The Effect of Audio-Assisted Reading on Incidental Learning of Present Perfect by EFL Learners
-
Musa Nushi
and
Peyman Jahanbin
Abstract
This study applies audio-assisted reading (AAR), a technique that provides multimedia input, to encourage incidental learning of the present perfect tense. The decision to choose this tense was informed by previous studies, which have shown that its multifunctional nature presents EFL learners with considerable challenges. Thirty-four Iranian EFL learners recruited through convenience sampling participated in this eight-week-long research. The learners were assigned to the experimental and control groups. The experimental group received the AAR instruction while the control group received reading-only instruction. The analysis of the data collected through pre-test, post-test, and delayed post-test indicated that the learners not only learned the target grammar structure significantly better through AAR, but also retained it longer. The findings of the study and the implications for language teaching and learning are discussed.
1 Introduction
The teaching of grammar in second language (L2) instruction has been a topic of ongoing debate (Larsen-Freeman, 2015; Pawlak, 2021). Despite the decline in emphasis on grammar in L2 pedagogy following the advent of communicative language teaching (Richards, 2006), scholars (e.g., Ellis, 2008; Hinkel, 2017; Mystkowska-Wiertelak & Pawlak, 2011; Roehr & Gánem-Gutiérrez, 2009) have persistently highlighted the importance of grammar instruction in L2 learning, maintaining that a deliberate emphasis on the grammatical aspects of L2 can contribute significantly to learners attaining a heightened level of language proficiency. Although the debate may not be as controversial today and recent years have witnessed a paradigm shift from whether grammar should be taught explicitly or implicitly to how best to balance these approaches within more integrated frameworks such as task-based language teaching (Borro, 2021a) and even based on individual differences (Taylor, 2023), there remain significant discussions, particularly where English is taught as a foreign language, on how grammar should ideally be taught (Ellis, 2014; Sato & Oyanedel, 2019). In such input-poor contexts, a prevailing instructional method involves explicitly teaching grammar, with a strong focus on distinct grammatical features (Klapper & Rees, 2003; Pawlak, 2021, Roehr-Brackin, 2024, however). Even though language learners’ grasp of grammatical rules and terminology may be enhanced by this approach, their ability to implement these principles in real-world linguistic contexts might not see improvement (Al-Mekhlafi and Nagaratnam, 2011; Borro, 2021a). Moreover, there is a belief that acquiring grammar through explicit instruction will require a substantial investment of time and cognitive effort from learners (Murphy & Hastings, 2006). Petraki and Hill (2011) add that many teachers may not possess an adequate understanding of grammar or may lack the ability to explain grammar appropriately, even though they deem grammar instruction vital and effective. Therefore, explicit grammar teaching may not be what would work for learners and teachers.
Acquiring grammar through reading can be an alternative, given the fact that the approach is consistent with those that eschew discrete sentence-level grammar instruction and focus on the target language structures in the context to create an understanding of the form-meaning relationship (e.g., Celce-Murcia & Olshtain, 2000; Nassaji & Fotos, 2004). Endorsing that idea, Celce-Murcia (2002) states “all naturalistic learning of first and second languages takes place in context and at the level of discourse rather than the abstract sentence level” (p. 119). Additionally, Aka (2020) argues that reading is a well-recognized incidental learning method and has gained recognition as an efficient method for promoting language development in learners. Research has indicated that reading results in incidental gains in vocabulary (Chen, 2021; Teng, 2016; Vu & Peters, 2022), formulaic sequences (Borro, 2021b), reading speed and comprehension (McLean & Rouault, 2017; Suk, 2017), and grammatical competence (Aka, 2020; Altakhaineh & Ibrahim, 2019).
Given the importance of reading in language acquisition, educators are frequently recommended to promote students’ engagement with diverse reading techniques both within and beyond the classroom (Grabe & Stoller, 2020). Scholars (e.g., Chang & Millett, 2014, 2015) have suggested that combining visual and auditory stimuli enhances understanding and results in more substantial educational benefits; it also provides a positive experience for learners (Tragant & Vallbona, 2018; Tragant, Muñoz, & Spada, 2016). Such arguments can be supported by Mayer’s (2009, 2011, 2020) Cognitive Model of Multimedia Learning (CTML) that advocates integrating audio and text in the process of learning. The core idea of the CTML is that the human brain utilizes distinct visual/pictorial and verbal/aural channels to process information independently of each other. Learning is at its most efficient when both channels are activated at once.
Nevertheless, as observed by Nakanishi (2015), the acquisition of grammar through reading, particularly when it is combined with auditory stimuli, is a less explored subject, and the situation gets worse when it comes to incidental grammar learning through reading. Research (e.g., Chang & Millett, 2014, 2015; Chang, 2009) has shown that students undergoing audio-assisted reading (AAR) instruction outperform their counterparts receiving reading-only (RO) or listening-only (LO) instruction in both reading and listening comprehension. To the researchers’ best understanding, only three studies have explored the effect of RO rather than AAR on incidental grammar learning, and these studies only focused on “to-infinitives” (Aka, 2020) and prepositions (Altakhaineh & Ibrahim, 2019; Song & Sardegna, 2014). Building on Mayer’s (2009, 2011, 2020) CTML, this study, therefore, explores how AAR can enhance learners’ grammar learning and retention by providing simultaneous visual and auditory input, thus contributing to the emerging conversation on how multimedia modalities can foster more effective grammar learning. More specifically, this study intends to determine the impact of AAR instruction on incidental learning and retention of the present perfect tense to address this gap in the existing literature.
The present perfect tense was selected as the focus structure in the present study since it is “one of the most semantically complex verb forms” (Kearns, 2011, p. 182) and conveys a variety of meanings (Greenbaum & Nelson, 2002; Swan & Walter, 1997). In fact, the complexity of the present perfect tense may be one of the reasons learning the tense proves challenging for English language learners (Han & Hong, 2015; Thumvichit, 2016). Other studies have indicated that L2 learners experience difficulties learning English tense and aspect features due to morphosemantic differences between their first language (L1) and their L2 (Gabriele, 2009; Montrul & Slabakova, 2002). According to Thumvichit (2016), students struggle with the present perfect tense due to a lack of explanations in textbooks and instructional materials. Fujimoto (2017) and Shortall (2007) add that textbooks typically focus on present perfect forms only by providing a thorough explanation of time adverbs like “already,” “just,” “ever,” and “never.”
2 Literature Review
2.1 Incidental vs Intentional Learning
Hulstijn (2003) distinguishes two prevalent perspectives regarding the process of acquiring an L2. One viewpoint emphasizes intentional learning, which involves dedicated and prolonged periods of studying various grammar rules as well as active memorization of numerous words (including their definitions, pronunciation, and spelling). The other perspective, which is complementary, emphasizes incidental learning Hulstijn (2012, p. 1) defines incidental learning refers to “the acquisition of a word or expression without the conscious intention to commit the element to memory, such as picking up an unknown word from listening to someone or from reading a text.” Hulstijn (2003) contends incidental learning processes that can help ease the burden of intentional learning for language learners. Mäntylä (2001) makes a distinction between incidental learning from intentional learning, arguing that in intentional learning participants receive prior notification that they will be assessed on the content they encounter, while incidental learning pertains to the test-free, spontaneous learning processes of everyday life. In essence, incidental learning transpires without the intense scrutiny of evaluation typically associated with formal academic study in institutions of higher learning (Dörnyei, 2009).
