Exploring the Relationship of Reading Fluency and Accuracy in L2 Learning: Insights from a Reading Assistant Software

Abstract

L2 reading fluency and accuracy are crucial aspects of second language (L2) acquisition, as they directly influence cognitive processing and overall reading comprehension. Developing these skills enables learners to engage with texts efficiently and build confidence in their reading abilities. This study uses reading software to examine the relationship between reading fluency and accuracy in the L2 learning context. Two primary research questions were addressed: the correlation between reading fluency and accuracy and potential gender-based differences in reading performance metrics, including reading time, comprehension, and developmental progress. Pearson’s correlation analysis revealed a strong, positive relationship between fluency and accuracy, indicating that improvements in one skill are associated with enhancements in the other. Additionally, fluency and accuracy were positively correlated with reading comprehension and developmental measures, underscoring the interconnected nature of these skills. Gender comparisons, analyzed through independent t-tests, indicated no significant differences in reading software engagement or performance. This suggests the software provides equitable support across genders, enabling consistent skill development in fluency, accuracy, and comprehension. The findings highlight the critical role of technology in facilitating balanced reading instruction and offer insights into its potential to address diverse learner needs effectively.

1. Introduction

In today’s information-driven era, strong L2 reading skills are vital for learners to efficiently process and comprehend vast amounts of content. Reading is the primary means of acquiring new knowledge, making it a necessary skill for second language learners. However, achieving reading proficiency requires more than decoding words; it demands both fluency and accuracy to ensure smooth and meaningful comprehension, which is a complex process. Second-language reading encompasses various cognitive processes to decipher printed words and construct meaning from the decoded information (Gorsuch & Taguchi, 2008; Park, 2022; Yasemin, 2020). These processes encompass lower-level tasks like word recognition, meaning proposition encoding, syntactic parsing, and working memory activation. At the same time, elevated processing entails making inferences, employing strategic methods, interpreting, and utilizing background information (Gorsuch & Taguchi, 2008; Grabe, 2009; Iser, 2022; Koda, 2005; Smith et al., 2021). Attaining proficient reading necessitates skillfully navigating through this complex set of abilities. To date, the initial working definitions of fluency and accuracy continue to be employed to assess overall language proficiency: Accuracy indicates language use that closely aligns with the target language and is free from errors (Duijm et al., 2018; Michel, 2017). Conversely, fluency encompasses the seamless, effortless, and articulate delivery of speech, characterized by minimal pauses, hesitations, or revisions (Nation, 2009; Sidek & Rahim, 2015). Over the last two decades, an increasing amount of research in the field of second language acquisition (SLA) has employed measures related to reading fluency and accuracy to assess L2 reading comprehension (see Baker et al., 2015; Kim et al., 2014; Papadopoulos et al., 2016; Paul & Karmarkar, 2022; Rasinski et al., 2016).

Regarding reading fluency, O’Grady (2023) recently analyzed rater scores on oral fluency using analytic rating scales featuring descriptors for speech production to examine the correlation among lexical complexity, speech fluency, and accuracy. The researcher found that the raters’ fluency scores reflected the ability to express the second language quickly and smoothly while maintaining coherence, correcting errors, and using diverse vocabulary. This shows an interdependence between fluency and accuracy in oral production (Skehan, 2009). While O’Grady (2023) analyzed rater scores using analytic rating scales by human assessment focusing on the descriptors for oral production and examining the correlation among lexical complexity, speech fluency, and accuracy, further research is warranted to investigate the criteria employed by automated reading scoring machines, also known as reading assistant software (RAS), in evaluating reading fluency and accuracy. This approach could provide valuable insights into the reliability and objectivity of machine rating compared to human assessments (Ostrand & Gunstad, 2021). Exploring the intricacies of how technology assesses language proficiency can make a meaningful contribution to the ongoing conversation surrounding language evaluation methodologies (Handley & Wang, 2023; Wang & Wang, 2022). Within this broader field, a research gap exists, which is especially pertinent given the increasing dependence on technology in language assessment. One underexplored area is the correlation between reading fluency and reading accuracy, as identified by RAS. Addressing this gap can provide valuable insights into the effectiveness and reliability of technology-mediated language assessment. By understanding this relationship, educators, researchers, and learners can make informed decisions to enhance reading proficiency and overall language acquisition.

