et al., 2017; Merzenich et al., 1996 ; Rogowsky et al., 2013 ). These findings highlight the promising potential of brain-adaptive programs like LIRRA-NP in optimizing L2 learning and reaching a native-like performance through brain-based approaches. Secondly, the results support the improvement of second language using an implicit method of instruction. In congruence with the studies that support implicit learning on the linguistic and psycholinguistic levels (Chan & Leung, 2014 ; Francis et al., 2009 ; Kerz et al., 2017 ; Morgan-Short, 2007 ; Yusuf, 2021 ), this study supports implicit learning for lexical phrases forms and contextual use. The findings from this study add to the growing evidence base supporting the efficacy of implicit methods, like those employed in LIRRA-NP, for effectively enhancing L2 proficiency, particularly in the domain of lexical phrases. Repetitive exposure to lexical phrases, a core feature of LIRRA-NP, aligns with the established importance of practice in both language proficiency (N.S. Segalowitz, 2010 ; Paradis, 2004 , 2009 ) and automatization (Dekeyser, 2015 ; Goswami, 2008 ; Lee, 2004 ). While this study cannot conclusively determine whether this exposure facilitated L2 automatization through proceduralization of declarative knowledge or via direct procedural learning, it is suggested that eight repetitions within meaningful contexts were sufficient to impact procedural memory and internalize language skills. This finding emphasizes the significance of both practice and meaningful context for effective language acquisition. However, clarifying the specific mechanisms underlying L2 automatization requires further neurolinguistic investigation. This study further suggests that intrinsic motivation may play a crucial role in L2 automatization by promoting neurological restructuring. Consistent with existing literature (Goswami, 2008 ; Lee, 2004 ; Merzenich et al., 2014), motivation and reward are hypothesized to facilitate the transformation of declarative memory into procedural memory, trigger neuroplasticity, and elevate dopamine levels, creating a rewarding learning environment. This study underlines the importance of prioritizing engaging, interactive, and adaptable learning environments and materials over traditional reward systems in language program development. 4.4. Practical Implications Based on the promising results of this study, several practical implications emerge for utilizing LIRRA-NP in improving second language for intermediate adult L2 learners are worth mentioning. Its efficacy extends beyond standalone implementation, offering seamless integration into existing curricula for enriched L2 pedagogy and empowering self-directed learning trajectories. It is recommended to adapt the LIRRA-NP for diverse proficiency levels (beginner, advanced), age groups, language domains (grammar, writing), and even across languages, democratizing effective and engaging L2 acquisition opportunities. This multifaceted approach promises not only enhanced proficiency but also personalized, inclusive, and globally-accessible L2 learning experiences. 4.5. Limitations Due to resource limitations, the utilization of neuroimaging or neurophysiological techniques, such as fMRI, EEG, and eye-tracking, to directly assess pre- and post-intervention changes in brain structures associated with L2 processing was constrained. Implementing these measures would have provided valuable in vivo insights into the program's impact on L2 representation and neural qualitative underlying restructure of the brain. 4.6. Recommendations for Future Research Future research directions can build upon the promising results of this study by a number of recommendations. First, exploring an intensive or an extended version of LIRRA-NP; comparing its impact on automatization with the current version could reveal the program's potential for enhanced skill acquisition, particularly among low-achievers and slow learners. Second, Utilizing neurophysiological techniques: Employing fMRI, EEG, or eye-tracking could provide in-vivo insights into the program's effects on brain structures and cognitive processes before and after intervention, elucidating the mechanisms underlying automatization. Third, developing immersive game-based versions: Creating LIRRA-NP as interactive graphic games, clue-based challenges, or virtual reality experiences could further enhance learner engagement and motivation, potentially increasing immersion and yielding further improvements in proficiency and automaticity. 4.7. Conclusion This study examined the efficacy of the LIRRA-NP in fostering language automatization, lexical phrase acquisition, and reading proficiency enhancement. Grounded in the LIRRA neuroeducational model, the LIRRA-NP leverages brain equipotentiality and neuroplasticity through adaptive features. Notably, the LIRRA-NP yielded promising results, particularly for faster and higher-achieving learners, demonstrating considerable gains in language automatization, and significant gains in lexical phrase acquisition and reading proficiency. The findings from this study open up the possibility that a LIRRA model-based neuroeducation program, such as the LIRRA-NP, could serve as a transformative tool for second language acquisition in intermediate-level young adults. By acknowledging the brain's crucial role in language processing and incorporating evidence-based practices, LIRRA-NP has the potential to revolutionize L2 teaching. Declarations The study ethics were followed and the study was approved by Ain Shams University. References Abutalebi, J., & Green, D. (2007). Bilingual language production: The neurocognition of language representation and control. Journal of Neurolinguistics, 20 (3), 242–275. https://doi.org/10.1016/j.jneuroling.2006.10.003 Akamatsu, N. (2008). The effects of training on automatization of word recognition in English as a foreign language. Applied Psycholinguistics, 29 (2), 175-193. http://doi.org/10.1017/S0142716408080089 Albert, M., & Obler, L.K. (1978). 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Language Learning 52 (4), 723–754. https://doi.org/10.1111/1467-9922.00202 Sperry, R. (1973) Lateral specialization of cerebral function in surgically-separated hemispheres. In F. J. McGuigan & R. A. Schooner (Eds.), The psychophysiology of thinking (pp. 205-229). Academic Press. Ullman, M. T. (2005). A cognitive neuroscience perspective on second language acquisition: methods, theory, and practice. In C. Sanz (Ed .), Mind and context in adult second language acquisition (pp. 141-168). Georgetown University. https://www.jstor.org/stable/j.ctt2tt6xc Ullman, M. T. (2015). The declarative/procedural model: A neurobiologically motivated theory of first and second language. In B. Van Batten & J. Williams (Eds.), Theories in second language acquisition (2nd ed., pp. 128-161). Routledge. https://doi.org/10.4324/9780203628942 Van Gelderen, A., Schoonen, R., De Glopper, K., Hulstijn, J., Simis, A., Snellings, P. & Stevenson, M. (2004). Linguistic knowledge, processing speed and metacognitive knowledge in first- and second-language reading comprehension: A componential analysis. Journal of Educational Psychology, 96 (1), 19–30. http://dx.doi.org/10.1037/0022-0663.96.1.19 Van Lancker, D. & Yang, S. (2017). Formulaic language performance in left- and right-hemisphere damaged patients: structured testing. Aphasiology, 31 (1), 82-99. https://doi.org/10.1080/02687038.2016.1157136 Warrington, E. K. & Shallice, T. (1984). Category-specific semantic impairment. Brain, 107 (Pt 3), 829-854. https://doi.org/10.1093/brain/107.3.829 Wood, D. (2009). Effects of focused instruction of formulaic sequences on fluent expression in second language narratives: A case study. The Canadian Journal of Applied Linguistics, 12 , 39-57. Wood, D. (2010). Formulaic language and second language speech fluency: Background, evidence and classroom applications . Continuum. Wray, A. (1992). The focusing hypothesis: The theory of left hemisphere lateralized language re-examined . John Benjamins. https://doi.org/10.1075/sspcl.3 Wray, A. (2002). Formulaic language and the lexicon . Cambridge University Press. https://doi.org/10.1017/CBO9780511519772 Yusuf, N. H. (2020). Lexical phrases: An essential brain-adaptive requisite for second language acquisition. International Journal of Linguistics, 12 (3), 153-171. https://doi.org/10.5296/ijl.v12i3.17260 Yusuf, N. H. (2021). Implicit learning in second language acquisition: Insights from neuroscientific data. Communication and Linguistics Studies, 7 (2), 21-30. http://doi.org/10.11648/j.cls.20210702.11 Yusuf, N. H. (2024). A Neuroeducational model for Improving Second Language Acquisition in Late Learners. [manuscript submitted for publication]. Department of English and Scientific Methods, The German University in Cairo. Footnotes Level K1 refers to the 1st 1,000 Most Frequent Words of English (words 1 to 1000) Level K2 refers to the 2nd 1,000 Most Frequent Words of English (words 1001 to 2000) Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4337380","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296331343,"identity":"e519e668-5745-41f2-828e-37318928576a","order_by":0,"name":"Nermin H. Yusuf","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYLCCBDACgg8QvgHxWhhnEK2FAaqFmYcYLebsvU83PNzBkMfPf/aYtM2vw/IM7M3bJBgqanFqsew5bnYj8QxDseSMvDTp3L7Dhg08x8okGM4cx6nF4EYa243ENobEDTd4zKRzew4nMEjkmEkwth3DreX+M6iW82fMpC1BWuTfALX8w6PlBhtUy4EcM2mGHyBbeIBaGmpwazkDdphE4swZOcaWvQ3phm08acUWCccO4NZy/BjbzZ9tNon9/GcMb/z4Yy3Pz354440PNXU4tUCBBIhgAfqagYENxExgOExICxgwf2D4A+cQtGUUjIJRMApGDgAA9QlVJiHSSX8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0000-7340-0038","institution":"The German University in Cairo","correspondingAuthor":true,"prefix":"","firstName":"Nermin","middleName":"H.","lastName":"Yusuf","suffix":""}],"badges":[],"createdAt":"2024-04-28 10:02:07","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4337380/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4337380/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55736970,"identity":"ae4faffa-2344-4f28-8e35-0c109b4cdc82","added_by":"auto","created_at":"2024-05-02 12:36:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":694428,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4337380/v1/27ba9d23-4c8a-4590-ab0f-4163c1b81429.