2.2 Multimedia Learning: The Underlying Theory of AAR Instruction
Mayer (2009, p. 4) states “people learn better from words and pictures than from words alone.” This brief sentence captures the promise of multimedia learning. Mayer (2009, 2020) believes that educational resources must be delivered in a manner that is compatible with how the human mind works. Using multimedia means taking “advantage of the full capacity of humans for processing information” (Mayer, 2009, p. 6). Mayer (2020) believes that when we present instructional materials in a single mode, we ignore the significant contribution of the human capacity to process materials in the visual and audial modes. This belief is based on two reasons. First, from a quantitative perspective, presenting materials on two channels allows for more information to be conveyed, similar to how more traffic can move in two lanes versus one. Second, Mayer’s qualitative explanation asserts that while words and pictures possess qualitative distinctions, they can synergistically complement each other, fostering learning when learners can mentally integrate correlated visual and verbal representations.
Research has shown that multimedia-supported pedagogy significantly improves learning grammar (e.g., Hagiwara, 2014; Pattemore & Muñoz, 2020), vocabulary (e.g., Aldera & Mohsen, 2013; Bisson, van Heuven, Conklin, & Tunney, 2015), reading skills (e.g., Tragant & Vallbona, 2018), listening skills (e.g., Lee & Mayer, 2015; Yang, 2014), and other language knowledge and skills such as word form recognition (Peters, 2019), word meaning recognition (Montero-Pérez, Peters, & Desmet, 2018), and pronunciation (Kaplan-Rakowski & Loranc-Paszylk, 2019). While Mayer’s CTML addresses how learners utilize dual channels to process visual and auditory input, Skehan’s Limited Attentional Capacity Model (1998) adds further insight into how learners manage cognitive load when processing complex tasks. Skehan’s model posits that learners have limited attentional resources, and when task demands increase, trade-offs occur between different aspects of language performance, such as complexity, accuracy, and fluency (Skehan, 1998). This model is particularly relevant for tasks that require simultaneous processing of both visual and auditory input, such as AAR. Skehan (2009) further explains that heightened cognitive task demands lead to attentional shifts, resulting in a focus on one aspect of language performance, often at the expense of others, such as when learners prioritize comprehension over accuracy.
Mayer (2001, 2009, 2020) categorized multimedia into three types: delivery media, presentation modes, and sensory modalities. Our study specifically examines the sensory modalities viewpoint, which emphasizes the engagement of at least two sensory systems in the learning process. This perspective prioritizes the sensory receptors utilized by learners, such as the eyes and ears, over the codes used to represent knowledge. Mayer (2020) illustrated it with an instance:
In a computer-based environment, an animation can be presented visually, and a narration can be presented auditorily. In a lecture scenario, the speaker’s voice is processed in the auditory channel, and the slides from the projector are processed in the visual channel. In a textbook, illustrations and printed text are both processed visually, at least initially. (p. 10)
This approach is centered on the learner, considering their processing of information activity. In contrast to the presentation-modes viewpoint, the sensory-modalities viewpoint contends that multimedia includes both visual and auditory processing, founded on the assumption that people interpret visual pictures and sounds differently. Hence, the sensory modalities perspective is in accordance with cognitive learning theory, recognizing that people have distinct channels for processing information from visual and auditory stimuli (Mayer, 2009).
2.3 The CTML
Figure 1 depicts CTML as a representation of the information-processing system in humans. Materials in different forms, such as images and text, enter the sensory memory, which briefly saves exact visual and auditory images of the materials. Working memory, where most multimedia learning occurs, temporarily stores and manipulates knowledge and creates linkages between visual and verbal mental models. Long-term memory functions as the learner’s repository of knowledge, capable of retaining substantial amounts of information for extended durations, but necessitating transfer to working memory for active cognitive processing. Mayer (2009, 2020) based this model on three learning science theories: dual channels (Baddeley, 1992; Paivio, 1986), limited capacity (Baddeley, 1992; Chandler & Sweller, 1991), and active processing (Mayer, 2008; Wittrock, 1989), which are summarized in Table 1.
Cognitive theory of multimedia learning (Mayer, 2009, p. 61).
Foundational premise of CTML (Mayer, 2009, p. 63)
| Premise | Definition | Relevant references |
|---|---|---|
| Dual channels | Individuals have distinct channels for handling visual and auditory information | Baddeley (1992), Paivio (1986) |
| Limited capacity | There is a constraint on the quantity of information that humans can process in each channel simultaneously | Baddeley (1992), Chandler and Sweller (1991) |
| Active processing | Individuals actively learn by attending to pertinent information, organizing it into coherent mental representations, and integrating these with existing knowledge | Mayer (2008), Wittrock (1989) |
2.4 Employing AAR to Provide Multimedia Input for Incidental Learning
L2 learners derive significant advantages from reading as it facilitates the acquisition of additional grammatical structures, introduces novel expressions, and enhances their familiarity with colloquial language (Grabe & Stoller, 2020; Wilkinson, 2012). Due to its significance, researchers (e.g., Chang & Millett, 2014, 2015; Chang, 2009; Conklin, Alotabi, Pellicer-Sánchez, & Vilkaitė-Lozdienė, 2020) have explored strategies to support the reading process and maximize its learning outcomes. They suggest that reading in conjunction with aural input enhances comprehension and produces more significant learning gains. This integration contributes to a positive learning experience for learners (Brown, Waring, & Donkaewbua, 2008; Tragant & Vallbona, 2018; Tragant et al., 2016).
AAR is not a novel approach; it has been employed for years in L1 reading programs and is particularly successful with learners with reading difficulties (van Bon, Boksebeld, Font Freide, & van den Hurk, 1991). When done in a setting that encourages shared or leisure reading, it also results in significant improvements in reading and listening abilities (Sénéchal & Cornell, 1993). This is accomplished by encouraging learners to assimilate more extensive semantic units, enhancing processes like analyzing sentence structures and comprehending meaning at a deeper level (Amer, 1997; Dhaif, 1990). Chang (2009, 2011) believes AAR serves as an effective approach for familiarizing students with the authentic cadence of the target language and encouraging text chunking for improved comprehension. This is in line with the term “modality effect” that Mousavi, Low, and Sweller (1995) suggested. The researchers stated that “effective cognitive capacity may be increased if both auditory and visual working memory can be used” to deal with receiving multimedia messages (p. 321). In summary, the “effective size of working memory can be increased by presenting information in a mixed (auditory and visual mode) rather than a single mode” (p. 320).