1.1. Literature Review

1.1.1. Reading Fluency

Reading fluency in second language (L2) learners pertains to their capacity to read text effortlessly, quickly, and with appropriate expression or intonation (Perfetti, 1985; Schreiber, 1980). This includes smooth and effective decoding of words, understanding of texts, and the skill to sustain an appropriate reading pace (Koda, 2005; Nation, 2009). The speed of reading, often indicated by the rate of word recognition and considered a measure of reading fluency, refers to the total number of words a person can recognize per minute (Barzegar & Fazilatfar, 2019). Scholars propose that proficient readers typically read in their first language at approximately 250–300 words per minute, with approximately 90 fixations per 100 words (Carver, 1982). For second language learners reading a text with familiar words, the recommended speed is approximately 250 words per minute for the intermediate level (Nation, 2009). However, some studies have demonstrated that reading in a second/foreign language tends to be slower than reading in the first language. Several studies (e.g., Dombey, 2009; Rogde et al., 2019; Van Gelderen et al., 2004) have delved into the elements of comprehension within reading, categorizing them into linguistic and reading comprehension. Linguistic comprehension pertains to utilizing vocabulary knowledge to comprehend orally presented text, while reading comprehension involves leveraging vocabulary knowledge based on written information perceived visually. Scholars have proposed that comprehension is an indication factor of fluency in silent reading, as noted by researchers (see Grabe, 2009; Nation, 2009; Vu et al., 2024; Yamashita & Ichikawa, 2010). Achieving both rapid reading and simultaneous comprehension is crucial for a reader to be considered fluent. Many instructional approaches aimed at enhancing reading fluency prompt students to report their reading speed in words per minute while also responding to queries about their comprehension, offering insights into learners’ progress in both reading speed and comprehension levels (Barzegar & Fazilatfar, 2019; Quinn et al., 2007). Some scholars (e.g., Dombey, 2009; Nation, 2009; Sidek & Rahim, 2015; Smith et al., 2021; Van Gelderen et al., 2004) assert that learners can enhance reading fluency through practice and exposure to various texts in the target language, decoding skills by recognizing and processing words quickly, or using strategies such as prediction, summarization, and inference which propel learners to engage with texts, promoting fluency actively.

1.1.2. Reading Accuracy

Reading accuracy in L2 learners refers to the ability to comprehend and interpret written statements with high precision and correctness. It involves the accurate recognition and pronunciation of words, comprehension of sentence structures, and overall understanding of the written content. Several factors enhance reading accuracy, including vocabulary knowledge, grammatical competence, phonetic awareness, and contextual understanding (Borràs & Llanes, 2020; Grabe, 2009; Kitjaroonchai & Maywald, 2024). Traditionally, assessing oral reading accuracy involves individuals reading aloud a graded list of words. This will help provide a comprehensive view of an individual’s foundational reading skills and support tailored to their specific needs. According to Fuchs et al. (2001) and Share (2008), these lists are structured by length, typically beginning with short, high-frequency words and advancing to longer and less frequent words. The correct pronunciation is emphasized, for it reflects the reader’s ability to decode and articulate words accurately, relying on well-defined phonological representations (Davelaar et al., 1978). Some scholars, such as Michel (2017) and Pallotti (2009), posited that accuracy signifies attaining a native-like language characterized by error-free output. This perspective quantifies the degree to which utterances deviate from the norm of the target language. Noteworthy is the application of the word error rate (WER) method in previous studies (e.g., Duijm et al., 2018; Housen et al., 2012; Polio, 1997), which involves calculating the ratio of the total number of errors (substitutions, deletions, and insertions) in the recognized words to the entire set of utterances produced.

1.1.3. Reading Skills Differences Between Male and Female EFL Learners

The literature on reading skills among male and female ESL/EFL learners highlights notable gender differences in reading comprehension and strategy use. Studies have consistently shown that female learners tend to outperform males. For example, Khaghaninejad and Arefinejad (2015) found that female Iranian EFL learners excelled in concept mapping and text interpretation, likely due to more effective cognitive strategies. Similarly, Ceran (2015) observed higher reading comprehension scores among female students, while Masroor (2022) noted superior strategy use among female Pakistani ESL undergraduates, enhancing their comprehension. Jaiswal (2018) further emphasized that proficient readers, often female, demonstrate greater metacognitive strategy awareness. However, Liu (2014) emphasized that while gender differences in reading comprehension exist, these differences are shaped by a range of complex and interconnected factors. The study suggested that implementing tailored instructional strategies could benefit both genders. In general, females tend to demonstrate stronger reading abilities and more strategic awareness. However, McGeown et al. (2021) argued that when male and female students are provided with equal levels of motivation and support in their reading activities, gender-based disparities in reading performance diminish or disappear altogether. In other words, when factors such as teaching methods, motivation, and assessment design are controlled or balanced, male and female students often achieve similar reading comprehension outcomes.