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Effect of a Neuroeducation Program on L2 Automatization and Acquisition for Intermediate Young Adult Learners\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe field of neuroeducation has emerged as a prominent area of study within contemporary insights. Neuroeducation broadly encompasses findings in neuroscience with potential application in education (Howard-Jones et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As a seminal cross-disciplinary approach, it has prompted a surge of investigation across a diverse spectrum of educational domains. Neuroeducation has been investigated in the literature from two main routes, viz. the direct route and the indirect route. On the one hand, the direct route -which is out of the scope of this paper- examines the direct relationship of the biological brain to educational outcomes; in this route, researchers investigated physical fitness, diet and sleep patterns, relaxation and meditation, and other environmental factors including pollution. On the other hand, the indirect route -which is the focus of this paper- proposes new learning techniques to fill the gap between cognitive neuroscience and education; investigations of concern to this route are on executive and cognitive functions, emotion regulation, and social cognition.\u003c/p\u003e \u003cp\u003eAcross the educational domains, the burgeoning field of neuroeducation holds promising potential to inform more effective methods towards second language acquisition (L2A). By shedding light on the brain's mechanisms for language learning such as neuroplasticity and the equipotentiality of the brain, neuroeducation offers insights for improving L2A.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Neuroplasticity and the Equipotentiality of the Brain\u003c/h2\u003e \u003cp\u003eNeuroplasticity, which is the amenability of the brain to change according to stimuli, was misconceived to cease in adulthood while it was contrarily shown to continue into adulthood (Li et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Counter-plasticity views were challenged by numerous evidence demonstrating ongoing brain plasticity in adults, as new input continues to stimulate growth and formation of fiber connections (Goswami, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), suggesting that functional and structural changes, albeit with constraints, persist throughout adulthood (Ansari et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A particularly noteworthy feature of adulthood plasticity is that it differs distinctively from childhood plasticity in the distinction between experience-expectant and experience-dependent plasticity. Experience-expectant plasticity -which is the childhood plasticity- involves inherent brain growth responding to environmental stimuli such as visual and auditory stimuli. Experience-expectant plasticity can be considered as universal inputs which shape the brain of infants in almost identical ways. However, experience-dependent plasticity -the adulthood plasticity- is shaped by individual experiences and varies based on unique life experiences (Goswami, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In language acquisition, first language acquisition (L1A) exemplifies experience-expectant plasticity, responding innately to language stimuli for neurotypical children. Conversely, L2A showcases experience-dependent plasticity, with learning experiences depending heavily on the quality of input. In accordance with this, neuroplasticity is an ongoing process that can be induced via training which triggers changes in white and grey matter volume, as well as increases activation in brain regions (Ansari et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Above all else, neurogenesis -the birth of new neurons- is evidenced to occur in language-related areas during adulthood, particularly in the hippocampus (Howard-Jones, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn line with neuroplasiticity, the adult brain is advantaged with its equipotentiality. Yet, regrettably, hemispheric equipotentiality has been ignored in L2A approaches in that there is a paucity of involving the right-hemisphere in the language learning approaches which focus primarily on the left hemisphere abilities in language learning despite the role the right hemisphere contributes to language acquisition. The role of the right hemisphere has been supported by various studies positing that the right hemisphere is responsible for the processing of several other non-analytical language-related functions including the processing of lexical phrases as well as the processing of pragmatics and non-literal language like indirect speech acts, connotative meaning, and figurative language. (Albert \u0026amp; Obler, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Damasio et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Hillis \u0026amp; Caramazza, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Paradis, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Segalowitz, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1983\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sperry et al., 1973; Warrington \u0026amp; Shallice, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn line with the aforementioned, neuroeducation theories and approaches such as biomodality (Danesi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and the neurolinguistic approach (Netten \u0026amp; Germain, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and neuroplasticity-based programs (Merzenich et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Rogowsky et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) emerged to address L2A challenges and to take advantage of the equipotentiality of the brain and/or neuroplasticity for a more effective L2A. Yet, the investigation of neuroeducation programs in second language learning remains sparse. Despite this, the potential of its results is promising. In light of this, the Neurolinguistic Approach (NLA), for example, emphasizing implicit competence and authentic language engagement, yielded remarkable results in studies by Gal-Bailly (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Ricordel (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Consistent with these results, other programs emphasizing gamification to promote learning and motivation also showed a promising potential for neuroeducation (Howard-Jones et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similar significant results were obtained from programs hinging on cognitive skills training (Merzenich et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Rogowsky et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); the results of these programs demonstrated significant improvements in language skills for L2 learners and for children with learning difficulties. While these findings highlight the promising prospects of neuroeducation in L2A, further contemporary research was due to particularly address neuroeducation programs with a focus on proceduralizing and automatizing second language learning for adult learners.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Language Proceduralization and Automaticity\u003c/h2\u003e \u003cp\u003eLanguage proceduralization refers to the transformation of linguistic knowledge from reliance on declarative memory to dependence on procedural memory. The procedural memory is postulated to play a factorial role in language learning and underlies implicit and unconscious learning (Ullman, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In essence, proceduralization entails routing language through the procedural memory instead of the declarative memory. This can occur either through implicit exposure to language, where the procedural memory directly acquires knowledge, or through or by developing the declarative knowledge into a procedural one. Significantly, proceduralized knowledge offers an advantage over declarative knowledge due to its effortless processing, enhancing fluency and automaticity in language use. This is because procedural knowledge does not require a learner to assemble pieces of information into a program for a certain behavior; \u0026ldquo;instead, that \u0026lsquo;program\u0026rsquo; is now available as a ready-made chunk to be called up in its entirety each time the conditions for that behavior are met\u0026rdquo; (DeKeyser, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, p. 95). In language learning, proceduralization is pivotal for processing language functions in an effortless manner; hence, ultimately achieve language automaticity.