Vandergrift (2007) evaluated the effectiveness of AAR as a monitoring tool for listening comprehension. He documented that AAR resulted in an auditory-written verification stage, proving particularly advantageous for low-proficiency groups in cultivating auditory discrimination skills and for high-proficiency groups in honing word recognition. Furthermore, AAR could enhance reading fluency as learners are compelled to read at the pace of the auditory input, often surpassing the students’ reading speed (Chang & Millett, 2015). Tragant et al. (2016) highlight an additional advantage of AAR, the dual mode, allowing learners to engage with the task based on their individual strengths. Certain individuals may favor written input, aligning with their unique strengths, while others may lean towards auditory input.
Gobel (2011) also analyzed the effects of AAR on the overall English ability of 162 university students who completed a 10-month AAR program. Statistically significant increases in the students’ language ability, as measured by TOEFL scores, were found in the study. The results of the multiple regression analysis demonstrated that the quantity of AAR significantly predicted the gains in TOEFL scores. In the same vein, researchers (e.g., Brown et al., 2008; Conklin et al., 2020; Tragant & Vallbona, 2018; Tragant et al., 2016) have also conducted studies on reading while listening (RWL) in which the impact of providing visual and auditory text concurrently was sought. Compared to other input modes, learners seemed to find RWL more engaging and tended to have a more positive attitude towards it. Therefore, RWL proves to be advantageous to learning and is positively viewed by students.
2.5 Incidental Learning of Grammar Through AAR vs RO Instruction
Studies (e.g., Rebuschat & Williams, 2012; Tagarelli, Mota, & Rebuschat, 2011; Williams, 2005) show that learners can learn grammatical features of the target language under incidental circumstances. Individuals in those studies who were incidentally exposed to an artificial or semi-artificial grammar performed better than chance on knowledge retention post-tests. However, these researchers concentrated on comprehension, and there has been relatively little study on learning productive grammatical knowledge through incidental exposure (Brooks & Kempe, 2013; Hama & Leow, 2010). Furthermore, few studies have examined grammar learning through reading (Nakanishi, 2015), particularly incidental learning of grammar through reading. There is ample evidence that reading can not only affect students’ learning of particular grammar features (Aka, 2020; Altakhaineh & Ibrahim, 2019; Song & Sardegna, 2014) but also their overall grammar mastery (Elley & Mangubhai, 1983).
Through enhanced reading, Song and Sardegna (2014) investigated preposition learning in 24 Korean high schoolers (15–16 years old). While the control group followed their usual curriculum, the experimental group dedicated extra time to English through an after-school program that included a full hour of reading and half an hour of interactive activities to solidify their learning. Compared to the control group, these students showed greater gains in noticing, understanding, and producing prepositions. However, the study’s small sample size, mixed instructional methods (reading and output), and short duration limit the generalizability of the findings. The mixed-method approach, in particular, makes it difficult to isolate the effects of incidental grammar learning through reading alone, and the prepositions studied are relatively simple grammatical features compared to more complex structures like verb tenses.
Altakhaineh and Ibrahim (2019) investigated if Arabic-speaking EFL learners could incidentally acquire English prepositions through reading exercises. In a pre-and-post-test design, fifty participants were assigned to experimental and control groups. The experimental group engaged in reading comprehension exercises, whereas the control group did not. Both groups underwent tests on English prepositions. Results revealed a significant impact on the experimental group’s multiple-choice performance, with some improvement in the productive task, which was more challenging due to language differences. The study concluded that incorporating exercises into reading led to incidental gains in learning English prepositions. While this study highlighted incidental learning gains, the narrow focus on prepositions and the short-term nature of the intervention limit its broader applicability.
Aka’s (2020) study exposed two groups of Japanese high school EFL learners to varying quantities of “to-infinitives” used as nouns. The experimental group (n = 74) encountered these structures in 40 sentences across five passages, while the control group (n = 83) saw them only ten times. To evaluate the efficacy of the intervention, participants were given grammar exams both before and after the course. The findings demonstrated that the experimental group incidentally acquired knowledge of using “to-infinitives” as nouns through reading. The results showed that despite their concentration on reading comprehension, the students still pay attention to language forms. The finding was argued to indicate that repeated exposure to targeted grammar items facilitates students’ awareness of a grammatical rule, which favors learners’ incidental grammar learning. While the study successfully demonstrated an effect of 40 exposures on “notice” level grammar acquisition, its generalizability could be limited. Testing additional grammatical features and involving participants of diverse proficiency levels might reveal nuances in the effectiveness of exposure.
All the studies mentioned above employed the RO instruction. To the best of the researchers’ knowledge, no previous study has investigated the impact of AAR on incidental learning of grammatical features. Therefore, this investigation aims to address the following research question:
Does AAR instruction significantly impact Iranian EFL learners’ incidental learning and retention of the present perfect tense?
3 Method
3.1 Participants and Setting
We employed a quasi-experimental design with both experimental and control groups. The participants included 34 pre-intermediate EFL learners, aged 16 to 23, selected via convenience sampling from two intact classes at an Iranian language school. The learners’ language proficiency was based on the scores of a standardized language placement test employed by the institute. The classes were randomly assigned to the experimental and control groups. Mayer’s CTML (2009, 2011, 2020) served as the guiding framework for the design of this study. Specifically, the study operationalized the CTML principles of dual-channel processing by integrating both auditory (AAR) and visual (text) input. This allowed learners to process grammatical information through both channels simultaneously, supporting more effective learning and retention. Furthermore, the study aimed to reduce cognitive load by ensuring that the audio and text inputs were presented in a way that minimized unnecessary distractions, thereby allowing learners to focus on the target grammar structure. The experimental group (n = 18, 10 females, 8 males) received the AAR instruction, and the control group (n = 16, 9 females, 7 males) received the RO instruction. This study was conducted when students had just started studying American English File 1, a course book published by Oxford University Press. Based on the researchers’ review of their course books, it was determined that the present perfect tense was introduced in the final unit of the book, which the students would not study as their course units; thus, present perfect was probably a novel structure for the learners in this study. The study focused on the first three functions of the present perfect tense (i.e., announcing the news, talking about finished actions, and situations “up to now”) as specified in the grammar book How English works: A grammar practice book (Swan & Walter, 1997, pp. 152–158).
3.2 Instrumentation
3.2.1 Reading Passages
The study’s reading passages and comprehension questions were developed 1 month prior to the main study. Taking the learners’ proficiency level and their interest into account, the researchers selected six passages from ESL/EFL websites (e.g., www.eslprintables.com) to engage the learners. A pilot test was conducted to assess and refine the passages and comprehension questions, involving ten learners with similar characteristics to those in the main study. Four measures were utilized to ascertain that the content and difficulty level of the passages and questions were commensurate with the learners’ language proficiency.
Firstly, the researchers discussed the reading passages and the questions with an ELT expert and three experienced EFL teachers. Recommendations from these discussions led to the modification of some reading comprehension questions, but the reading passages remained intact. Secondly, the researchers measured the Common European Framework of Reference for Languages (CEFR) level of each type and token used in the passages using the English Vocabulary Profile (EVP) developed by Cambridge University Press and Assessment, British Council, University of Bedfordshire, and EnglishUK. The type indicated the number of unique words in the text, excluding numbers, while the token represented the total occurrences of each word in texts, excluding numbers (Table 2). Thirdly, the researchers used two AI text inspectors, Textinspector and Cathoven AI, to analyze the CEFR level of each passage. Fourthly, reading passages in students’ current book (American English File 1) and their previous book (American English File Intro) were analyzed using EVP and AI text inspectors. The number of the present perfect structure in every passage ranged between 10 and 14 items. It also should be noted that the word count of passages ranges between 237 and 291 words, including numbers and titles (Table 3).
