The particular dialect or language that a person chooses to use on any occasion is called a code 2050 Copyright © 2024 The Author IDEAS is licensed under CC-BY-SA 4.0 License Issued by English study program of IAIN Palopo IDEAS Journal of Language Teaching and Learning, Linguistics and Literature ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) Volume 13, Number 1, June 2025 pp. 2050 - 2069 The Role of Socioeconomic Status in EFL Learning Strategies Finaty Ahsanah1, Dias Tiara Putri Utomo2, Ririn Indah Prastiwi3 1,2Faculty of Health, Universitas Muhammadiyah Lamongan, East Java 3Politeknik Pelayaran Surabaya, East Java Corresponding E-Mail: finatyahsanah@umla.ac.id Received: 2025-06-29 Accepted: 2025-06-30 DOI: 10.24256/ideas. v13i1.6842 Abstract This current study aims to investigate the relationship between students’ socioeconomic status (SES) and Language learning strategies preference. The researchers used a quantitative research design to obtain the result. This research was conducted at Universitas Muhammadiyah Lamongan, and the sample was 46 hospital administration students. There were two instruments that were used in this current research, first in obtaining the LLSs data, the Strategy Inventory for Language Learning (SILL) questionnaire was administered, and then for SES data was gained by using Socioeconomic Status survey. All the data was analyzed by using the statistical analysis, Pearson Correlation. The finding showed that there was no significant correlation between the variables, it can be proved by a correlation coefficient (r = -.095) and a p-value (p = .529), indicating no statistically significant correlation between socioeconomic status and language learning strategies. This suggests that students from different socioeconomic status backgrounds use strategies with similar frequency and manner. Keywords: EFL, Language Learning Strategies, Socioeconomic Status Introduction Learning a language is a complicated and multidimensional process that requires self-control, strategic awareness, and cognitive involvement. In recent years, language learning strategies (LLS) have become increasingly important tools for learners to help them acquire, retain, and use a second or foreign language (Ahsanah, 2020; Ahsanah & Utomo, 2022). This approach is generally divided into two categories: direct and indirect strategies. The direct strategy encompasses memory, cognitive, and compensation. Next, the indirect strategy involves metacognitive, affective, and social (Oxford, 1990). http://u.lipi.go.id/1457703302 mailto:finatyahsanah@umla.ac.id IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 2051 In Oxford’s (1990) taxonomy of language learning strategies, Memory Strategies are categorized as direct strategies, which involve the mental processing of language input. These strategies help learners store, retain, and retrieve information effectively, particularly lexical items (vocabulary), grammatical structures, and expressions. Then, cognitive strategies involve conscious mental processes that learners use to understand, manipulate, or produce the target language. Unlike memory strategies, which focus primarily on storage and recall, cognitive strategies are task-oriented and directly engage with the language material through analyzing, reasoning, or practicing. The last one for direct strategies is compensation strategies; learners use these strategies to compensate for missing knowledge, particularly vocabulary, grammar, or comprehension gaps. They enable learners to continue communication despite limitations, promoting fluency and confidence in real-time language use. Unlike memory or cognitive strategies for learning and practicing language, compensation strategies are applied during performance, especially in speaking and listening contexts. They are especially relevant for learners at beginner and intermediate levels, who frequently encounter situations where they lack full command of the target language. In indirect strategies, there are metacognitive, affective, and social. Metacognitive strategies are part of the indirect language learning strategies in Oxford’s (1990) taxonomy. They refer to the higher-order executive skills that enable learners to manage, control, and evaluate their learning processes. Unlike cognitive strategies that deal directly with the language (e.g., summarizing, translating), metacognitive strategies guide learning by involving planning, monitoring, and self-evaluation. And then, the second one is affective strategies involve managing emotions, motivation, attitudes, and anxiety—all of which are critical in the process of acquiring a new language. Although learning a language is frequently perceived as a cognitive or linguistic pursuit, affective variables have a substantial impact on the long-term success, persistence, and motivation to engage in challenging tasks of learners. Last, yet importantly, is social strategies involving interaction with others to aid language acquisition, such as asking questions, cooperating with peers, and developing cultural understanding. Dealing with LLSs approach: In her book, language learning strategies: What Every Teacher Should Know, Oxford further asserts that numerous factors influence the selection of language learning strategies. These factors include age, gender, language style, and socioeconomic status, among others. All the factors affecting the use of language learning strategies have been extensively investigated and highlighted by numerous language scholars (Abubakar, 2020; Ahsanah, 2020; Ahsanah & Utomo, 2022; Aydog an & Akbarov, 2014; Damanik, 2022; Domí nguez & Juaní as, 2024; Kevin Tam, 2013; Suryanto & Sari, 2021). Finaty Ahsanah, Dias Tiara Putri Utomo, Ririn Indah Prastiwi The Role of Socioeconomic Status in EFL Learning Strategies 2052 However, research concerning the relationship between socioeconomic status and the use of Language Learning Strategies (LLS) is particularly difficult to obtain. The social and economic aspects that may influence learners' strategic behavior have received little attention, even though employing strategies is crucial for successful language learning. Socioeconomic status (SES) is one of the most significant yet little-studied of these factors (Hui & Chen, 2024). A learner's educational experience is greatly influenced by their socioeconomic standing, which is a composite metric that frequently combines parental education, family income, and employment status. Prior studies have shown that socioeconomic status influences academic performance, motivational orientation, access to educational resources, and overall cognitive development (Vonkova et al., 2024). In language learning, learners from higher socioeconomic status families generally have enhanced access to supplemental educational resources, including private tutoring, digital learning tools, and improved home literacy environments. These resources may facilitate more frequent engagement in metacognitive and elaborative methods, resulting in enhanced language acquisition efficiency (Hui & Chen, 2024). In contrast, students from lower socioeconomic status backgrounds may be hindered by insufficient resources, increased cognitive burden from external stressors, and a deficiency in explicit strategy instruction, leading to a dependence on rudimentary or superficial strategies such as memorization or translation (Njeri & Taym, 2024). The connection between socioeconomic status (SES) and language learning strategies is educational and sociocultural. Sociocultural theory posits that learners' cognitive development is shaped by social interactions and contextual resources (Vygotsky, 1891). In this context, socioeconomic status indicates both material and symbolic capital, affecting learners' ability to access, choose, and apply effective learning strategies. Research suggests that strategic awareness and metacognitive regulation are not solely inherent traits but are often developed through social instruction and cultural transmission (Liu & Li, 2023). Stratified learning outcomes may arise from socioeconomic status disparities, which can be reinforced through differentiated strategies, thus perpetuating educational inequities. Despite this relationship's theoretical and practical significance, empirical research remains limited and dispersed across various cultural and educational contexts. A consistent trend has been observed in research conducted in South Korea (Jung et al., 2023), Indonesia (Azzahra & Purnawan, 2025), and the Middle East (Asadi et al., 2024) Learners with a higher socioeconomic status exhibit a greater propensity for self-regulated learning and employ a broader range of language learning strategies. The absence of a cohesive framework in these studies frequently impedes the ability to establish robust causal inferences or generalize findings. IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 2053 Considering the background discussed above, this study is conducted to address the need for a clearer understanding and more concrete findings regarding the influence of socioeconomic status on English language learning. Therefore, this study attempts to comprehensively investigate the impact of socioeconomic status on the frequency and selection of language learning strategies employed by English as a Second Language learners. Two questions have been formulated: 1) What types of English as a Foreign Language (EFL) learning strategies are commonly used by students? 2) Are there significant correlations in the use of learning strategies between students of high and low socioeconomic status in EFL classrooms? Afterwards, the objectives are to identify the types of EFL learning strategies commonly used by students from different socioeconomic backgrounds, and to analyze the differences in learning strategy use between students of higher and lower socioeconomic status. Method This study employed a quantitative research design to examine the relationship between students’ socioeconomic status (SES) and their use of language learning strategies (LLSs) in an English as a Foreign Language (EFL) setting. The participants, selected by using convenience sampling, were 46 undergraduate students of the Hospital Administration major. The research was conducted at Universitas Muhammadiyah Lamongan during the second semester of the 2024/2025 academic year. Socioeconomic status was determined based on students’ self-reported data regarding parental income and occupation, which were then categorized into low, middle, and high SES groups according to national income standards. Table 1. SES Statistical Analysis SES Level Parental Education Family Income (USD/month) Parental Occupation Access to Learning Resources Low No formal or only primary Less than Rp. 1.000.000 Unskilled labor, unemployed, informal Very limited (no books, no internet) Middle Completed secondary/high school Rp. 1.000.000 – Rp. 5.000.000 Skilled labor, small business, and clerical Moderate (some books, shared internet) High University degree or higher More than Rp. 5.000.000 Professionals, managers, officials Full access (books, internet, devices) Table 2. SES Score Category Finaty Ahsanah, Dias Tiara Putri Utomo, Ririn Indah Prastiwi The Role of Socioeconomic