\u003c/p\u003e \u003cp\u003eAutomaticity, in general, refers to a quick process which occurs without much awareness and control; through automatic process, and by sufficient mental practice, a skill is well-developed and performed in a quick as well as an accurate manner (Schneider et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Shiffrin \u0026amp; Schneider, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Language automaticity, in particular, which is achieved through the procedural memory, refers to the efficient use of language where language performance becomes mechanical with minimal effort and minimal errors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3. The LIRRA Model\u003c/h2\u003e \u003cp\u003eOne of the neuroeducational models that was proposed to proceduralize language and achieve automaticity is the LIRRA neuroeducational model (Yusuf, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This groundbreaking approach paves the way for the practical application of neuroeducation in second language acquisition, offering promising opportunities for late learners to enhance their language acquisition. The LIRRA neuroeducational model rests primarily on five key brain-adaptive features, namely \u003cem\u003eexposure to L2 lexical phrases, implicit learning of L2, reiterative exposure to L2 lexical phrases, a rewarding/motivating environment, and an attentional-stimulating setting\u003c/em\u003e. It is postulated that incorporating a LIRRA neuroeducational model into a neuroeducation program would facilitate L2A process and help proceduralize and automatize L2A.\u003c/p\u003e \u003cp\u003eFirst, the neuroeducation program is postulated to achieve its purpose by virtue of improving the acquisition of lexical phrases. This is due to the grounded speculation that by exposing L2 learners to \u003cem\u003elexical phrases\u003c/em\u003e, the right hemisphere of the brain becomes involved in the learning process; this addresses the equipotentiality of the brain which allows for the proceduralization of L2 knowledge resulting in an improved proficiency and an enhanced automatic language performance. In a similar vein, \u003cem\u003eimplicit learning\u003c/em\u003e is postulated to address the procedural memory directly, thus internalizing and proceduralizing L2 knowledge as well as storing L2 knowledge in the long-term memory leading to a more-efficient language performance and automatization. In like manner, by a \u003cem\u003ereiterative exposure\u003c/em\u003e to L2 phrases, stronger synapsis occurs and neural activation increases, in particular, in the basal ganglia -the core of the procedural memory. This allows for strengthening and proceduralizing L2 knowledge leading to an enhanced L2 performance and automatization. Additionally, a \u003cem\u003erewarding/motivating environment\u003c/em\u003e is key to the learning process since reward/motivation stimulates the release of dopamine and triggers neuroplasticity; hence, surmounts age-related limitations and improves the learning experience; importantly, reward is a neuroplasticity-based feature which powers automatization. Finally, \u003cem\u003ean attentional-stimulating setting\u003c/em\u003e via the use of novel and relevant material is pivotal to inducing attentional neurotransmitters such as acetylcholine and noradrenaline, thus deriving neural change and triggering neuroplasticity which aid automatize the language as well as aid the formation of memory. This proposed neuroeducational model equipped with these brain-adaptive features is predicted to foster a brain-adaptable environment for L2A in a way that mirrors L1A, promoting efficient and accurate language development towards native-like proficiency while bypassing the obstacles and inaccuracies inherent in explicit instructional methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4. Rationale of the Research\u003c/h2\u003e \u003cp\u003eAccording to the best of the researcher\u0026rsquo;s knowledge, there is a dearth of studies that use a brain-based neuroeducation program to automatize second language in young adults. In particular, there are no studies, according to the best of the researcher\u0026rsquo;s knowledge, that use a neuroeducation program which encompasses the LIRRA model brain-adaptive features. Accordingly, this is the first study that investigates a neuroeducation program using the LIRRA neuroeducational model for the primary purpose of enhancing L2A through aiding automatize and proceduralize second language in young adults. In essence, this research proposes a computer- LIRRA neuroeducational model-based neuroeducation program to investigate its effect on L2A for intermediate-level young adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.5. Aim and Significance\u003c/h2\u003e \u003cp\u003eThe aim of this research is to investigate the effect of a LIRRA neuroeducational model-based program (LIRRA-NP) -which hinges on five core brain-adaptive features viz. \u003cem\u003eexposure to L2 lexical phrases, implicit learning of L2, reiterative exposure to L2 lexical phrases, a rewarding/motivating environment, and an attentional-stimulating setting-\u003c/em\u003e on the acquisition of lexical phrases, reading proficiency improvement, and language automatization for intermediate-level university students. The significance of this research lies in the use of a brain-adaptive program where L2 young adult learners can maximize their brain potential and experience neuroeducation-related benefits to overcome L2A difficulties imposed by traditional explicit learning methods which the learners normally undergo in their early stages of L2A. The LIRRA-NP is expected to positively impact learners\u0026rsquo; reading proficiency, acquisition of lexical phrases, and automatization of their second language. The LIRRA-NP is postulated to have the potential to allow for the implicit processing of L2 without conscious effort where the brain analyzes language-related data while focusing on the meaning and the overall structure of the language, potentially mirroring the natural L1 acquisition. This enables for the proceduralization of language knowledge including lexical phrases leading to language automatization. Resultantly, language long-term gains and efficiency in performance could be attained. The potential application of the LIRRA-NP is for probable integration into L2 learning and teaching approaches as well as into curriculum textbooks. Also, this program\u0026rsquo;s design allows for independent exploration and progress, and empowers individual self-paced development.\u003c/p\u003e \u003cp\u003eA key functionality of the LIRRA-NP is that it is self-paced and fully online. On the one hand, the self-paced feature allows learners to manage their time and academic load effectively along with the program\u0026rsquo;s requirements, learn at their convenience regardless of their colleagues\u0026rsquo; asynchronous pace, and enforces autonomy. On the other hand, being a fully online program, the LIRRA-NP offers flexibility and ease of access from any technological device. This also adapts to this generation\u0026rsquo;s preferences in using their mobile and laptop devices through their daily routines. Simultaneously, the use of electronic devices and online learning offers an interesting setting for learning, particularly when coupled with games and interactive material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.6. Research questions and hypotheses\u003c/h2\u003e \u003cp\u003eIn light of the aim of this research, the following questions and hypotheses were proposed:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRQ1\u003c/strong\u003e \u003cp\u003eWhat is the effect of the LIRRA-NP on improving the acquisition of lexical phrases for intermediate-level L2 young adults?\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eThe LIRRA-NP will improve the acquisition of lexical phrases for intermediate-level L2 young adults.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRQ2\u003c/strong\u003e \u003cp\u003eWhat is the effect of the LIRRA-NP on automatizing second language performance for intermediate-level L2 young adults?\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eThe LIRRA-NP will have an impact on automatizing second language performance for intermediate-level L2 young adults.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRQ3\u003c/strong\u003e \u003cp\u003eWhat is the effect of the LIRRA-NP on improving the reading proficiency of intermediate-level L2 young adults?\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eThe LIRRA-NP will improve the reading proficiency of intermediate-level L2 young adults.