EVP of passages
| Types | Percentage (%) | Token | Percentage (%) | ||
|---|---|---|---|---|---|
| Passage 1 | A1 | 94 | 74.60 | 185 | 80.09 |
| A2 | 24 | 19.05 | 35 | 15.15 | |
| B1 | 7 | 5.56 | 8 | 3.46 | |
| B2 | 1 | 0.79 | 3 | 1.30 | |
| C1 | 0 | 0 | 0 | 0 | |
| C2 | 0 | 0 | 0 | 0 | |
| Passage 2 | A1 | 117 | 75.48 | 249 | 86.16 |
| A2 | 25 | 15.48 | 27 | 9.34 | |
| B1 | 12 | 7.74 | 12 | 4.15 | |
| B2 | 1 | 0.65 | 1 | 0.35 | |
| C1 | 0 | 0 | 0 | 0 | |
| C2 | 0 | 0 | 0 | 0 | |
| Passage 3 | A1 | 101 | 77.69 | 218 | 87.55 |
| A2 | 20 | 15.38 | 22 | 8.84 | |
| B1 | 4 | 3.08 | 4 | 1.61 | |
| B2 | 5 | 3.85 | 5 | 2.01 | |
| C1 | 0 | 0 | 0 | 0 | |
| C2 | 0 | 0 | 0 | 0 | |
| Passage 4 | A1 | 78 | 56.12 | 177 | 64.84 |
| A2 | 26 | 18.71 | 39 | 14.29 | |
| B1 | 20 | 14.39 | 29 | 10.62 | |
| B2 | 9 | 6.47 | 17 | 6.23 | |
| C1 | 5 | 3.60 | 7 | 2.56 | |
| C2 | 1 | 0.72 | 4 | 1.47 | |
| Passage 5 | A1 | 71 | 47.02 | 153 | 60.47 |
| A2 | 31 | 20.53 | 41 | 16.21 | |
| B1 | 29 | 19.21 | 34 | 13.44 | |
| B2 | 17 | 11.26 | 22 | 8.70 | |
| C1 | 3 | 1.99 | 3 | 1.19 | |
| C2 | 0 | 0 | 0 | 0 | |
| Passage 6 | A1 | 91 | 58.33 | 177 | 68.08 |
| A2 | 29 | 18.59 | 34 | 13.08 | |
| B1 | 23 | 14.74 | 35 | 13.46 | |
| B2 | 11 | 7.05 | 12 | 4.62 | |
| C1 | 2 | 1.28 | 2 | 0.77 | |
| C2 | 0 | 0 | 0 | 0 | |
CEFR Level of passages, number of present perfect structures in each passage, and passage word count
| Passage name | CEFR level | Number of present perfect structures | Passage word count |
|---|---|---|---|
| Passage 1: All about my city | A2 | 10 | 237 |
| Passage 2: What’s up? | A2 | 14 | 286 |
| Passage 3: A Man in the army | B1 | 13 | 261 |
| Passage 4: Ice age wolf | B1 | 8 | 291 |
| Passage 5: Climate change | B1 | 12 | 274 |
| Passage 6: Tech and our lives | A2 | 11 | 276 |
3.2.2 Grammar Test
The researchers developed a grammar test for measuring the students’ knowledge of the present perfect tense. The test, which was prepared 1 month prior to the main study, served as the pre-test, post-test, and delayed post-test. To prevent memorization, the researchers shuffled the test items for each test administration. The test consisted of three subtests: noticing (25 multiple-choice questions), noticing + manipulation (15 re-ordering questions), and noticing + manipulation + production (10 context-based questions), and included other filler items besides the main items (Table 4). The test was designed based on what the students had learned in previous lessons and courses, and only 20 items were focused on measuring the students’ learning and retention of the present perfect tense across the three subtests. During the pilot phase, the reliability of the test was calculated to be 0.87, indicating a high degree of internal consistency. The time limit of 60 min for each test, including the pre-test, post-test, and delayed post-test, was determined based on the average time taken by participants in the pilot study. The tests were scored using a binary scale (1 for correct, 0 for incorrect) due to its simplicity and compatibility with statistical analyses. This also facilitated straightforward interpretation and comparison of performance across participants and test sections.
Number of test items in the subsets of noticing, noticing + manipulation, and noticing + manipulation + production of the grammar test
| Grammar items | N | N + M | N + M + P |
|---|---|---|---|
| Present perfect | 10 | 6 | 4 |
| Subject–verb agreement (limited to “be” form) | 1 | 1 | 0 |
| Simple present | 2 | 0 | 2 |
| Imperatives | 1 | 1 | 0 |
| Possessive’s & Whose | 1 | 1 | 0 |
| Present continuous | 1 | 0 | 2 |
| Simple past | 2 | 0 | 1 |
| Simple future (will and going to) | 1 | 1 | 0 |
| Like/love/hate + ing | 1 | 1 | 0 |
| Preposition of time and place | 1 | 1 | 0 |
| There is/there are | 1 | 1 | 0 |
| Adverbs of frequency (always, never, sometimes, usually) | 1 | 0 | 1 |
| Wh-questions | 1 | 1 | 0 |
| Modals (can) | 1 | 1 | 0 |
Table 5 shows the reliability indices for the pre-test, post-test, and delayed post-tests as well as those for their sub-sections (i.e., noticing, noticing + manipulation, and noticing + manipulation + production). Except for the noticing + manipulation + production sub-section of the pre-test, whose reliability index was 0.68, all of the reliability indices could be considered appropriate (Tseng et al., 2006). The low-reliability index for the noticing + manipulation + production sub-section of the pre-test may be justified by its limited number of items. As noted by Pallant (2016), low-reliability indices are frequent in case the number of items is less than 10.
Reliability statistics of the grammar test
| Cronbach’s Alpha | N of items | ||
|---|---|---|---|
| Pre-test | Noticing | 0.895 | 10 |
| Noticing/Manipulation | 0.703 | 6 | |
| Noticing/Manipulation/Production | 0.750 | 4 | |
| Total | 0.912 | 20 | |
| Post-test | Noticing | 0.880 | 10 |
| Noticing/Manipulation | 0.810 | 6 | |
| Noticing/Manipulation/Production | 0.750 | 4 | |
| Total | 0.927 | 20 | |
| Delayed post-test | Noticing | 0.921 | 10 |
| Noticing/Manipulation | 0.858 | 6 | |
| Noticing/Manipulation/Production | 0.770 | 4 | |
| Total | 0.954 | 20 |
The average inter-item correlations for the four items of “noticing + manipulation + production” turned out to be 0.353, thus ensuring that the noticing + manipulation + production sub-section of the pre-test enjoyed an appropriate reliability index.