Status in EFL Learning Strategies 2054 Total Score SES Category 4 – 6 Low 7 – 9 Middle 10 – 12 High Table 2 presents the classification of participants’ Socioeconomic Status (SES) based on their total SES scores. These scores were derived from a combination of indicators such as family income, parental education level, and parental occupation, which are commonly used in SES assessments in educational research. The scoring system categorizes participants into three distinct SES levels: • Low SES (Score 4–6): This category includes participants who fall into the lower range of socioeconomic indicators. • Middle SES (Score 7–9): This group represents participants whose SES indicators suggest a moderate or average level of socioeconomic stability. • High SES (Score 10–12): Participants in this category are those with the highest total SES scores. They generally come from families with higher income levels, parents with higher education (e.g., undergraduate or postgraduate degrees), and professional occupations. Subsequently, Data on students’ language learning strategies were collected using a modified version of the Strategy Inventory for Language Learning (SILL) developed by Oxford (1990), which was adapted and translated into Bahasa Indonesia to ensure clarity and accessibility. The instrument consisted of 30 items covering six categories of language learning strategies: memory, cognitive, compensation, metacognitive, affective, and social strategies. Students rated their frequency of strategy use on a 5-point Likert scale ranging from “never or almost never true of me” to “always or almost always true of me.” Table 3. SILL Item Samples Categories Sample Items Memory Strategy I use new English words in a sentence so I can remember them Cognitive Strategy I watch English language TV shows spoken or go to movies spoken in English Compensation Strategy I try to guess what the other person will say next in English. Metacognitive Strategy I pay attention when someone is speaking English. Affective Strategy I try to relax whenever I feel afraid of using English. Social Strategy I practice English with other students. IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 2055 To ensure the internal consistency and reliability of the translated instrument, a reliability test was performed using Cronbach’s Alpha. This statistical method helps determine whether the items in the questionnaire consistently measure the intended constructs: Table 4. Reliability Statistics Cronbach's Alpha N of Items .838 30 The reliability test using Cronbach’s Alpha produced a value of 0.838, indicating a high level of internal consistency. This result suggests that the items within the instrument, specifically the translated version of the Strategy Inventory for Language Learning (SILL), are measuring the same underlying construct consistently. A Cronbach’s Alpha value above 0.8 is generally considered acceptable to good in social science research, signifying that the instrument is reliable and can be confidently used to assess language learning strategies among the participants. The strong internal consistency also supports the appropriateness of the translation and adaptation process, indicating that the meaning and intent of the original items were preserved effectively in the Indonesian context. In addition, to find out the successful learners who employed language learning strategies, Oxford’s (1990) a rating scheme for strategy use was being utilized. The range of the rating scheme is 1.0 to 5.0, with the description as follows: Table 5. Strategy Used Rating Scheme Mean Category 1.0 – 2.4 Low 2.5 – 3.4 Moderate/Medium 3.5 – 5.0 High Table 5 outlines the criteria used to categorize the mean scores of participants’ responses regarding their use of language learning strategies. This rating scheme helps interpret how frequently learners apply specific strategies, based on the average scores obtained from the Likert-scale questionnaire (ranging from 1 to 5). • A mean score between 1.0 and 2.4 is categorized as "Low". This range indicates that the participants rarely or almost never use the strategy in question. • A mean score from 2.5 to 3.4 falls under the "Moderate/Medium" category. This suggests occasional use of the strategy. • A mean score from 3.5 to 5.0 is interpreted as "High" usage. This indicates that the learners frequently or always utilize the strategy in their language learning process. Finaty Ahsanah, Dias Tiara Putri Utomo, Ririn Indah Prastiwi The Role of Socioeconomic Status in EFL Learning Strategies 2056 Lastly, the data collected from the questionnaire responses were processed and analyzed using the Statistical Package for the Social Sciences (SPSS), a widely used tool for quantitative data analysis in social science research. To examine the potential relationship between students' socioeconomic status (SES) and their use of language learning strategies, the Pearson product-moment correlation coefficient was applied. This statistical test is appropriate for measuring the strength and direction of the linear relationship between two continuous variables. In this study, the significance level was set at p < 0.05, indicating that any correlation with a p-value below this threshold would be considered statistically significant. In addition to inferential analysis, descriptive statistics such as means and standard deviations were also utilized to provide a clearer overview of the patterns and frequencies of strategy use across participants from various SES backgrounds. This dual approach allowed for both general trend identification and hypothesis testing, contributing