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. Research design\u003c/h2\u003e\n\u003cp\u003eThe study investigated the effect of a LIRRA-based neuroeducation program on automatizing second language, and improving reading proficiency and the acquisition of lexical phrases for intermediate-level young learners. Accordingly, the research design was a quasi-experimental design with pre/post-test measures, including a purposefully developed lexical phrases test to determine the participants\u0026rsquo; level of knowledge of lexical phrases, a writing test to determine their production of lexical phrases, and a shortened standardized reading test to determine their reading proficiency level. Response times were recorded to determine the degree of automatization for the participants. In this study, automatization is defined, after Segalowitz and Segalowitz (\u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e), in terms of a reduction of coefficient variability of reaction time (CVʀт) from the pre-test to post-test. According to Segalowitz and Segalowitz, a reduction in the CVʀт occurs in response to automaticity while a reduction in the reaction time (RT) would only indicate a speed-up performance. Adding to this, positive correlation between CVʀт and mean RT might further validate automaticity; yet, absence of a positive correlation does not negate it (N. Segalowitz, personal communication, July 4, 2022). In light of this, in this study, accuracy increase, and RT decrease would indicated a speed-up performance while CVʀт decrease would indicate a degree of automatization in response to qualitative change in the cognitive underlying mechanisms.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2. Participants\u003c/h2\u003e\n\u003cp\u003eThe present study employed a purposive sampling strategy to recruit a participant pool of 38 Egyptian English learners exhibiting intermediate-level proficiency. Participants were carefully selected from the Department of English within the Faculties of Arts and Education at Ain Shams University. Specific targeting of individuals enrolled in their second or third year of English studies served as the initial criterion for participation, as this academic level typically corresponds to an intermediate proficiency band. To further strengthen the representativeness of the sample in terms of language aptitude, a two-pronged assessment approach was adopted. Firstly, each participant provided a self-rating of their English language proficiency through established scales. Secondly, all individuals completed a standardized placement test specifically designed to gauge intermediate-level language competency.\u003c/p\u003e\n\u003cp\u003eInitially, 57 individuals expressed interest in the program by completing the registration form. However, during the screening process, 19 candidates were excluded due to not meeting the minimum program requirements. Subsequently, during program implementation, an additional eight participants withdrew due to unforeseen commitment conflicts. Consequently, the final sample size consisted of 30 students, encompassing three males (10%) and 27 females (90%). Participants' ages ranged from 18 to 23 years, with a mean age of 19.9 years (SD\u0026thinsp;=\u0026thinsp;1.124952). Prior to program participation, all subjects confirmed their voluntary participation and informed consent through a designated consent form.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3. Instruments\u003c/h2\u003e\n\u003cp\u003eThe study's main instrument was a custom-designed pre-/post-test built on the Gorilla platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://app.gorilla.sc/\u003c/span\u003e\u003c/span\u003e) to capture reaction time. This self-paced test spanned three key areas: lexical phrase acquisition, language proficiency, and automatization level, with a maximum duration of 2 hours and 45 minutes (including optional breaks). The pre- and post-tests employed a multi-faceted approach to assess the impact of the LIRRA-NP:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSection One (A Lexical-phrases Production Test)\u003c/strong\u003e: This test comprised 50 questions of randomly chosen phrases out of the target phrases incorporated into the LIRRA-NP. This test was based on deliberate deletion of specific words in lexical phrases to test the learners\u0026rsquo; knowledge and acquisition of lexical phrases.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSection Two (A shortened TOEFL Reading Proficiency Test)\u003c/strong\u003e: This section, a shortened version of a standardized reading test, assessed participants' general English reading comprehension abilities.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSection Three (A Writing Test)\u003c/strong\u003e: Participants responded to two open-ended prompts in paragraph form. This ensured a focused evaluation of how participants incorporated learned lexical phrases into their language production.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis comprehensive test battery, encompassing pre- and post-assessments, enabled a nuanced evaluation of the LIRRA-NP\u0026rsquo;s effectiveness in enhancing participants' lexical phrase knowledge, and reading skills, and examining language automatization. For internal consistency, section one of the test was assessed for its reliability by employing Pearson Coefficient and Spearman Brown Correction formulae, which revealed a high reliability level of 0.985 and 0.993, respectively. The test was further assessed in terms of its validity by seeking the insights of a panel of experts in the field.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4. The Treatment\u003c/h2\u003e\n\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n\u003ch2\u003e2.4.1. Phase I: Developing The LIRRA-NP\u003c/h2\u003e\n\u003cp\u003eIn accordance with the LIRRA framework, the LIRRA-NP was developed according to the key features of the LIRRA model which was speculated to derive neural change. To this end, the LIRRA-NP practically translated the LIRRA model as follows:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eExposure to lexical phrase which are the core of the program in that target phrases are incorporated along with other existent ones through the learning material \u003cem\u003e(targeting right hemisphere functions; thus, automatizing L2A).\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReiterative exposure of lexical phrases in that the target lexical phrases are repeated eight times across the program in different context. \u003cem\u003e(increasing and strengthening the synaptic connections of neurons; thus, automatizing L2A)\u003c/em\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eImplicit method of presenting lexical phrases in that the lexical phrases are incorporated into authentic context that is presented without explicit instruction \u003cem\u003e(targeting directly the procedural memory; thus, automatizing L2A)\u003c/em\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMotivating/Rewarding structure of the program to spark curiosity via three means: 1) interesting readings and input; 2) motivationally-designed interfaces, games, challenges and interactive tasks; 3) rewarding points for achievements (\u003cem\u003estimulating the release of Dopamine; powering neuroplasticity, thus automatizing L2A).\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAn attentional-stimulating setting by providing relevant and novel input through targeted language and memory tasks as well as accentuated emphasis achieved through bolding the lexical phrases (\u003cem\u003einducing the release of the neurotransmitters Acetylcholine and Noradrenaline, thus automatizing L2A).\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv id=\"Sec14\" class=\"Section4\"\u003e\n\u003ch2\u003e2.4.1.1. Lexical Phrases Extraction\u003c/h2\u003e\n\u003cp\u003eThe LIRRA-NP targeted three types of lexical phrases categorized after Lewis (\u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e): fixed expressions, semi-fixed expressions, and collocations. To foster motivation and introduce these phrases in an engaging context, a corpus tailored to the participants' interests was constructed. Utilizing Sketch Engine (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sketchengine.eu/\u003c/span\u003e\u003c/span\u003e), a two-million-word corpus (\"LIRRA-NP-specific corpus\") was compiled from various sources identified as age-appropriate and engaging for the participants. This comprehensive corpus included materials such as jokes, riddles, hypothetical scenarios, moral stories, fascinating scientific facts, and informative content related to everyday life, health, success, diet, career, popular music, movies, celebrities, gender differences, personality traits, and even psychology tricks.\u003c/p\u003e\n\u003cp\u003eUpon compiling the LIRRA-NP-specific corpus, n-gram lexical phrases were extracted. Frequency alone was not the sole determinant of selecting a target phrase. This decision stemmed from the observation that high-frequency phrases often fall within the lower proficiency range, often dominated by prepositions, pronouns, articles, and quantifiers. To ensure targeting for intermediate-level learners, the following criteria were considered. Inclusion criteria included: 1) LIRRA-NP-specific corpus-based phrases; 2) High frequency of use in the language; 3) High frequency of occurrence in the LIRRA-NP-specific corpus; 4) Sketch Engine-analyzed phrases as multi-word units; 5) level K2 words as indicated by Tom Cobb\u0026rsquo;s website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://app.gorilla.sc/\" target=\"_blank\"\u003ewww.lextutor.ca\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e1\u003c/sup\u003e. The exclusion criteria included: 1) Exclusion of pronouns, relative pronouns, articles, conjunctions, quantifiers, intensifiers, superlatives, modal verbs, modifiers, not, verb.