1.1.4. The Reading Assistant Software (RAS)

The RAS, a crucial element of the Fast ForWord program created by Carnegie Learning, functions as a guided online reading tool. This software identifies inserted speeches and provides instant corrective feedback to individuals as they participate in oral reading, enabling self-correction of pronunciation errors (Bhatt et al., 2020). Recent research findings (Faisol et al., 2021; Li, 2020; Saito et al., 2016) have indicated that incorporating the RAS into second language (L2) classrooms enhances learners’ vocabulary acquisition, reading fluency, comprehension, and language prosody. The RAS tool offers immediate assistance, helping individuals decipher words and enhancing their reading fluency (Kilag et al., 2023). For students facing challenges in reading, which might lead to frustration and discouragement, this software can play a crucial role in fostering confidence and independence in their reading skills. Based on Kilag et al. (2023), the Fast ForWord intervention, created to address auditory processing and phonological awareness, has shown notable enhancements in reading skills. The adaptability of these programs, customizing instruction based on individual needs and progress, results in personalized learning experiences, enhancing the effectiveness and engagement of the intervention. In line with the language development of young learners, the program steers them through reading passages with the guidance of a skilled speaker. Like a parent reading to a child, the RA software supervises and corrects mispronunciations in real time during comprehension assessments (see some characteristics of RAS in

Table 1. Characteristics of RAS.

Speech Recognition Technology	Utilizes advanced speech recognition to provide real-time feedback on pronunciation and fluency.
Immediate Feedback	Offers instant correction and guidance to help students improve reading accuracy and fluency.
Progress Tracking	Tracks individual progress, offering detailed analytics on areas such as fluency, accuracy, and comprehension.
Interactive Reading Practice	Engages learners by allowing them to read passages aloud and interact with the text.
Differentiated Support	Provides personalized scaffolding and support tailored to each student’s reading level.
Comprehension Checks	Includes questions and activities to ensure understanding of the text being read.
Content Variety	Features a diverse library of age-appropriate and interest-based reading materials.
Self-Paced Learning	Enables students to practice independently, allowing for flexibility in learning.
Teacher Analytics Dashboard	Offers a dashboard for teachers to monitor student progress, identify challenges, and adjust instruction.

). Subsequently, learners imitate the speaker’s intonation, rhythm, and prosody (Keller et al., 2018; Mahdi & Al Khateeb, 2019), which assists them in increasing their reading fluency. Expanding on this groundwork, Bhatt et al. (2020) underscore the software’s ability to automatically compute the number of words read accurately per minute. This functionality allows educators to evaluate learners’ reading proficiency levels and identify specific areas of strength and weakness.

In L2 learning, the RAS has been a pivotal tool for over two decades, devoted to advancing reading fluency and comprehension. Despite its extensive use, a notable research gap exists concerning the specific correlation between reading fluency and accuracy in L2 learning classrooms. While the software is recognized for its efficacy in enhancing fluency and comprehension (Faisol et al., 2021; Keller et al., 2018; Mahdi & Al Khateeb, 2019; Li, 2020; Saito et al., 2016), a more focused investigation is required to elucidate the intricate relationship between fluency and accuracy.

This research seeks to comprehensively understand the interplay between reading fluency and accuracy in the L2 learning context facilitated by RAS. The following research objectives guide the present study: (1) What is the relationship between reading fluency and reading accuracy measured by the reading software? (2) Are there significant differences in reading time per week, selection of readings per week, percentage of reading development level, reading comprehension, reading accuracy per minute, and fluency per minute based on gender?