to a comprehensive understanding of the data. Results Socioeconomic Status (SES) of the Participants The initial step in the data analysis process was to categorize participants based on their socioeconomic status (SES). As explained in the methodology section, the SES classification was derived from responses to a questionnaire administered to the participants. This questionnaire was specifically designed to capture key indicators that are widely recognized as components of SES in educational research. These included the educational background of the participants’ parents, the family’s monthly income, the type of parental occupation, and the participants’ access to learning resources, particularly the availability of technology that could support their English language learning, such as internet access, smartphones, computers, or educational platforms. Each of these indicators was assigned a specific score, which was then totaled to determine the SES level (low, middle, or high) for each participant. This categorization served as the foundation for further statistical analysis, particularly in examining whether SES is associated with the frequency and type of language learning strategies employed by students. Table 6. Statistical Data of SES Statistics Parents' Education Family Income Parents' Occupation Learning Resources Socioeconomic Status Score N Valid 46 46 46 46 46 Missing 0 0 0 0 0 Mean 1.9783 2.2826 2.2174 2.7391 9.2174 Std. Error of Mean .07283 .07397 .06149 .06546 .17833 Median 2.0000 2.0000 2.0000 3.0000 9.0000 Mode 2.00 2.00 2.00 3.00 9.00 IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 2057 Std. Deviation .49392 .50169 .41703 .44396 1.20946 Variance .244 .252 .174 .197 1.463 Range 2.00 2.00 1.00 1.00 5.00 Minimum 1.00 1.00 2.00 2.00 7.00 Maximum 3.00 3.00 3.00 3.00 12.00 Sum 91.00 105.00 102.00 126.00 424.00 Percentiles 25 2.0000 2.0000 2.0000 2.0000 9.0000 50 2.0000 2.0000 2.0000 3.0000 9.0000 Table 6 presents the descriptive statistical analysis of participants’ socioeconomic status (SES), based on four key indicators: parents’ education, family income, parents’ occupation, and access to learning resources. The analysis includes data from 46 valid responses with no missing values. The mean score for parents’ education was 1.9783, with a standard deviation of 0.49392, indicating that the majority of participants’ parents had attained education at the lower to middle level. The median and mode for this variable were both 2.00, suggesting consistency in the responses. For family income, the mean score was 2.2826, with a standard deviation of 0.50169, indicating that most participants belonged to families in the middle-income category. The median and mode were also 2.00. In terms of parents' occupation, the mean score was 2.2174 with a standard deviation of 0.41703. This relatively low variability suggests that most parents were engaged in similar types of moderately skilled jobs, with a median and mode of 2.00. Meanwhile, learning resources had a slightly higher mean score of 2.7391 and a standard deviation of 0.44396, indicating that most participants had access to essential learning tools such as smartphones, laptops, or internet connectivity. The median and mode for this variable were 3.00, showing that the majority reported high access to learning technologies. The overall SES score was calculated by summing the scores from all four components, resulting in a mean score of 9.2174 and a standard deviation of 1.20946. These average places the participants in the middle SES category based on the predetermined SES classification scale. The SES scores ranged from a minimum of 7.00 to a maximum of 12.00, with a median and mode of 9.00. These results indicate that most participants came from moderately advantaged socioeconomic backgrounds, with relatively consistent SES characteristics across the sample. Language Learning Strategies used by the Participants The next part of the findings focuses on the analysis of the language learning strategies employed by the participants. This section highlights the frequency and types of strategies used, categorized according to Oxford’s (1990) framework, which includes memory, cognitive, compensation, metacognitive, affective, and social strategies. The results are presented through descriptive statistics, allowing Finaty Ahsanah, Dias Tiara Putri Utomo, Ririn Indah Prastiwi The Role of Socioeconomic Status in EFL Learning Strategies 2058 for a clearer understanding of which strategies were most and least frequently applied by learners in their efforts to acquire English as a foreign language. Table 7. LLSs Statistical Data In table 7, it can be seen the statistical data of language learning strategies used by the participants. The mean of the overall score of language learning strategy employed by the participants was 4.04, which means they were higher usage of language learning strategies. Moreover, in all the strategy points also indicated that the participants of the study were also in a high LLSs category; the mean of memory strategies was 3.2, the mean for cognitive strategies was 3.4, the mean for compensation strategies was 3.3, the mean for metacognitive 3.2, the mean for affective strategies was 3.5, and the mean for social strategies was 2.8. Therefore, it can be concluded that the strategies mostly used by the participants were affective, and the least used were social strategies. The Correlation Data of Socioeconomic status on language learning strategies preferences. The relationship between socioeconomic status (SES) and students’ preferences for language learning strategies has been