[be], verb.[have], interrogative words (wh-question words) for 2\u0026ndash;3 grams; 2) Exclusion of specific structure combinations: [(PREP\u0026thinsp;+\u0026thinsp;PREP, PREP\u0026thinsp;+\u0026thinsp;ART, CONJ\u0026thinsp;+\u0026thinsp;ART, Noun.+PROP/PRON\u0026thinsp;+\u0026thinsp;verb.[be]\u0026thinsp;+\u0026thinsp;ART\u0026thinsp;+\u0026thinsp;noun] for 4\u0026ndash;6 grams.\u003c/p\u003e\n\u003cp\u003eBeyond the lexicon of the LIRRA-NP-specific corpus, the program sought to enhance proficiency by incorporating additional phrases from \u003cem\u003eNTC\u0026rsquo;s Pocket Dictionary of Words and Phrases.\u003c/em\u003e These phrases were then meticulously integrated into a context through manual search using Sketch Engine and WebCorp (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.webcorp.org.uk/live/\u003c/span\u003e\u003c/span\u003e) for natural and authentic contexts. Finally, to ensure feasibility, the total pool of target phrases was capped at 300.\u003c/p\u003e\n\u003cp\u003eFollowing phrase extraction, LIRRA-NP prioritized context integration into an engaging and stimulating format. Recognizing motivation as a cornerstone of the program and a presumed catalyst for neural change, diverse techniques were employed to sustain motivation. Gamification took center stage, featuring online interactive games like word searches, crosswords, puzzles, memory games, and card matching. These were joined by captivating animated PowerPoints, interactive flipbooks, and picturesque documents. Further engagement stemmed from interactive Google Forms for scenarios, experience exchange, and riddles, alongside compelling videos. In some instances, personality and psychology surveys were also incorporated. This rich tapestry of activities sought to foster a dynamic and enjoyable learning environment, maximizing motivation and potentially driving the program's effectiveness in neural change.\u003c/p\u003e\n\u003cp\u003eMaximizing reiterative exposure to target phrases was central to the program's design. Each phrase was strategically presented eight times: five within its designated session and three scattered throughout as engaging revision games. This dual approach ensured both initial immersion and spaced repetition for optimal retention. Additionally, a two-pronged approach catered to the need for an attention-stimulating environment. Firstly, novel and relevant contexts were meticulously curated to introduce the phrases in a meaningful way. Secondly, all target phrases were consistently bolded throughout the materials, prompting implicit learning through heightened visual salience. This emphasis extended to other encountered phrases, further enriching the learners' lexical phrase exposure.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n\u003ch2\u003e2.4.2. Phase II: Implementing The LIRRA-NP\u003c/h2\u003e\n\u003cp\u003eLearners, who expressed initial interest through an online registration form outlining the program's overview, objectives, and expected outcomes, were oriented via a comprehensive Zoom webinar with the program's significance and its unique neuroeducation features. Following this, to ensure appropriate participant proficiency, a placement test was administered to those who expressed further interest. Finally, all participants engaged in a pilot pre-test for baseline assessment before embarking on the 14-week program. The program unfolded throughout the participants' academic year, incorporating flexibility and focus within its structure. Each week featured one engaging session, strategically pausing during mid-year exams and intensifying during the mid-year vacation. The program, estimated to encompass an average of 50 hours of learning, aimed to maximize engagement while accommodating academic commitments.\u003c/p\u003e\n\u003cp\u003eThe material was uploaded to Google Classroom. The classroom comprised 23 sessions divided based on the recurrence of the target lexical phrases. Each session was structured to include five elements: 1) Material; a compilation of the various activities mentioned above; 2) Question(s); supported by readings and/or videos for interaction and free discussions; 3) Checkpoint; to check for the comprehension and acquisition of target lexical phrases and collect rewarding points accordingly; 4) My learning log; a log for taking note of the lexical phrases of the learners\u0026rsquo; interest; 5) A Satisfaction form; an exit ticket to follow up on the learners\u0026rsquo; motivation and satisfaction of the program.\u003c/p\u003e\n\u003cp\u003eEach session functioned as a self-paced learning module. Students were encouraged to explore the diverse \u003cem\u003ematerial\u003c/em\u003e at their convenience, engaging with the games, discussions and various activities, and culminating in a checkpoint activity to assess comprehension and progress. Reward points, awarded based on performance, and unlimited \u003cem\u003echeckpoint\u003c/em\u003e retakes fostered a supportive and engaging learning environment. Notably, learners were prompted to focus on bolded lexical phrases, read for deeper context, and log new or intriguing phrases in their personal learning logs. These logs were accessible to all participants, enriching the learning experience through peer-to-peer knowledge exchange.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Overview\u003c/h2\u003e \u003cp\u003eDemonstrating dedication, all 30 participants met the program's expectations by completing at least 90% of LIRRA-NP tasks. This included diligently engaging with the session materials (material, questions, checkpoints, and learning logs) and actively participating in revisiting key phrases through the revision games. This unwavering commitment yielded a robust dataset for analysis. Both the pre- and post-tests were administered to all participants, and their results were subjected to rigorous statistical analyses to address the core research questions outlined in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2. The Effect of the LIRRA-NP on Lexical phrases\u0026rsquo; Acquisition\u003c/h2\u003e \u003cp\u003eAddressing the first research question about LIRRA-NP's impact on lexical phrase acquisition, the analysis focused on responses from the Lexical-phrases production test. A paired-samples t-test revealed a significant increase in participant scores from pre-test (M\u0026thinsp;=\u0026thinsp;9.97, SD\u0026thinsp;=\u0026thinsp;8.79) to post-test (M\u0026thinsp;=\u0026thinsp;37.27, SD\u0026thinsp;=\u0026thinsp;10.53), \u003cem\u003et\u003c/em\u003e(29)\u0026thinsp;=\u0026thinsp;14.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. This statistically significant improvement demonstrates the program's effectiveness in enhancing the familiarity with and acquisition of target lexical phrases.\u003c/p\u003e \u003cp\u003e Further examining writing samples for evidence of lexical phrase acquisition in a free production format, the analysis revealed a marked increase in participants' acquisition and use of lexical phrases. Compared to their pre-writing, where lexical phrases were inaccurate and scarce, the post-writing showcased a significant integration of these phrases. A dependent t-test confirmed this observation, indicating a statistically significant difference between the mean number of correctly used lexical phrases in pre-writing (M\u0026thinsp;=\u0026thinsp;2.7, SD\u0026thinsp;=\u0026thinsp;2.24) and post-writing (M\u0026thinsp;=\u0026thinsp;7.33, SD\u0026thinsp;=\u0026thinsp;4.16), \u003cem\u003et\u003c/em\u003e(59)\u0026thinsp;=\u0026thinsp;8.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001. This improvement extended to error reduction, with participants demonstrating a notable decline in lexical phrase errors from pre-test (M\u0026thinsp;=\u0026thinsp;0.55, SD\u0026thinsp;=\u0026thinsp;0.8) to post-test (M\u0026thinsp;=\u0026thinsp;0.22, SD\u0026thinsp;=\u0026thinsp;0.5), \u003cem\u003et\u003c/em\u003e(59)\u0026thinsp;=\u0026thinsp;2.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003. These findings together provide compelling evidence of the LIRRA-NP\u0026rsquo;s effectiveness in enhancing participants' ability to readily and accurately acquire and integrate lexical phrases into their writing.\u003c/p\u003e \u003cp\u003eThe combined evidence from the Lexical-phrases production test and the Writing test paints a clear picture of LIRRA-NP's success. Participants demonstrated a statistically significant improvement in both their acquisition and production of target lexical phrases. Not only did their accuracy increase, but they also incorporated them more effectively into their writing, displaying heightened awareness of appropriate usage. These findings provide robust support for Hypothesis one, confirming the program's effectiveness in enhancing learners' acquisition of lexical phrases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The Effect of the LIRRA-NP on Language Automatization\u003c/h2\u003e \u003cp\u003eTo answer the second research question, the coefficient variability of the participants\u0026rsquo; reaction time (CVʀт) in the Lexical-phrases production posttest was calculated. Accordingly, the reaction time of the students\u0026rsquo; responses was recorded and statistical analyses were conducted to calculate their speed-up performance as well as to calculate the difference in the CVʀт between the pre and post Lexical-phrases production test; additionally, the correlation between RT and CVʀт were further examined.