3.3 Data Collection Procedure
In the experimental group, the students received the audio input simultaneously with the presented passage. The researchers employed two artificial intelligence (AI) based websites (www.naturalreaders.com and www.play.ht) to play the audio for the experimental group. The recording speeds ranged from about 110–150 words per minute, which is a moderate conversational pace that should not hinder students’ ability to understand (Griffiths, 1992). With the help of a sample text + audio, students were asked if they had a problem with the speed of the audio. None of the learners in the experimental group expressed any concern or difficulty regarding the speed of the audio.
The present study was conducted over eight weeks, during which the participants completed several activities. In the first week, a pre-test was administered to assess the students’ prior knowledge of the present perfect tense. The researchers began the classes a week later, and for three weeks, both the experimental and control groups attended two sessions each week. The classes did not explicitly focus on teaching the present perfect tense. Instead, after a brief discussion about the subject matter of each passage, students in the AAR group were given audio along with their passages, while the RO group did not have audio support. Both groups were required to concentrate on the passages to complete comprehension tasks at the end of each reading. The comprehension tasks included true/false, multiple choice, summarizing key points, and discussion questions. For true/false, multiple-choice, and summarizing key points, students worked individually, while for the discussion questions, they worked in pairs. After completing the tasks, the teacher reviewed the answers with the students, allowing for correction and discussion of any incorrect responses. Following three weeks of reading classes, students took a post-test to assess their learning. Two weeks later, a delayed post-test was given to evaluate their retention of the knowledge gained.
4 Results
Skewness and kurtosis measures, along with their ratios to standard errors, were used to assess normality of the data distribution. As the computed ratios fell within the range of ±1.96, it was inferred that the current data exhibited no substantial deviation from normality.
A one-way ANCOVA was conducted to compare the mean scores of the experimental and control groups on the post-test for incidental learning of the present perfect tense, controlling for the influence of the pre-test. The results, depicted in Table 6, indicate that the experimental group achieved a higher mean on the post-test compared to the control group.
Descriptive statistics for the post-test of incidental learning of the present perfect tense by groups with pre-test
| Group | Mean | Std. error | 95% Confidence interval | |
|---|---|---|---|---|
| Lower bound | Upper bound | |||
| Experimental | 11.783a | 0.658 | 10.441 | 13.125 |
| Control | 8.619a | 0.698 | 7.196 | 10.043 |
aCovariates appearing in the model are evaluated at the following values: Pre-test = 6.06.
Table 7 presents the outcomes of the One-Way ANCOVA. The findings (F(1, 37) = 34.88, p < 0.05, partial η 2 = 0.485, representing a large effect size) suggest that the experimental group demonstrated a significant performance advantage over the control group on the post-test, even after accounting for the influence of the pre-test. Consequently, AAR significantly enhanced the incidental learning of the present perfect tense among learners.
Tests of between-subjects effects of the post-test of incidental learning of the present perfect tense by groups with pre-test
| Source | Type III sum of squares | Df | Mean square | F | Sig. | Partial Eta squared |
|---|---|---|---|---|---|---|
| Pre-test | 1073.265 | 1 | 1073.265 | 137.798 | 0 | 0.816 |
| Group | 84.667 | 1 | 84.667 | 10.871 | 0.002 | 0.26 |
| Error | 241.45 | 31 | 7.789 | |||
| Total | 4,984 | 34 |
A multivariate analysis of covariance (MANCOVA) was conducted to compare the mean scores of the experimental and control groups on the subtests of “noticing, noticing + manipulation” and “noticing + manipulation + production,” while controlling for the impact of the pre-test. Table 8 presents the outcomes of the MANCOVA. The significant findings (F(3, 27) = 6.64, p < 0.05, Partial η 2 = 0.425, representing a large effect size) indicate substantial differences between the mean scores of the experimental and control groups on the post-test subsections of incidental learning of the present perfect tense. This difference persists even after accounting for the effects of the pre-test. Therefore, AAR significantly enhanced learners’ incidental learning across the present perfect tense tests, encompassing “noticing, noticing + manipulation” and “noticing + manipulation + production” sub-sections.
MANCOVA results for the sub-sections of the post-test of incidental learning of the present perfect tense by groups with pre-test
| Effect | Value | F | Hypothesis df | Error df | Sig. | Partial Eta squared | |
|---|---|---|---|---|---|---|---|
| Intercept | Pillai’s Trace | 0.605 | 13.774 | 3 | 27 | 0.000 | 0.605 |
| Wilks’ Lambda | 0.395 | 13.774 | 3 | 27 | 0.000 | 0.605 | |
| Hotelling’s Trace | 1.530 | 13.774 | 3 | 27 | 0.000 | 0.605 | |
| Roy’s Largest Root | 1.530 | 13.774 | 3 | 27 | 0.000 | 0.605 | |
| PreN | Pillai’s Trace | 0.627 | 15.110 | 3 | 27 | 0.000 | 0.627 |
| Wilks’ Lambda | 0.373 | 15.110 | 3 | 27 | 0.000 | 0.627 | |
| Hotelling’s Trace | 1.679 | 15.110 | 3 | 27 | 0.000 | 0.627 | |
| Roy’s Largest Root | 1.679 | 15.110 | 3 | 27 | 0.000 | 0.627 | |
| PreNM | Pillai’s Trace | 0.473 | 8.082 | 3 | 27 | 0.001 | 0.473 |
| Wilks’ Lambda | 0.527 | 8.082 | 3 | 27 | 0.001 | 0.473 | |
| Hotelling’s Trace | 0.898 | 8.082 | 3 | 27 | 0.001 | 0.473 | |
| Roy’s Largest Root | 0.898 | 8.082 | 3 | 27 | 0.001 | 0.473 | |
| PreNMP | Pillai’s Trace | 0.390 | 5.747 | 3 | 27 | 0.004 | 0.390 |
| Wilks’ Lambda | 0.610 | 5.747 | 3 | 27 | 0.004 | 0.390 | |
| Hotelling’s Trace | 0.639 | 5.747 | 3 | 27 | 0.004 | 0.390 | |
| Roy’s Largest Root | 0.639 | 5.747 | 3 | 27 | 0.004 | 0.390 | |
| Group | Pillai’s Trace | 0.425 | 6.643 | 3 | 27 | 0.002 | 0.425 |
| Wilks’ Lambda | 0.575 | 6.643 | 3 | 27 | 0.002 | 0.425 | |
| Hotelling’s Trace | 0.738 | 6.643 | 3 | 27 | 0.002 | 0.425 | |
| Roy’s Largest Root | 0.738 | 6.643 | 3 | 27 | 0.002 | 0.425 | |
Table 9 displays the mean scores of the experimental and control groups on the post-test sub-sections, accounting for the effects of pre-tests. The outcomes indicate that the experimental group achieved higher means than the control group on the sub-sections of the post-test, even after controlling for the effects of pre-tests.