previously discussed in terms of general patterns. In this section, the focus shifts to a more specific analysis: examining whether SES has a significant impact on the selection and frequency of Statistics Language Learning Strategies Score Memory Average Score Cognitive Average Score Compen sation Average Score Metacog nitive Average Score Affective Average Score Social Average Score N Valid 46 46 46 46 46 46 46 Missing 0 0 0 0 0 0 0 Mean 4.0435 3.2609 3.4130 3.3696 3.2826 3.5870 2.8478 Std. Error of Mean .06920 .09027 .09619 .07838 .09662 .09619 .10294 Median 4.0000 3.0000 3.0000 3.0000 3.0000 4.0000 3.0000 Mode 4.00 3.00 3.00 3.00 3.00 4.00 3.00 Std. Deviation .46935 .61227 .65238 .53161 .65534 .65238 .69817 Variance .220 .375 .426 .283 .429 .426 .487 Range 2.00 2.00 3.00 2.00 3.00 3.00 3.00 Minimum 3.00 2.00 2.00 2.00 2.00 2.00 1.00 Maximum 5.00 4.00 5.00 4.00 5.00 5.00 4.00 Sum 186.00 150.00 157.00 155.00 151.00 165.00 131.00 Percentiles 25 4.0000 3.0000 3.0000 3.0000 3.0000 3.0000 2.0000 50 4.0000 3.0000 3.0000 3.0000 3.0000 4.0000 3.0000 IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 2059 language learning strategy use among participants. This analysis aimed to determine whether a statistically meaningful correlation exists between the two variables. To achieve this, a Pearson product-moment correlation test was conducted using the collected data. The results of this correlation analysis are presented and interpreted in the following section. Table 8. Correlation of SES and LLS Correlations Language Learning Strategies Score Socioeconomic Status Score Language Learning Strategies Score Pearson Correlation 1 -.095 Sig. (2-tailed) .529 N 46 46 Socioeconomic Status Score Pearson Correlation -.095 1 Sig. (2-tailed) .529 N 46 46 The significance value (p-value) is a critical indicator in determining whether a relationship between two variables is statistically meaningful. In general, if the sig. (2-tailed) value is less than 0.05, the correlation is considered statistically significant. However, in this study, the sig. value for the correlation between socioeconomic status (SES) and language learning strategy (LLS) use was 0.52, which is notably greater than 0.05. This result clearly indicates that the correlation is not statistically significant. Moreover, the correlation coefficient (r) was calculated to be –0.095, suggesting a very weak and negative relationship between the two variables. This value is close to zero, reinforcing the idea that no meaningful linear association exists between SES and the use of language learning strategies among the participants. Furthermore, the 95% confidence interval for the correlation coefficient ranged from –0.375 to 0.201, which includes zero, confirming that the observed correlation could very likely be due to chance. Taken together, these findings imply that there is no strong evidence to support a connection between students’ socioeconomic backgrounds and the frequency or type of language learning strategies they use. Specifically, among the hospital administration students at Universitas Muhammadiyah Lamongan, those classified under low, middle, or high SES categories reported using language learning strategies in a similar frequency and manner. This suggests that regardless of their economic or social background, students demonstrate comparable approaches and preferences when learning English as a foreign language. Finaty Ahsanah, Dias Tiara Putri Utomo, Ririn Indah Prastiwi The Role of Socioeconomic Status in EFL Learning Strategies 2060 Table 9. The Correlation between SES and Each LLS Strategy LLS TYPE R P-VALUE MEMORY -0.048 0.750 COGNITIVE -0.032 0.834 COMPENSATION -0.024 0.874 METACOGNITIVE -0.051 0.735 AFFECTIVE -0.053 0.728 SOCIAL -0.118 0.435 The table above presents the correlation analysis between socioeconomic status (SES) and the six types of language learning strategies (LLS) as identified by Oxford: memory, cognitive, compensation, metacognitive, affective, and social strategies. The correlation coefficients (r) and corresponding p-values indicate the strength and significance of the relationship between SES and the use of each strategy type. Across all six categories, the correlation values are negative and very weak, ranging from –0.024 to –0.118. This suggests that there is no meaningful linear relationship between socioeconomic status and the use of any specific language learning strategy. For example, the weakest correlation is found in the compensation strategy (r = –0.024) with a p-value of 0.874, indicating almost no relationship. Similarly, the strongest (yet still weak) correlation is observed in the social strategy (r = –0.118), but the p-value of 0.435 shows that this relationship is not statistically significant. All p-values exceed the 0.05 significance threshold, confirming that none of the correlations are statistically significant. These results suggest that students from varying socioeconomic backgrounds, whether low, middle, or high, tend to use language learning strategies in similar ways and with similar frequency. Therefore, socioeconomic status does not appear to play a determining role in the strategic approaches students adopt in learning English in this study. Discussion This discussion section addresses two main aspects in response to the research questions formulated earlier. First, it explores the types of language learning strategies used by the participants, as identified through the Strategy Inventory for Language Learning (SILL). This analysis provides insight into which strategies are most commonly employed by learners in the context of English as a Foreign