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Speed-up Performance Analysis\u003c/h2\u003e \u003cp\u003eTo assess the program's impact on the speed-up performance of the participants, a within-subject analysis was performed. A dependent t-test compared participants' mean reaction times in the pre- and post-test. The results from the pre-test (M\u0026thinsp;=\u0026thinsp;9,481,983.41, SD\u0026thinsp;=\u0026thinsp;12,124,386.09) and post-test (M\u0026thinsp;=\u0026thinsp;1,497,509.86, SD\u0026thinsp;=\u0026thinsp;644,086.54) show a significant reduction in the reaction time (in milliseconds) of the participants\u0026rsquo; performance, \u003cem\u003et\u003c/em\u003e(29)\u0026thinsp;=\u0026thinsp;3.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001. This substantial improvement strongly suggests a speed-up effect attributable to the LIRRA-NP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Automaticity Analyses\u003c/h2\u003e \u003cp\u003eInvestigating potential automaticity gains involved two parallel analyses. First, the CVʀт was calculated for both pre- and post-test data, allowing for a direct comparison of variability in response times. Second, correlation analyses were conducted to examine the relationship between RT and CVʀт at both time points.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section4\"\u003e \u003ch2\u003e3.3.2.1. Co-efficient of Variability Reduction Analysis\u003c/h2\u003e \u003cp\u003eFirst, to assess potential automaticity gains, the CVʀт was analyzed for the entire pre- and post-test data. Notably, a substantial reduction in CVʀт was observed, from 1.28 in the pre-test to 0.43 in the post-test, representing a decrease of 0.85. This significant decrease aligns with theoretical suggestions that a decrease in CVʀт, independent of mere speed-up, can indicate a shift towards automaticity in cognitive processing.\u003c/p\u003e \u003cp\u003eSecond, seeking further insight into potential automaticity gains, a within-subject analysis examined individual changes in CVʀт. While the overall reduction in CVʀт suggests potential automaticity gains, the within-subject analysis presents a seemingly contradictory picture (M\u0026thinsp;=\u0026thinsp;0.7, SD\u0026thinsp;=\u0026thinsp;0.2 pre-test, M\u0026thinsp;=\u0026thinsp;1.09, SD\u0026thinsp;=\u0026thinsp;0.349 post-test) showed no reduction of CVʀт, \u003cem\u003et\u003c/em\u003e(29)\u0026thinsp;=\u0026thinsp;5.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Accordingly, such potential inaccuracies within those aggregated values required further scrutiny. This discrepancy highlighted the importance of examining individual-level data beyond raw mean comparisons. Therefore, data cleaning was crucial to remove outliers. In this regard, reaction times exceeding three standard deviations (3SD) above the mean, corresponding to 65,000 milliseconds, were discarded. This threshold targeted excessively slow responses potentially indicative of attention lapses, thereby mitigating the influence of exceptionally long response times from individual participants on the overall analysis.\u003c/p\u003e \u003cp\u003eAnalysis of cleaned data revealed a reduction in the CVʀт for the pre-test scores (M\u0026thinsp;=\u0026thinsp;0.70, SD\u0026thinsp;=\u0026thinsp;0.205) compared to post-test scores (M\u0026thinsp;=\u0026thinsp;0.657, SD\u0026thinsp;=\u0026thinsp;0.134), \u003cem\u003et\u003c/em\u003e(29)\u0026thinsp;=\u0026thinsp;1.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12. Although this decrease did not reach statistical significance, it suggests some degree of potential automaticity gains through the LIRRA-NP. Notwithstanding, individual results diverged. While 56.67% of participants exhibited evidence of automaticity, the remaining 43.33% demonstrated performance speed-up, indicating no degree of automaticity as indicated by the CVʀт.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section4\"\u003e \u003ch2\u003e3.3.2.2. Co-efficient of Variability Correlation Analyses\u003c/h2\u003e \u003cp\u003eThe initial investigation into the correlation between CVʀт and mean RT yielded no significant positive correlation (r\u0026thinsp;=\u0026thinsp;0.066, p\u0026thinsp;=\u0026thinsp;0.73). Further examination focused on the correlation between CVʀт and overall RT for the post-test data. This analysis revealed a statistically significant, albeit weak, positive correlation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, despite a weak correlation, this positive correlation stands in contrast to the negative correlation observed in the pre-test data (\u003cem\u003er\u003c/em\u003e = -0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This shift towards a positive correlation suggests a promising impact of the LIRRA-NP on lexical phrase automaticity, indicating that speed gains might be accompanied, to some extent, by automaticity gains.\u003c/p\u003e \u003cp\u003eFurther analyses showed that if the participants were divided into relatively fast learners and relatively slow learners according to their RT, a moderate positive correlation can be detected between RT and CVʀт in the fast-students group, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.55, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027 compared to the slow-students group \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19. This is additional evidence to the more significant impact the LIRRA-NP has on automatizing L2 in relatively fast learners. Similarly, by dividing participants into relatively high-achievers and relatively low-achievers based on their performance in the proficiency test, a strong positive correlation \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0019 can be drawn between the high-achievers\u0026rsquo; CVʀт and RT. Contrarily, a correlation between the low-achievers\u0026rsquo; CVʀт and RT demonstrates a weak correlation, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eThe writing task provided further evidence of both performance speed-up and automaticity gains. Despite producing longer texts in the post-test, participants' writing speed, as measured by reaction time, significantly improved. Additionally, their entire CVʀт decreased from 0.5 in the pre-test to 0.42 in the post-test. This reduction in variability further suggests that participants achieved greater consistency and automaticity due to the LIRRA-NP.\u003c/p\u003e \u003cp\u003eIn sum, analyzing reaction times across pre- and post-tests reveals a modulating effect of the LIRRA-NP on automating L2 lexical phrases. Learners with faster RTs or higher proficiency levels exhibited stronger positive correlations between pre- and post-test CVʀт and RT values, suggesting greater automaticity gains within these subgroups. While this partially supports the initial hypothesis of LIRRA-NP's effect on automaticity, the findings also suggest that individual differences in learning speed and proficiency play a crucial role in determining the degree of automatization achieved.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.4. The Effect of the LIRRA-NP on Language Reading Proficiency\u003c/h2\u003e \u003cp\u003eTo answer the third research question regarding potential proficiency improvements, a dependent-samples paired t-test was conducted. Analyses revealed a significant increase in participants' proficiency scores from pre-test (M\u0026thinsp;=\u0026thinsp;9.7, SD\u0026thinsp;=\u0026thinsp;3.23) to post-test (M\u0026thinsp;=\u0026thinsp;11.53, SD\u0026thinsp;=\u0026thinsp;4.4), \u003cem\u003et\u003c/em\u003e(29)\u0026thinsp;=\u0026thinsp;1.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04. This significant difference supports the third hypothesis, providing evidence that LIRRA-NP can contribute to enhanced L2 proficiency.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Overview\u003c/h2\u003e \u003cp\u003eThe current study investigated the effect of the LIRRA-NP on language automatization, the acquisition of lexical phrases, and reading proficiency. The LIRRA-NP was developed in accordance with the LIRRA neuroeducational model which consists of brain adaptive features in that it adapts to the equipotentiality of the brain and takes advantage of neuroplasticity. The LIRRA-NP was postulated to bring stronger neuronal synaptic connections and to proceduralize L2 into the procedural memory. The LIRRA-NP hinged on five main features: \u003cem\u003eExposure to lexical phrases; reiterative nature of lexical phrases presentation; implicit method of presenting lexical phrases; motivational/rewarding nature an attentional-stimulating setting\u003c/em\u003e. The LIRRA-NP yielded promising results in that the participants experience a considerable degree of language automatization particularly for relatively faster and high-achieving students. Moreover, the participants\u0026rsquo; familiarity and acquisition of lexical phrases as well as their reading proficiency improved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Findings of the Study and Previous Research\u003c/h2\u003e \u003cp\u003eThe initial hypothesis postulating that LIRRA-NP would significantly enhance the L2 lexical phrase acquisition of intermediate-level young adults was firmly upheld by the study's outcomes. Analyses revealed a demonstrably positive impact on learner performance, manifesting in both the Lexical-Phrases production test, where post-test scores exceeded pre-test scores significantly, and the writing assessments, showcasing increased frequency and accuracy of lexical phrase utilization compared to pre-program compositions. These findings resonate with existing research for brain adaptability of lexical phrases (Yusuf, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and with the endeavors exploring effective methodologies for L2 lexical phrase acquisition, such as Lin's (2022) CALL tool employing formulaic sequence generation based on YouTube content, Puim\u0026egrave;ge and Peters' (2020) audiovisual documentary approach fostering incidental learning of formulaic sequences, and Kim's (2020) animated movie-based program incorporating explicit and implicit lexical phrase activities. The consistent positive outcomes across diverse pedagogical approaches, alongside the compelling data from the current study, not only solidify the efficacy of LIRRA-NP in promoting L2 lexical phrase acquisition but also underscore the pivotal role such acquisition plays in overall L2 development.