Descriptive statistics for the sub-sections of the post-test of incidental learning of the present perfect tense by groups with pre-test
| Dependent variable | Group | Mean | Std. error | 95% Confidence interval | |
|---|---|---|---|---|---|
| Lower bound | Upper bound | ||||
| PostN | Experimental | 5.603a | 0.403 | 4.778 | 6.428 |
| Control | 4.384a | 0.428 | 3.508 | 5.259 | |
| PostNM | Experimental | 4.164a | 0.266 | 3.620 | 4.707 |
| Control | 2.566a | 0.282 | 1.989 | 3.143 | |
| PostNMP | Experimental | 2.044a | 0.189 | 1.658 | 2.431 |
| Control | 1.638a | 0.201 | 1.227 | 2.048 | |
aThe covariates included in the model are assessed at the specified values: PreN = 3.09, PreNM = 1.97, and PreNMP = 1.00.
Table 10 provides the outcomes of Between-Subject Effects. Based on these results and the descriptive statistics in Table 9, the following conclusions can be drawn:
Tests of between-subjects effects of the incidental learning of the present perfect tense by groups with pre-tests
| Source | Dependent variable | Type III sum of squares | Df | Mean square | F | Sig. | Partial Eta squared |
|---|---|---|---|---|---|---|---|
| PreN | PostN | 134.101 | 1 | 134.101 | 46.332 | 0.000 | 0.615 |
| PostNM | 1.108 | 1 | 1.108 | 0.882 | 0.355 | 0.030 | |
| PostNMP | 1.427 | 1 | 1.427 | 2.246 | 0.145 | 0.072 | |
| PreNM | PostN | 0.589 | 1 | 0.589 | 0.203 | 0.655 | 0.007 |
| PostNM | 27.683 | 1 | 27.683 | 22.028 | 0.000 | 0.432 | |
| PostNMP | 3.824 | 1 | 3.824 | 6.016 | 0.020 | 0.172 | |
| PreNMP | PostN | 3.519 | 1 | 3.519 | 1.216 | 0.279 | 0.040 |
| PostNM | 0.000 | 1 | 0.000 | 0.000 | 0.988 | 0.000 | |
| PostNMP | 6.714 | 1 | 6.714 | 10.563 | 0.003 | 0.267 | |
| Group | PostN | 12.289 | 1 | 12.289 | 4.246 | 0.048 | 0.128 |
| PostNM | 21.090 | 1 | 21.090 | 16.782 | 0.000 | 0.367 | |
| PostNMP | 1.366 | 1 | 1.366 | 2.149 | 0.153 | 0.069 | |
| Error | PostN | 83.937 | 29 | 2.894 | |||
| PostNM | 36.445 | 29 | 1.257 | ||||
| PostNMP | 18.433 | 29 | 0.636 | ||||
| Total | PostN | 1263.000 | 34 | ||||
| PostNM | 546.000 | 34 | |||||
| PostNMP | 189.000 | 34 |
A: The experimental group (M = 5.60) demonstrated a significant superiority of performance over the control group (M = 4.38) on the post-test of noticing after adjusting for the pre-test effect (F(1, 29) = 4.24, p < 0.05, Partial η 2 = 0.128, indicating a moderate effect size).
B: The experimental group (M = 1.16) showed superior performance compared to the control group (M = 2.56) on the noticing + manipulation sub-section of the post-test, controlling for the pre-test effect (F(1, 29) = 16.78, p < 0.05, Partial η 2 = 0.367, indicating a large effect size).
C: No significant difference was observed between the experimental (M = 2.04) and control (M = 1.63) groups’ means on the noticing + manipulation + production sub-section of the post-test after accounting for the pre-test effect (F(1, 29) = 2.14, p < 0.05, Partial η 2 = 0.069, indicating a moderate effect size).
An Independent Samples t-test was conducted to examine the differences between the experimental and control groups’ means on the delayed post-test, assessing the retention of the target grammar structure. Table 11 displays the descriptive statistics for both groups on the delayed post-test, revealing that the experimental group exhibited a higher mean than the control group.
Descriptive statistics of the delayed post-test of incidental learning of present perfect tense by groups
| Group | N | Mean | Std. deviation | Std. error mean | |
|---|---|---|---|---|---|
| Delayed post-test | Experimental | 18 | 14.72 | 5.074 | 1.196 |
| Control | 16 | 6.63 | 7.145 | 1.786 |
Table 12 illustrates the outcomes of the Independent Samples t-test. The results (t(32) = 3.84, p < 0.05, r = 0.562, representing a large effect size, 95% CI [3.80, 12.38]) suggest a significant difference, with the experimental group exhibiting a notably higher mean on the delayed post-test compared to the control group. Consequently, learners exposed to AAR instruction retained knowledge of the present perfect tense significantly longer than those who received the RO instruction.
Independent samples t-test of the delayed post-test of incidental learning of the present perfect tense by groups
| Levene’s test for equality of variances | t-test for equality of means | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| F | Sig. | T | Df | Sig. (2-tailed) | Mean difference | Std. error difference | 95% Confidence interval of the difference | |||
| Lower | Upper | |||||||||
| Equal variances assumed | 3.484 | 0.071 | 3.843 | 32 | 0.001 | 8.097 | 2.107 | 3.805 | 12.389 | |
| Equal variances not assumed | 3.767 | 26.723 | 0.001 | 8.097 | 2.150 | 3.684 | 12.510 | |||
A MANOVA was conducted to evaluate the mean scores of the experimental and control groups on the sub-sections of the delayed post-test to answer the fourth research question. Table 13 shows the experimental and control groups’ means of noticing, noticing + manipulation, and noticing + manipulation + production. The results showed that the experimental group had higher means than the control group on delayed post-tests.
Descriptive statistics for the sub-sections of the delayed post-tests of incidental learning of present perfect tense by groups
| Dependent variable | Gender | Mean | Std. error | 95% Confidence interval | |
|---|---|---|---|---|---|
| Lower bound | Upper bound | ||||
| Noticing | Experimental | 7.111 | 0.816 | 5.449 | 8.774 |
| Control | 3.563 | 0.866 | 1.799 | 5.326 | |
| Noticing + manipulation | Experimental | 4.833 | 0.406 | 4.006 | 5.660 |
| Control | 1.813 | 0.431 | 0.935 | 2.690 | |
| Noticing + manipulation + production | Experimental | 2.722 | 0.324 | 2.061 | 3.383 |
| Control | 1.250 | 0.344 | 0.549 | 1.951 | |
Table 14 presents the outcomes of MANOVA. The findings (F(3, 30) = 9.65, p < 0.05, pη 2 = 0.491, representing a large effect size) suggest a significant disparity between the overall mean scores of the experimental and control groups on the sub-sections of the delayed post-test. Thus, learners who received the AAR instruction retained the incidental learning of the present perfect tense on the tests significantly longer compared to those who received the RO instruction.