Language (EFL). Second, the discussion explores the relationship between socioeconomic status (SES) and the use of language learning strategies. This includes an analysis of whether participants' economic and social backgrounds, such as family income, parental education level, and parental occupation, have any influence on the frequency or preference of strategy use in language learning. By addressing both of these areas in depth, this section aims to contribute to a better IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 2061 understanding of the extent to which external factors, such as SES, affect strategic behavior in language learning within the EFL classroom. The Language Learning Strategies Used by the Participants There are six distinct categories or taxonomies of language learning strategies, which are further divided into two sections (Oxford, 1990). The direct strategies consist of three strategies: memory strategy, cognitive strategy, and compensation strategy. Additionally, indirect strategies encompass metacognitive, affective, and social strategies. Although Oxford’s taxonomy identifies six distinct language learning strategies, this study focuses exclusively on one strategy from the direct category, cognitive, and one from the indirect category, affective. This delimitation is intended to streamline the analysis and maintain clarity, given that the participating students demonstrated relatively equal frequency in employing all six strategy types throughout the learning process. In order to share the best understanding of the strategies, the researchers will explain two strategies as an example of the overall LLS, since there was no statistical correlation with it. The first strategy discussed in this section is direct, the cognitive strategy, which relates to the students’ ability to understand and use the language they are learning. This strategy is closely linked to their ability to apply the language in practical contexts. Table 10. Data Analysis on Students’ Response-Cognitive Descriptive Statistics N Minimum Maximum Mean Std. Deviation Cognitive 1 46 2.00 5.00 3.8043 .74891 Cognitive 2 46 2.00 5.00 3.5435 .83550 Cognitive 3 46 2.00 5.00 3.9565 .81531 Cognitive 4 46 1.00 5.00 3.3261 1.13636 Cognitive 5 46 1.00 5.00 2.6087 .99952 Valid N (listwise) 46 The table presents the descriptive statistics for five items categorized under cognitive language learning strategies, based on responses from 46 participants. Each item was rated on a Likert scale ranging from 1 (never or almost never true of me) to 5 (always or almost always true of me). Cognitive 3 has the highest mean score of 3.9565, suggesting that this particular cognitive strategy is the most frequently used among the five. With a standard deviation of 0.81531, the responses are relatively consistent. Cognitive 1 follows closely, with a mean of Finaty Ahsanah, Dias Tiara Putri Utomo, Ririn Indah Prastiwi The Role of Socioeconomic Status in EFL Learning Strategies 2062 3.8043, indicating frequent use. It also has a relatively low standard deviation (0.74891), suggesting low variability in how students responded to this item. From this data, it can be concluded that not all cognitive strategies are equally preferred or used by students. Strategies represented by Cognitive 1 and 3 are generally more favored and consistently applied, while strategies like Cognitive 5 are used less frequently and inconsistently. These differences highlight the importance of examining individual strategy items rather than general category scores, as they reveal specific learner preferences and tendencies in language learning behavior. Let’s briefly break down the participants' responses towards those two mostly used cognitive strategies: Figure 1. Student’s response to cognitive strategy 1 Figure 2. Student’s response to cognitive strategy 3 Figures 1 and 2 illustrate the students’ responses to the use of cognitive strategies. Figure 1 shows their responses to Cognitive Strategy Statement 1, which is related to practicing their English skills by repeatedly reading texts. A total of 47.8% of the students reported that they often engage in this activity. Figure 2 presents the students’ responses to Cognitive Strategy Statement 3, which involves translating English into Indonesian or vice versa to help them learn English more effectively. Approximately 47.8% of the students reported that they often use this strategy, while 26.1% stated that they always or almost always use it. The next strategy to be used as example and discussed is one from the indirect strategy category, namely the affective strategy. This strategy is closely related to the learners’ ability to manage their emotions while learning the target language. Affective strategies play a crucial role in reducing anxiety, increasing motivation, IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 2063 and fostering a positive attitude toward language learning. Table 11. Data Analysis on Students’ Response-Affective Descriptive Statistics N Minimum Maximum Mean Std. Deviation Affective 1 46 1.00 5.00 3.6087 .82941 Affective 2 46 1.00 5.00 3.3913 1.16387 Affective 3 46 2.00 5.00 3.5652 .83406 Affective 4 46 2.00 5.00 3.7391 .82825 Affective 5 46 1.00 5.00 3.4565 .95932 Valid N (listwise) 46 The table displays descriptive statistics for five items categorized as affective strategies, which relate to managing emotions, motivation, and attitudes in language learning. Affective 4 has the highest mean score at 3.7391, indicating it is the most frequently used affective strategy among participants. With a standard deviation of 0.82825, responses