\u003c/p\u003e \u003cp\u003eThe second hypothesis regarding LIRRA-NP's potential for automatizing L2 in adult learners received partial confirmation. Analyses revealed a measurable impact for speed-up performance as well as automatization, demonstrated by decreased RT and CVʀт, respectively. Interestingly, the degree of automatization varied, with some individuals exhibiting marked RT decreases while others showed primarily CVʀт reduction. These findings align with established neuroscientific and linguistic literature on automaticity (Diaz \u0026amp; McCarthy, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Frost et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Garrod \u0026amp; Pickering, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Morgan-Short et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; N\u0026auml;\u0026auml;t\u0026auml;nen, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Shtyrov \u0026amp; Pulverm\u0026uuml;ller, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Acamatsu, 2008; Dekeyser, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Elgort, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Elgort \u0026amp; Warren, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ellis \u0026amp; Schmidt, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Fukkink et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Li, 2021; Lim \u0026amp; Godfroid, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Logan, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Ma \u0026amp; Zhang, 2017; N. S. Segalowitz et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Pellicer-S\u0026aacute;nchez, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pili-Moss et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Segalowitz \u0026amp; Freed, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Snellings et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Van Gelderen et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Practice and repeated exposure to lexical phrases as the core element of LIRRA-NP appear to be key drivers of automaticity gains. This finding resonates with research by Bybee (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), N. C. Ellis (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), Gatbonton (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), and Wray (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), who emphasize the crucial role of lexical phrases in L2 automatization.\u003c/p\u003e \u003cp\u003eIt is worth noting that the results revealed differential impacts of LIRRA-NP on automaticity based on individual learning speed and proficiency level. High achievers and faster learners exhibited greater automicity gains, as evidenced by their stronger positive correlations between RT and CVʀт post-intervention. These findings echo those of Segalowitz and Segalowitz (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), who observed similar patterns in a year-long study of English-speaking French learners. They attributed greater automaticity in faster performers to potentially requiring less effort for learning. In the present study, higher achievers and faster learners' lower learning demands within the program might explain their enhanced automaticity. Conversely, low achievers and slower learners may require more focused and intensive support, as suggested by Abutalebi and Green (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Perani et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In this regard, it can be assumed that learners with lower proficiency and slower performance may require extended exposure to activate the neural mechanisms underlying language automaticity. Accordingly, increased exposure to lexical phrases through an extended program with multiple phases/levels might be beneficial for this group to fully harness LIRRA-NP's potential for automaticity gains.\u003c/p\u003e \u003cp\u003eIt is suspected that the considerable difference between the performance of some slow participants (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) in comparison with their faster counterparts (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.82) may have played a role in preventing an overall positive correlation between RT and CVʀт for all participants; which would have further validated automaticity gains. Unequal test item lengths likely impacted the SD and CVʀт calculations, inflating post-test values. Shorter items would naturally elicit faster responses compared to longer ones, further contributing to the variation in reaction times and potentially inflating the overall SD. Consequently, dividing the inflated SD by the reduced mean reaction time may erroneously increase the CVʀт, leading to misleading conclusions about individual-level automaticity gains. The varying lengths, coupled with a whole-test time limit instead of time control within individual test trials, may have introduced extraneous factors influencing response times and misleadingly suggesting greater automaticity gains.\u003c/p\u003e \u003cp\u003eThe hypothesis that LIRRA-NP would enhance L2 reading proficiency in adult learners was confirmed. Pre- and post-test scores revealed statistically significant improvement in overall reading proficiency. This finding aligns with a growing body of research highlighting the crucial role of lexical phrases in language learning and achieving native-like performance (Arnon et al., 2017; Biber et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; N. C. Ellis, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Hatami, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mohammadi \u0026amp; Enayati, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nattinger \u0026amp; DeCarrico, 1992; Rafieyan, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Participants initially reported increased self-awareness of their enhanced comprehension and the ability to process L2 texts more efficiently, leading to improved confidence in thinking, writing, and producing L2 output. These subjective experiences were corroborated by significant gains in both the reading proficiency and writing tests. These results resonate with prior studies by Arnon and Snider (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Boers et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), Chandra (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Hoang \u0026amp; Bores (2016), and Wood (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), who demonstrated the positive impact of lexical phrases on L2 learners' fluency and proficiency levels. Notably, this is supportive of the lexical approach (Lewis, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) which posits that prioritizing multi-word expressions over individual words is key to achieving native-like L2 performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Insights and Implications\u003c/h2\u003e \u003cp\u003eThis study's findings reveal several profound implications for brain-adaptive L2 learning environments. LIRRA-NP demonstrably fosters qualitative change in L2 skill development, impacting lexical phrase familiarity, reading proficiency, and automaticity. This adaptability to the human brain resonates with neuroeducation (Danesi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Netten \u0026amp; Germain, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and neuroplasticity research (Chang et al., 2017; Merzenich et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Rogowsky et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These findings highlight the promising potential of brain-adaptive programs like LIRRA-NP in optimizing L2 learning and reaching a native-like performance through brain-based approaches.