2. Materials and Methods

2.1. Research Setting and Participants

This quantitative study was conducted at an international university in Thailand. The choice of this university was deliberate, as it has a well-established ESL program designed to enhance students’ English language skills. The university’s ESL program is well-regarded for its focus on reading fluency and accuracy, making it a suitable setting for investigating the impact of reading software on English reading comprehension. Their English proficiency levels range from struggling (CEFR A1-A2) to proficient (CEFR B1-B2), based on the RA software’s detection when they first began using the program. The researchers collected data from RAS users who actively engaged with the software as part of their twelve-week coursework requirements. The collected data, stored in a dedicated database, were analyzed to draw insights into the effectiveness of the RAS. The participants in this study comprised six hundred and four students (n = 604) from various Asian countries, including Cambodia, China, Indonesia, Malaysia, Myanmar, Thailand, and Vietnam (see

Table 2. Participants in the study.

Participant	Country
Nationality	Cambodia	China	Indonesia	Malaysia	Myanmar	Thailand	Vietnam
	n = 75	n = 158	n = 25	n = 22	n = 12	n = 280	n = 32
Gender	Female			Male
	n = 365			n = 239

). These students were enrolled in diverse English reading skills courses within the university’s ESL program.

2.2. Instrument

The research instrument involves leveraging existing data from RAS to assess users’ English reading fluency (the rate of word recognition of the total number of words the reading software can recognize per minute) and accuracy (reading accuracy rate is calculated based on the percentage of correctly read words by dividing the number of correct words by the total number of words and multiplying the result by 100) (Duijm et al., 2018; Housen et al., 2012; Polio, 1997). The reading software captures and records users’ interactions, providing rich data on reading behaviors (see characteristics of the reading software) below.

In this context, the instrument is the pre-existing infrastructure of RAS itself, automatically rating users’ performance during oral reading. This automated system is a valuable tool for analyzing and evaluating the software’s effectiveness in enhancing English reading skills. The utilization of the reading software data streamlines the research process, allowing for a comprehensive exploration of users’ fluency and accuracy levels. In this study, the participants were encouraged to log in and practice oral reading for at least 150 min per week at their convenience.

Table 3. Example of the software recording users’ data.

Students	MPW	SPW	BRL	RL	PDL	RCP	APM	FPM
1	160.00	2.00	3.00	0.90	50.00	73.00	41.00	48.00
2	164.00	1.70	3.00	0.90	8.00	41.00	41.00	45.00
3	251.40	3.80	3.00	5.30	58.00	73.00	124.00	127.00
4	174.00	0.70	3.00	4.10	25.00	70.00	67.00	75.00
5	410.80	13.90	3.00	6.00	33.00	62.00	91.00	96.00
6	238.60	3.50	3.00	4.20	30.00	62.00	94.00	95.00
7	213.00	4.90	3.00	4.50	17.00	46.00	104.00	105.00
8	149.90	4.30	3.00	1.00	7.00	37.00	20.00	27.00
9	181.00	1.30	3.00	2.20	50.00	72.00	67.00	74.00
10	131.90	1.00	2.00	6.70	50.00	76.00	109.00	117.00
11	146.50	1.50	1.00	3.00	30.00	28.00	52.00	59.00
12	155.20	5.00	3.00	5.00	40.00	26.00	48.00	53.00
13	167.00	2.00	3.00	5.30	25.00	53.00	38.00	42.00
14	152.10	6.40	1.00	3.10	42.00	69.00	101.00	105.00
15	151.80	2.50	3.00	4.20	71.00	80.00	128.00	129.00
16	114.80	0.90	3.00	1.80	13.00	50.00	31.00	41.00
17	197.00	2.20	3.00	2.30	14.00	42.00	55.00	61.00
.	264.60	5.70	3.00	7.90	64.00	79.00	88.00	127.00
.	137.70	4.90	3.00	3.10	24.00	53.00	47.00	55.00
.	261.10	3.80	3.00	3.20	37.00	67.00	56.00	58.00
604	189.80	1.50	3.00	4.00	90.00	91.00	63.00	65.00
Average	183.90	3.41	2.79	3.82	36.82	60.82	58.35	63.86

Note: MPW—Minute per Week; SPW—Selection per Week; BRL—Beginning Reading Level; RL—Reading Level; PDL—Percentage of Development Level; RCP—Reading Comprehension; APM—Accuracy per Minute; FPM—Fluency per Minute.

shows how the reading software records users’ data.