are relatively consistent, meaning most participants had similar experiences or usage of this strategy. Affective 1 (mean = 3.6087) and Affective 3 (mean = 3.5652) also show relatively high levels of use, with low standard deviations (0.82941 and 0.83406, respectively). This indicates that learners regularly engage in the strategies represented by these items, and there's not much variation among students. Let’s briefly break down the participants' responses towards those two mostly used affective strategies: Figure 3. Student’s response to affective strategy 1 Figure 3 presents an effective strategy that is closely associated with how students regulate their emotions while speaking English. Specifically, this strategy reflects their tendency to remain calm when placed in situations that require them Finaty Ahsanah, Dias Tiara Putri Utomo, Ririn Indah Prastiwi The Role of Socioeconomic Status in EFL Learning Strategies 2064 to communicate in the target language. The data indicate that 54.3% of the participants (24 students) reported using this strategy frequently, while 8.7% (4 students) stated that they always employed it. Figure 4. Student’s response to affective strategy 4 Figure 4 illustrates an effective strategy related to how students motivate themselves in learning English. One of the methods employed involves listening to music and watching content in English as a means of increasing their motivation to learn. The data reveal that 52.2% of the students reported using this strategy frequently, while approximately 15.2% (7 students) stated that they always or almost always engaged in this practice. Based on the descriptive statistical analysis of both cognitive and affective language learning strategies, it can be concluded that students generally employ these two categories of strategies with moderate to high frequency. Among the cognitive strategies, items such as Cognitive 3 and Cognitive 1 received the highest mean scores, indicating that learners tend to frequently engage in activities that involve practicing, analyzing, or transforming the language. However, some cognitive strategies (e.g., Cognitive 5) showed lower mean values and higher variability, suggesting less frequent and more inconsistent use across participants. In terms of affective strategies, students also reported regular use, particularly Affective 4, which had the highest mean among all affective items. This reflects an awareness of the importance of managing emotions and motivation during the learning process. Nonetheless, Affective 2 displayed a high standard deviation, indicating notable variation in how learners apply emotional regulation strategies. Overall, the data suggest that while students are actively using both cognitive and affective strategies, certain strategies are clearly favored over others. This reflects the possibility that students gravitate toward strategies they perceive as more effective or easier to use. These findings highlight the need for targeted strategy training in classrooms to ensure that learners are aware of and able to use a broader and more balanced range of strategies, especially those less frequently employed. IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 2065 The Relationship between Socioeconomic Status and Language Learning Strategies Preferences. This study explored the potential relationship between socioeconomic status (SES) and the use of language learning strategies (LLS) among students learning English as a foreign language (EFL). Drawing from a sample of 46 students, the study utilized the Strategy Inventory for Language Learning (SILL) developed by Oxford (1990) to measure the frequency and type of strategies used, and examined correlations between these strategies and SES indicators, including family income, parental education, and occupational background. Contrary to the assumption that learners from higher socioeconomic backgrounds tend to use language strategies more frequently or effectively, the findings revealed no statistically significant relationship between SES and any of the six categories of LLS (memory, cognitive, compensation, metacognitive, affective, and social). The absence of a significant relationship between SES and LLS suggests that strategy use may be equally distributed across students from varying economic and educational backgrounds. This challenges common assumptions in educational theory and practice that socioeconomic advantage automatically translates into better learning behaviors or higher levels of learner autonomy. Instead, the findings imply that internal learner factors, such as motivation, awareness, personal initiative, and exposure to strategy instruction, may be more influential in determining the use of learning strategies than external factors like income or parental education. This interpretation is further supported by the demographic data of the participants. The majority of students came from lower-middle to low-income households, with 67.4% reporting family incomes between Rp. 3.000.000 and Rp. 4.999.999 per month, and 21.7% between Rp. 1.000.000 and Rp. 2.999.999. Furthermore, a significant portion of parents had only completed basic education, with no participants reporting parental education beyond high school. From a traditional SES lens, these factors could suggest potential limitations in academic support, educational culture at home, and access to enriched language environments. However, the actual use of strategies reported by students appears unaffected by these limitations. An important factor in understanding this result lies in the widespread access to learning