\u003c/p\u003e \u003cp\u003eSecondly, the results support the improvement of second language using an implicit method of instruction. In congruence with the studies that support implicit learning on the linguistic and psycholinguistic levels (Chan \u0026amp; Leung, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Francis et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kerz et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Morgan-Short, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Yusuf, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), this study supports implicit learning for lexical phrases forms and contextual use. The findings from this study add to the growing evidence base supporting the efficacy of implicit methods, like those employed in LIRRA-NP, for effectively enhancing L2 proficiency, particularly in the domain of lexical phrases.\u003c/p\u003e \u003cp\u003eRepetitive exposure to lexical phrases, a core feature of LIRRA-NP, aligns with the established importance of practice in both language proficiency (N.S. Segalowitz, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Paradis, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and automatization (Dekeyser, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Goswami, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lee, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). While this study cannot conclusively determine whether this exposure facilitated L2 automatization through proceduralization of declarative knowledge or via direct procedural learning, it is suggested that eight repetitions within meaningful contexts were sufficient to impact procedural memory and internalize language skills. This finding emphasizes the significance of both practice and meaningful context for effective language acquisition. However, clarifying the specific mechanisms underlying L2 automatization requires further neurolinguistic investigation.\u003c/p\u003e \u003cp\u003eThis study further suggests that intrinsic motivation may play a crucial role in L2 automatization by promoting neurological restructuring. Consistent with existing literature (Goswami, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lee, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Merzenich et al., 2014), motivation and reward are hypothesized to facilitate the transformation of declarative memory into procedural memory, trigger neuroplasticity, and elevate dopamine levels, creating a rewarding learning environment. This study underlines the importance of prioritizing engaging, interactive, and adaptable learning environments and materials over traditional reward systems in language program development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Practical Implications\u003c/h2\u003e \u003cp\u003eBased on the promising results of this study, several practical implications emerge for utilizing LIRRA-NP in improving second language for intermediate adult L2 learners are worth mentioning. Its efficacy extends beyond standalone implementation, offering seamless integration into existing curricula for enriched L2 pedagogy and empowering self-directed learning trajectories. It is recommended to adapt the LIRRA-NP for diverse proficiency levels (beginner, advanced), age groups, language domains (grammar, writing), and even across languages, democratizing effective and engaging L2 acquisition opportunities. This multifaceted approach promises not only enhanced proficiency but also personalized, inclusive, and globally-accessible L2 learning experiences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Limitations\u003c/h2\u003e \u003cp\u003eDue to resource limitations, the utilization of neuroimaging or neurophysiological techniques, such as fMRI, EEG, and eye-tracking, to directly assess pre- and post-intervention changes in brain structures associated with L2 processing was constrained. Implementing these measures would have provided valuable in vivo insights into the program's impact on L2 representation and neural qualitative underlying restructure of the brain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Recommendations for Future Research\u003c/h2\u003e \u003cp\u003eFuture research directions can build upon the promising results of this study by a number of recommendations. First, exploring an intensive or an extended version of LIRRA-NP; comparing its impact on automatization with the current version could reveal the program's potential for enhanced skill acquisition, particularly among low-achievers and slow learners. Second, Utilizing neurophysiological techniques: Employing fMRI, EEG, or eye-tracking could provide in-vivo insights into the program's effects on brain structures and cognitive processes before and after intervention, elucidating the mechanisms underlying automatization. Third, developing immersive game-based versions: Creating LIRRA-NP as interactive graphic games, clue-based challenges, or virtual reality experiences could further enhance learner engagement and motivation, potentially increasing immersion and yielding further improvements in proficiency and automaticity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e4.7. Conclusion\u003c/h2\u003e \u003cp\u003eThis study examined the efficacy of the LIRRA-NP in fostering language automatization, lexical phrase acquisition, and reading proficiency enhancement. Grounded in the LIRRA neuroeducational model, the LIRRA-NP leverages brain equipotentiality and neuroplasticity through adaptive features. Notably, the LIRRA-NP yielded promising results, particularly for faster and higher-achieving learners, demonstrating considerable gains in language automatization, and significant gains in lexical phrase acquisition and reading proficiency. The findings from this study open up the possibility that a LIRRA model-based neuroeducation program, such as the LIRRA-NP, could serve as a transformative tool for second language acquisition in intermediate-level young adults. By acknowledging the brain's crucial role in language processing and incorporating evidence-based practices, LIRRA-NP has the potential to revolutionize L2 teaching.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe study ethics were followed and the study was approved by Ain Shams University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbutalebi, J., \u0026amp; Green, D. (2007). Bilingual language production: The neurocognition of language representation and control. \u003cem\u003eJournal of Neurolinguistics, 20\u003c/em\u003e(3), 242\u0026ndash;275. https://doi.org/10.1016/j.jneuroling.2006.10.003\u003c/li\u003e\n\u003cli\u003eAkamatsu, N. (2008). 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Department of English and Scientific Methods, The German University in Cairo.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Level K1 refers to the 1st 1,000 Most Frequent Words of English (words 1 to 1000) Level K2 refers to the 2nd 1,000 Most Frequent Words of English (words 1001 to 2000)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Ain Shams University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"neuroeducation, second language acquisition, automaticity, implicit learning, neuroplasticity, lexical phrases","lastPublishedDoi":"10.21203/rs.3.rs-4337380/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4337380/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlmost without exception, adult second language (L2) learners fail to acquire an L2 to a comparable level of proficiency as their first language (L1). In part, traditional teaching approaches fail to address this issue or adapt to the equipotential brain. In light of this, this research attempted to explore language acquisition from a neuroeducation perspective. Accordingly, a neuroeducation program was developed which draws on concepts from neuroscience. The neuroeducation program was developed based on the LIRRA neuroeducational model which was proposed to automatize L2 and improve language acquisition for late learners. The program hinged on reiterative implicit exposure of lexical phrases in an attention- and reward- stimulating setting. This was to allow for procedural memory processing (like in L1) where L2 becomes automatized. In this regard, in a pre-post design, 30 English intermediate-level undergraduates were enrolled to the study. The program was a computerized online one encompassing 23 sessions and was implemented using a variety of engaging techniques such as games, riddles, and interactive readings. The outcomes were measured using a pre/post-test comprising three sections: a lexical-phrases production test, a reading proficiency test, and a writing test. The results revealed that the students showed a significant speed-up performance and considerable automaticity gains. Additionally, students showed a significant improvement in their acquisition of L2 lexical phrases and their reading proficiency. This research holds a promising future for the implementation of neuroeducation programs in language learning and for self-directed learning.\u003c/p\u003e","manuscriptTitle":"The Effect of a Neuroeducation Program on L2 Automatization and Acquisition for Intermediate Young Adult Learners","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 12:28:27","doi":"10.21203/rs.3.rs-4337380/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"329a0adb-bc48-4d55-ba18-db6fc46ae687","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31253243,"name":"Educational Psychology"},{"id":31253244,"name":"Linguistics"}],"tags":[],"updatedAt":"2024-05-02T12:28:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 12:28:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4337380","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4337380","identity":"rs-4337380","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}