Multivariate tests of the delayed post-tests of incidental learning of the present perfect tense by groups
| Effect | Value | F | Hypothesis df | Error df | Sig. | Partial Eta squared | |
|---|---|---|---|---|---|---|---|
| Intercept | Pillai’s Trace | 0.798 | 39.594 | 3 | 30 | 0.000 | 0.798 |
| Wilks’ Lambda | 0.202 | 39.594 | 3 | 30 | 0.000 | 0.798 | |
| Hotelling’s Trace | 3.959 | 39.594 | 3 | 30 | 0.000 | 0.798 | |
| Roy’s Largest Root | 3.959 | 39.594 | 3 | 30 | 0.000 | 0.798 | |
| Gender | Pillai’s Trace | 0.491 | 9.654 | 3 | 30 | 0.000 | 0.491 |
| Wilks’ Lambda | 0.509 | 9.654 | 3 | 30 | 0.000 | 0.491 | |
| Hotelling’s Trace | 0.965 | 9.654 | 3 | 30 | 0.000 | 0.491 | |
| Roy’s Largest Root | 0.965 | 9.654 | 3 | 30 | 0.000 | 0.491 | |
Table 15 presents the outcomes of the Between-Subject Effects analysis. Drawing on these results and the descriptive statistics in Table 14, the following conclusions can be drawn:
Tests of between-subjects effects of the delayed post-tests of incidental learning of the present perfect tense by groups
| Source | Dependent variable | Type III sum of squares | df | Mean square | F | Sig. | Partial Eta squared |
|---|---|---|---|---|---|---|---|
| Group | DelayedN | 106.667 | 1 | 106.667 | 8.896 | 0.005 | 0.218 |
| DelayedNM | 77.298 | 1 | 77.298 | 26.054 | 0.000 | 0.449 | |
| DelayedNMP | 18.359 | 1 | 18.359 | 9.693 | 0.004 | 0.232 | |
| Error | DelayedN | 383.715 | 32 | 11.991 | |||
| DelayedNM | 94.938 | 32 | 2.967 | ||||
| DelayedNMP | 60.611 | 32 | 1.894 | ||||
| Total | DelayedN | 1497.000 | 34 | ||||
| DelayedNM | 568.000 | 34 | |||||
| DelayedNMP | 219.000 | 34 |
A: The experimental group (M = 7.11) demonstrated a significant advantage over the control group (M = 3.56) on the delayed Post-test of noticing (F(1, 32) = 8.89, p < 0.05, Partial η 2 = 0.218, indicating a moderate effect size).
B: The experimental group (M = 4.83) significantly outperformed the control group (M = 1.81) on the delayed post-test of noticing + manipulation (F(1, 32) = 26.08, p < 0.05, Partial η 2 = 0.449, indicating a moderate effect size).
C: The experimental group (M = 2.72) exhibited a significant performance advantage over the control group (M = 1.25) on the delayed post-test of noticing + manipulation + production (F(1, 32) = 9.69, p < 0.05, Partial η 2 = 0.232, indicating a moderate effect size).
5 Discussion
This study’s results indicate a significant enhancement in learners’ incidental learning of the present perfect tense through AAR. The findings also revealed that learners in the experimental group significantly outperformed the control group in the “noticing” subsection of the post-test. This is also true in the subsection of “noticing + manipulation” where the experimental group outperformed the control group significantly. The learning gains observed both in AAR and RO on the post-test in the “notice” and the “notice + manipulation” sections are likely the result of repeated encounters with the target structure, present perfect; however, since there was no difference between the materials presented to each group of participants in our study, we can conclude that AAR instruction was the only contributing factor that the experimental group demonstrated significantly superior performance. However, in the “noticing + manipulation + production” subsection of the present perfect tense on the post-test, there was no statistically significant difference between the experimental group and the control group. In the “noticing + manipulation + production” section of the test, the students needed to produce an answer based on the context and the information provided to them, unlike the other two subtests of “notice” and “notice + manipulation” wherein they could remember the form of the target structure from the text or even guess the best option (notice) and sentence order (noticing + manipulation). The “noticing + manipulation + production” section is more cognitively demanding due to its context-based and communicative functionality. This result aligns with Skehan’s (1998) Limited Attentional Capacity Model. According to Skehan (2009), heightened cognitive task demands, due to competition for limited attentional resources, can result in trade-off effects, particularly between complexity and accuracy.
These findings are consistent with what Mayer (2009, 2020) proposed regarding the impact of multimedia learning, especially within the framework of dual-coding theory (Paivio, 1986). Mayer (2009) believes that when we present instructional materials in a single mode, which is typically printed text, we ignore the significant contribution of the human information processing capability both in the visual or auditory modes; therefore, presenting material through two channels is better than one due to two reasons. As a quantitative rationale reason, Mayer (2009, p. 7) states “more material can be presented on two channels than on one channel – just as more traffic can travel in two lanes than in one lane.” Presenting through two channels is equivalent to presenting the topic twice, providing the learner with double the amount of exposure to the explanation. According to Mayer (2009), the qualitative perspective, on the other hand, argues that although words and pictures are qualitatively different, they can complement each other effectively. Learning occurs when students are able to mentally integrate and combine these two elements. Mayer (2001) states that transfer and retention can be used to measure the effectiveness of multimedia learning. The transfer is the ability of the learner to apply the information from multimedia input to address novel problems; however, information retained by the learner in the form of multimedia input is referred to as retention. The transfer was validated in the current investigation through the observation that the experimental group achieved significantly greater post-test gains in comparison to the control group. The results revealed a superior overall performance of the experimental group on the post-test, with significantly better performance on the subtests of “noticing, noticing + manipulation,” and “noticing + manipulation + production.” This discovery aligns with the outcomes of prior research on incidental vocabulary learning through AAR and RO as discussed in the literature review (Brown et al., 2008; Malone, 2018; Webb & Chang, 2012). Although these studies are on incidental vocabulary learning, this is not a surprise that AAR can also be effective in incidental grammar learning.
Another factor contributing to the enhanced performance of the experimental group, according to Bell (1998), is that students generally learn better when presented with the material through AAR instruction since they have more confidence in their ability to differentiate between sounds. This is also in line with studies highlighting the significance of aural-written verification (Brown et al., 2008; Vandergrift, 2007). Aural-written verification seems more likely to assist students in improving their word recognition, developing their auditory discrimination skills, and gaining awareness of the connection between form and meaning. As a result, AAR assists students in maintaining the integrity of the original texts and improving their grammar gain and retention, such as the present perfect tense in this case. This aligns with the findings of Brown et al. (2008), indicating that students prefer RWL mode to other modes because:
The necessity of having to segment or chunk the text of the story as they read it was done for them by the narrator on the cassette. Consequently, it would appear they had enough spare working memory to access the content more effectively and, in turn, make better deductions of the meanings of the target words. (pp. 156–157)
According to Mayer’s (2009, 2011, 2020) CTML, learners process information through two distinct channels: visual/pictorial and verbal/auditory. When learners receive input in both channels simultaneously, such as through AAR, they are able to process more information effectively than they would with single-mode input (e.g., reading alone). This dual-channel processing is particularly beneficial in language learning because it helps learners build referential connections between the visual (text) and auditory (sound) input, reinforcing the grammatical structures presented. In the case of incidental learning of grammar, this integration of audio and text provides learners with multiple exposures to the target structure in two different formats, which aids in deepening their understanding and promoting retention. Furthermore, as Mayer (2009) points out, multimedia learning capitalizes on the brain’s ability to integrate information across both channels, leading to a more coherent mental model of the learning material. This suggests that learners exposed to both visual and auditory inputs are more likely to internalize grammatical structures incidentally, as both modalities work together to support cognitive processing and memory retention.