In the Reading Assistant software, the program monitors user login activity and tracks engagement by recording the average usage in minutes per week (MPW) and selections per week (SPW), which refers to the average number of books completed by students each week. The beginning reading level (BRL) is determined by the course instructor based on students’ test performance prior to using the program. The reading level (RL) represents the proficiency level attained during program use. The percentage of development level (PDL) represents students’ reading progress relative to their initial proficiency at the start of the program. This is assessed based on the percentage of improvement and level advancement in reading comprehension (RCP), which is measured through students’ quiz scores on reading passages completed after each story. Additionally, accuracy per minute (APM) and fluency per minute (FPM) are evaluated by the software, which calculates the average reading speed and accuracy per minute based on word counts detected during reading sessions.

2.3. Data Analysis

IBM SPSS Version 26 was used to analyze the correlation between reading fluency and reading accuracy. Pearson’s correlation coefficient (r) was applied due to its effectiveness in measuring the strength and direction of a linear relationship between two variables. Furthermore, to investigate potential gender-based differences in reading comprehension achievement, the independent samples t-test was employed.

3. Results

To address the first research question, ‘What is the relationship between reading fluency and reading accuracy as measured by the reading software?’, the researcher utilized Pearson’s Correlation Coefficient (Pearson’s r) to examine the data. The results of this analysis are presented in

Table 4. Correlation between variables as measured by RA.

	Descriptive Statistics			Correlations
Variables	N	M	SD	MPW	SPW	BRL	RL	PDL	RCP	APM	FPM
MPW	604	183.90	110.59	1	0.606 **	−0.162 **	0.235 **	−0.018	0.084 *	0.211 **	0.224 **
SPW	604	3.41	2.34		1	−0.136 **	0.079	0.068	0.141 **	0.130 **	0.124 **
BRL	604	2.80	0.94			1	0.176 **	0.016	0.018	0.051	0.046
RL	604	3.82	1.85				1	0.240 **	0.142 **	0.629 **	0.627 **
PDL	604	36.82	20.95					1	0.761 **	0.305 **	0.293 **
RCP	604	60.81	15.31						1	0.302 **	0.286 **
APM	604	58.34	22.54							1	0.984 **
FPM	604	63.86	22.84								1

**. Correlation is significant at the 0.01 level (two-tailed) *. Correlation is significant at the 0.05 level (two-tailed). Note: MPW—Minute per Week; SPW—Selection per Week; BRL—Beginning Reading Level; RL—Reading Level; PDL—Percentage of Development Level; RCP—Reading Comprehension; APM—Accuracy per Minute; FPM—Fluency per Minute.

.

The results suggest a strong, positive relationship between reading fluency and reading accuracy (r (604) = 0.984, p < 0.01), indicating that as students improve their fluency, their reading accuracy also increases significantly. Additionally, both fluency and accuracy were positively associated with other important measures such as reading comprehension, reading level, and development level, emphasizing the interconnectedness of these reading skills. Specifically, higher fluency and accuracy were linked to better reading comprehension (r = 0.286 and r = 0.302, respectively), highlighting the importance of developing both skills to improve reading performance.

These results shed light on the essential elements of reading performance, highlighting the crucial connection between fluency and accuracy and their broader impact on reading comprehension and development.

To address the second research question, ‘Are there significant differences in reading time per week, selection of readings per week, percentage of reading development level, reading comprehension, reading accuracy per minute, and fluency per minute based on gender?’, a t-test was used. The results of this analysis are presented in

Table 5. Independent Samples t-test as measured by the reading software.

	Descriptive Statistics			Independent Samples t-Test
Group	N	MPW		SPW		BRL		RL		PDL		RCP		APM		FPM
		M	SD	M	SD	M	SD	M	SD	M	SD	M	SD	M	SD	M	SD
Male	239	179.5	114.9	3.28	2.38	2.71	0.84	3.73	1.68	38.16	21.57	61.02	16.09	57.41	21.88	63.14	22.01
Female	365	186.8	107.7	3.49	2.31	2.83	1.00	3.87	1.96	35.94	20.52	60.68	14.79	58.96	22.97	64.33	23.39
	t	0.798		1.087		1.402		0.863		−1.273		−0.269		0.827		0.627
	df	602		602		602		602		602		602		602		602
	p	0.425		0.277		0.161		0.388		0.202		0.788		0.409		0.531

Note: MPW—Minute per Week; SPW—Selection per Week; BRL—Beginning Reading Level; RL—Reading Level; PDL—Percentage of Development Level; RCP—Reading Comprehension; APM—Accuracy per Minute; FPM—Fluency per Minute.