technology among participants. An overwhelming majority reported having access to smartphones (93.5%) and laptops (87%), indicating that despite financial constraints, students were still able to engage with digital learning tools. This technological access could serve as a buffering factor, enabling students from lower SES backgrounds to explore, practice, and reinforce language learning strategies independently. With mobile applications, online dictionaries, interactive exercises, and video-based learning platforms readily available, students can be empowered to develop strategic behaviors regardless of their family background. Finaty Ahsanah, Dias Tiara Putri Utomo, Ririn Indah Prastiwi The Role of Socioeconomic Status in EFL Learning Strategies 2066 The results of this study align with those of Hui and Chen (2025), who examined the mediating role of English learning motivation between SES and pragmatic awareness in Chinese EFL learners. In their study of 292 participants, Hui and Chen found that SES did not significantly affect motivation or language awareness, concluding that internal learner characteristics were stronger predictors of success than economic background. The consistency between their findings and the current study strengthens the argument that SES may not directly influence cognitive or metacognitive dimensions of language learning, including how students plan, monitor, and evaluate their learning, or how they use social and affective strategies to enhance performance. The findings of this research contribute to our understanding of learner autonomy and self-regulated learning in EFL contexts. As Oxford (1990) noted, strategy use is not solely determined by environmental or social factors but also by a learner’s intentionality and awareness. The lack of SES-based differences in strategy use observed in this study supports the theory that language learning is a highly individual process, where strategic behavior reflects internal decisions more than external limitations. This suggests that strategy instruction in classrooms can be equally effective across diverse socioeconomic groups if it is clearly modeled, practiced, and supported by teachers. Moreover, the study adds to the growing body of research emphasizing the role of technological equity in education. The high rate of digital device ownership, even among students from low-income families, may indicate a shift in the way SES influences learning, from access to content toward the quality of instruction and learner engagement. As a result, future research and practice may benefit from placing less emphasis on SES as a predictor of strategy use and more focus on educational quality, teacher feedback, and student motivation. Conclusion The data presented in the socioeconomic profile of the participants highlight a predominantly lower- to middle-income population, with most coming from families with modest educational backgrounds. However, despite these limitations, a striking majority of students reported access to essential learning technologies, particularly smartphones and laptops. This accessibility appears to mitigate the potential disadvantages commonly associated with low socioeconomic status. The findings of this study, which revealed no significant correlation between SES and the use of language learning strategies, suggest that economic background alone may not determine how learners engage with strategic language learning. Instead, the widespread availability of digital tools and possibly school-based support may play a more equalizing role, allowing students from diverse socioeconomic backgrounds to apply similar strategies in learning English. These results reinforce the importance of focusing on learner motivation, autonomy, and access to technology as key factors in supporting effective language acquisition across all SES levels. Some theoretical implications have been drawn in this current IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 2067 study: For educators and curriculum developers, these findings suggest that language learning strategies can and should be taught across all SES groups, without the assumption that students from lower-income families are at a disadvantage in this area. Schools should continue to integrate strategy-based instruction as part of their regular English curriculum, ensuring that all learners, regardless of background, have equal access to the tools and techniques that foster autonomy and language proficiency. Additionally, institutions should leverage the high rate of smartphone and laptop access to create more inclusive digital learning environments. Mobile-assisted language learning (MALL) platforms, interactive apps, and online strategy training modules can be integrated into lessons to further support strategy development among students with limited home resources. Despite its contributions, this study has several limitations. First, the sample was limited to a specific group of students, hospital administration majors enrolled in an Intensive English Class, at a single institution, which may limit the generalizability of the findings to broader populations. Second, the study relied solely on self-reported data through questionnaires, which may be subject to bias such as social desirability or inaccurate self-assessment. Additionally, the study did not explore other potentially influential variables, such as access to educational support, parental involvement, or teacher practices, which might mediate or moderate the relationship between SES and language learning strategies. 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