Additionally, by activating both channels, learners may also experience a reduction in cognitive load, as AAR allows for more efficient chunking of information. This means that the grammar structures, such as the present perfect tense in our study, are processed more effectively because learners have more cognitive resources available for understanding the form-meaning relationships of the tense. Consistent with Mayer’s CTML predictions, our findings revealed that learners in the experimental group, who received both auditory and visual input through AAR, demonstrated significantly higher scores on the post-test. This suggests that processing information through both auditory and visual channels enhanced their ability to retain and recall the target grammar structures. Furthermore, the reduction in cognitive load facilitated by AAR allowed learners to focus more on processing and internalizing the present perfect tense, contributing to their improved performance.
As we mentioned earlier, the AAR group learned and retained the target vocabulary significantly better than other modes of instruction, namely RO and LO. Unlike the AAR group, in the RO setting, even though students read the passages at their own pace, they tend to fragment sentences inconsistently while reading. This practice compromises the coherence of the written texts and may lead to a lack of comprehension and minimal retention, as observed in the RO learners in our study.
The study revealed that the experimental group outperformed the control group significantly in the noticing and noticing + manipulation subsections of the post-test. However, in the noticing + manipulation + production subsection of the present perfect tense on the post-test, no statistically significant difference was observed between the two groups. The findings align with the predictions made by Mayer’s CTML retention of information received via multimedia. In this study, learners demonstrated evidence of retention when they showed significant gains and retention on the delayed post-test in test items and in the subtests of “noticing, noticing + manipulation,” and “noticing + manipulation + production.” According to CTML, combining text and audio assists learners in reinforcing the referential connections between text and audio, resulting in improved incidental learning and retention of grammar. The availability of two forms of input for the target structure, present perfect, is most likely responsible for reinforcing their incidental grammar learning effects. It further states presenting verbal and visual information aids learners in forming mental representations, depicting relationships, or providing a gestalt. This theory posits that individuals employ cognitive processing to make sense of experiences and construct a unified mental model. Mayer (2009) believes that “these active cognitive processes include paying attention, organizing incoming information, and integrating incoming information with other knowledge” (p. 67). Mayer (2009, p. 68) adds that cognitive processing results in “the construction of a coherent mental representation.” In the present study, therefore, we can conclude that since students could form a unified mental model of the received input, as the result of receiving AAR instruction, they could better retain the knowledge of incidental grammar learning in this study.
This study aligns with Teng and Zhang’s assertion (2021) grounded in Paivio’s (1986) dual-coding theory, suggesting that dual channels maintain consistent quality, fostering the creation of enduring mental models, alleviating cognitive load for EFL learners during information processing and improving vocabulary recall. Another reason for the AAR group’s superior performance in the delayed post-test compared to the RO group can be attributed, in part, to the influences of task familiarity and repetition, that is, “repetitions of the same or slightly altered tasks – whether whole tasks or parts of a task” (Bygate & Samuda, 2005, p. 43). In the present study, the researchers employed the same tasks in the pre-test, post-test, and delayed post-test but in a different order. This finding supports Bygate and Samuda’s (2005) hypothesis, which suggests that learners, through repeated interactions with the same task, shift from a focus on meaning to a focus on form. As learners become more familiar with the content, they are able to pay closer attention to the linguistic structures of their output. However, this shift can lead to a trade-off between complexity and accuracy, as described by Skehan’s Limited Attentional Capacity Model (1998, 2009).
6 Conclusion
The implications of the current study’s findings for EFL classrooms are multifaceted. First, material designers and teachers should not underestimate the importance of incidental grammar learning and teaching through reading. While research underscores the significance and effectiveness of explicitly teaching grammar, it does not guarantee consistent learning outcomes. Al-Mekhlafi and Nagaratnam (2011) argue that although students’ knowledge of grammatical rules and terminology might improve via explicit teaching of grammar, their ability to apply these principles in real-world language use would not. Furthermore, Petraki and Hill (2011) discovered that a significant number of L2 teachers, even though they consider grammar instruction to be both vital and effective, admitted to having an inadequate understanding of grammar or an incapacity to explain grammar appropriately. Therefore, students cannot benefit from teachers’ explicit instruction on grammar. Many educators are troubled by the inconsistency between their belief that grammar should be taught contextually and their practical application of that belief. (Phipps & Borg, 2009). Consequently, L2 learners might get the impression that learning grammar is unpleasant and uninteresting (Jean & Simard, 2011).
Second, teaching and presenting material through multimedia can be highly beneficial. In this study, the experimental group received reading materials in two modes, visual and audial, which triggered two channels of sensory memory (ear and eye). Receiving input through the ears (sounds) and the eyes (text), the working memory organizes and incorporates information with the pre-existing knowledge in the long-term memory. Therefore, learning and retention take place since students have a unified model of the received input. Previous studies (e.g., Brown et al., 2008; Chang, 2009; Tragant & Vallbona, 2018; Tragant et al., 2016) suggest AAR instruction is more effective since students prefer this approach.
Based on the findings of this study, several recommendations for educators can be made to enhance grammar instruction in EFL classrooms. Teachers are encouraged to incorporate multimedia input (audio and visual) into grammar instruction, as dual-channel processing has been shown to significantly improve retention and recall of grammar structures like the present perfect tense. Additionally, AAR should be used as a supplementary tool for teaching complex grammatical structures, particularly in contexts where explicit grammar instruction alone may not suffice. By combining AAR with other methods, educators can provide students with multiple modes of engagement, deepening their understanding of grammatical forms. Furthermore, educators should provide repeated exposure to grammar structures in both audio and text formats, as this promotes incidental learning and helps students notice and internalize grammatical rules over time.
However, several limitations of this study should be acknowledged. First, the use of convenience sampling may limit the generalizability of the findings. This method was necessary due to the practical constraints of working with intact classrooms in a language school and time limitations, but it may reduce the representativeness of the sample. Second, the relatively small sample size (n = 34) could affect the statistical power of the results and may not provide sufficient generalizability. Future studies could implement randomized sampling and aim for larger, more diverse samples to expand the scope and applicability of the findings. For future research, the researchers encourage the inclusion of learners with different proficiency levels in their design to ascertain the findings of this study. It can also be beneficial to see the effect of AAR instruction on those students’ incidental learning of lexical and grammatical features. Finally, it is suggested that future studies examine other grammatical features (e.g., causative structures) to determine whether AAR instruction leads to the incidental learning of those features as well.
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Funding information: Authors state no funding involved.
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Author contributions: MN proposed the research idea and suggested the outline for the different sections of the paper. He also commented on and revised multiple drafts of the manuscript, submitted the paper to the journal and did all the correspondence. PJ found the relevant sources, collected the data, wrote the original draft and revised it multiple drafts based on the feedback provided by MN. Additionally, the authors have approved the final version of the manuscript and have agreed to Open Education Studies’ submission policies.
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Conflict of interest: Authors state no conflict of interest.
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Data availability statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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