.

The analysis revealed no statistically significant differences between male and female students across all measures of RA usage, including minutes per week (MPW), selections per week (SPW), beginning reading level (BRL), reading level (RL), percentage of developmental improvement (PDL), reading comprehension (RCP), accuracy per minute (APM), and fluency per minute (FPM). This indicates that both male and female students exhibit similar engagement patterns with the RA software, suggesting that gender does not significantly influence the time spent using the software, the progression in reading levels, or their overall reading performance. Despite minor variations in the means for each measure, these differences were not statistically significant, as reflected by the p-values (all p > 0.05).

The findings are essential for understanding the equitable impact of RA software on students, irrespective of gender. They suggest that the software provides consistent support for both male and female students regarding skill development, comprehension, and fluency. This consistency may reflect the RA software’s effectiveness in addressing learners’ diverse needs without bias.

4. Discussion

The study’s findings provide significant insights into the correlation between reading fluency and accuracy among second-language learners, as measured by the reading software. The strong positive relationship between fluency and accuracy supports the theoretical and empirical work by Nation (2009) and Grabe (2009), emphasizing that fluency and accuracy are interdependent skills essential for developing reading proficiency. This correlation aligns with prior research highlighting that fluent readers are often accurate, as they decode words effortlessly while maintaining an understanding of the text (Perfetti, 1985; Rasinski et al., 2016).

Reading fluency, the ability to read text effortlessly, quickly, and with appropriate intonation, forms the foundation for accuracy, characterized by error-free reading and precise text interpretation (Barzegar & Fazilatfar, 2019). The findings of this study, which indicate a significant correlation between these two skills, align with earlier research by Michel (2017), who posited that fluency aids in reducing cognitive load, thereby improving accuracy. As readers become fluent, they allocate fewer cognitive resources to word decoding, allowing for more focus on understanding text structure and meaning, ultimately leading to greater accuracy (Koda, 2005). Further support for this relationship comes from research by Rasinski et al. (2016), which demonstrated that developing reading fluency in both L1 and L2 contexts positively impacts accuracy. Fluent readers are better equipped to recognize patterns in text and anticipate word sequences, leading to fewer pronunciation errors and improved comprehension. This process of automaticity, described by Carver (1982), is critical in second-language acquisition, as it allows learners to transition from decoding individual words to grasping the broader meaning of sentences and paragraphs.

The data from this study also emphasize that fluency and accuracy are not isolated skills but form a feedback loop where improvements in one reinforce the other. For instance, as learners read fluently, they develop a firmer grasp of phonetics and word recognition, which enhances accuracy. Conversely, as accuracy improves, learners experience fewer disruptions in reading flow, contributing to greater fluency. This interplay aligns with Van Gelderen et al. (2004) findings, which identified mutual reinforcement between linguistic and reading comprehension skills.

The integration of the reading software in this study offers compelling evidence for the role of technology in advancing fluency and accuracy. It provides instant corrective feedback, enabling learners to self-correct errors in real time, a feature that aligns with the principles of scaffolding in second-language learning (Faisol et al., 2021). By allowing learners to identify and address mistakes immediately, the software fosters greater metacognitive awareness, which is crucial for improving reading skills. The findings resonate with Keller et al. (2018), who observed that digital reading tools enhance learners’ ability to mimic native-like intonation and rhythm, thereby improving fluency. The positive correlation between fluency and comprehension supports the claim that technology-assisted tools can provide an immersive environment where learners can practice reading at their own pace while receiving personalized guidance. This aligns with the findings of Mahdi and Al Khateeb (2019), who highlighted the importance of adaptive technology in addressing individual learner needs.

Additionally, the ability of the reading software to calculate fluency and accuracy metrics in real time provides educators with valuable data for monitoring progress and tailoring instruction. This feature is consistent with the findings of Bhatt et al. (2020), who emphasized the importance of automated assessment tools in providing actionable insights for educators. By leveraging such tools, teachers can identify specific areas where students struggle, such as decoding complex vocabulary or maintaining a steady reading pace, and design targeted interventions to address these challenges.

The study’s findings also underscore the interconnectedness of fluency, accuracy, and comprehension. As demonstrated by the positive correlations between these variables, learners who excel in fluency and accuracy are better equipped to comprehend complex texts. This aligns with research by Sidek and Rahim (2015), who noted that vocabulary knowledge, a key component of fluency, directly influences reading comprehension. Furthermore, reading fluently and accurately enables learners to engage in higher-order cognitive processes, such as making inferences and synthesizing information, which is essential for deep comprehension (Grabe, 2009).

The correlation between fluency and accuracy with other measures, such as reading and development levels, reinforces the argument that these skills are foundational for broader literacy development. As Park (2022) noted, improving fluency and accuracy provides learners with the tools to approach text confidently, fostering a positive attitude toward reading and encouraging sustained engagement with the language. This finding has practical implications for curriculum design, highlighting the need to integrate fluency and accuracy training into comprehensive literacy programs.

Further findings revealed no significant differences in reading variables, such as reading level and percentage of developmental improvement, between male and female students. This result contradicts the broader literature, highlighting notable gender differences in reading comprehension and strategy use among ESL/EFL learners. For instance, studies have consistently shown that female learners outperform males (Ceran, 2015; Khaghaninejad & Arefinejad, 2015; Masroor, 2022). The lack of significant gender differences observed in the current study could be attributed to the nature of the reading task, a required component of the students’ English course. This compulsory nature of the task reduced the potential for gender-based differences to emerge, as all students were equally motivated to complete the activity. As McGeown et al. (2021) claimed that when both male and female students are equally motivated and encouraged in their reading practices, gender-based differences in reading performance are minimized or non-existent. This finding highlights the importance of considering task-specific and contextual factors when examining gender differences in reading performance. Future studies could explore this issue using qualitative methods, such as interviews or focus groups, to gain deeper insights into the mechanisms underlying the absence of significant gender differences in this context. Such investigations could better understand how task design and instructional strategies influence reading outcomes across genders.

4.1. Implications

4.1.1. Implications for Future Research

While this study provides valuable insights, it is essential to acknowledge its limitations. The focus on a single technology-assisted tool, RAS, may limit the generalizability of the findings to other educational contexts or tools. Future research should explore the efficacy of alternative digital platforms in promoting reading fluency and accuracy. Additionally, the unanswered second research question regarding variations in reading time, selection, and comprehension based on proficiency levels presents an opportunity for further investigation. Exploring these factors could provide a more nuanced understanding of how different learner profiles benefit from technology-assisted reading. Another area for future research is the longitudinal impact of fluency and accuracy development on overall language proficiency. While this study highlights short-term correlations, examining how these skills influence long-term academic and professional outcomes could provide deeper insights into their significance in second-language learning.

4.1.2. Implications for Teaching

The findings of this study underscore the importance of fluency and accuracy in reading development and the potential of technology, such as reading software, to enhance these skills. The strong correlation between fluency and accuracy suggests the need for balanced instruction that integrates both skills effectively. Teachers can incorporate repeated reading exercises, where students practice texts multiple times to improve speed and error-free decoding, ensuring proper intonation and rhythm. Additionally, combining fluency tasks with comprehension activities, such as answering questions after reading aloud, helps students approach reading holistically, emphasizing detail and meaning. Leveraging authentic texts related to learners’ interests can make reading more engaging and foster a positive attitude toward the activity.

Digital tools like reading software provide immediate feedback and foster self-regulation and metacognitive skills critical for language learning. Teachers should integrate such tools into lesson plans, for example, by assigning reading tools to regular sessions as an independent practice to complement classroom instruction. The analytics from these tools can inform targeted teaching strategies, such as designing phoneme-specific exercises for students struggling with pronunciation. This personalized approach aligns with differentiated instruction, effectively allowing teachers to meet individual learner needs. Professional development workshops focused on technology-assisted reading instruction are essential to equip educators with the skills to use digital tools effectively and interpret the data to enhance their teaching practices.

5. Conclusions

This study highlights the strong positive correlation between reading fluency and accuracy among second-language learners, underscoring their interdependent nature in developing reading proficiency. The findings reveal that improvements in fluency and accuracy enhance not only each other but also reading comprehension and overall literacy development. Using the reading software demonstrates the potential of technology to support these skills through immediate feedback and personalized guidance. These results emphasize integrating fluency and accuracy-focused activities into teaching practices and leveraging digital tools to create tailored and effective learning experiences. Future research should explore variations based on learner profiles and the long-term impact of such interventions on language acquisition.