Gender Difference and Errors in Writing Narrative Texts among Indonesian EFL College Students

This study aims at identifying and analyzing linguistic and textual errors in writing narrative texts committed by male and female students of the fourth-semester English department students. The focus is on finding the significant differences between male and female students in terms of the frequencies of linguistic and textual errors in their narrative texts and the factors contributing to the errors. Data were collected through writing tests. Data were analyzed and interpreted by quantitative descriptive analysis and multivariate analysis using Two Ways ANOVA (F-Test) to investigate the significant differences linguistic and textual errors. Then the types of linguistic and textual errors in the two groups of male and female students were compared using descriptive analysis and statistical analysis. The results show that 57.93% of linguistic errors occurred in female’s narrative writing, with 42.07% occurred in male’s. Meanwhile, 46.29% of textual errors occurred in female’s narrative writing, with 53.71% found in the male’s. Statistically, there was no significantly different interaction between male and female students in terms of textual error frequency in writing narrative text, with p = 0.975 (p > 0.05) at the level of significance a = 5 %. And there was a significantly different interaction between male and female students in terms of linguistic error frequency in writing narrative texts, with p = 0.029 (p < 0.05) at the level of significance a = 5%. Several factors that affect female and male students’ errors in their narrative text are intralingual interference, language transfer, lack of knowledge about the narrative text, and gender difference.

This study aims at identifying and analyzing linguistic and textual errors in writing narrative texts committed by male and female students of the fourth-semester English department students. The focus is on finding the significant differences between male and female students in terms of the frequencies of linguistic and textual errors in their narrative texts and the factors contributing to the errors. Data were collected through writing tests. Data were analyzed and interpreted by quantitative descriptive analysis and multivariate analysis using Two Ways ANOVA (F-Test) to investigate the significant differences linguistic and textual errors. Then the types of linguistic and textual errors in the two groups of male and female students were compared using descriptive analysis and statistical analysis. The results show that 57.93% of linguistic errors occurred in female's narrative writing, with 42.07% occurred in male's. Meanwhile, 46.29% of textual errors occurred in female's narrative writing, with 53.71% found in the male's. Statistically, there was no significantly different interaction between male and female students in terms of textual error frequency in writing narrative text, with p = 0.975 (p > 0.05) at the level of significance a = 5 %. And there was a significantly different interaction between male and female students in terms of linguistic error frequency in writing narrative texts, with p = 0.029 (p < 0.05) at the level of significance a = 5%. Several factors that affect female and male students' errors in their narrative text are intralingual interference, language transfer, lack of knowledge about the narrative text, and gender difference.

Keywords:
Gender Writing Narrative Text Textual error Linguistic Error writing skill, some previous studies have contested each other on whether there are differences in writing as a language skill between male and female. The studies and literature on gender and ability in composition writing point to differing results. In some areas, the results indicated that there were no significant differences between girls and boys in writing. Jewell and Malecki state girls significantly outperformed boys on all written expression fluency measures [4]. Based on their findings, girls in the sample wrote more accurately, spelled words correctly, and wrote writing sequences correctly than boys did. However, neither the production-independent nor accurate production indices revealed gender differences. To put it another way, they discovered that males and females wrote with the same level of accuracy, despite the fact that females wrote more than males.
In English text, there are some different types of texts or genres that each type has different characteristics, form, and function. Such as narrative, descriptive, recount, report, transactional conversation, anecdotes, interpersonal conversation texts, and procedure texts. Regarding to the narrative text at the university level, narrative texts have not been taught. There is no subject of narrative texts in their lesson. In the writer's previous study, some errors were found in students' narrative writings and there were some factors contributing to the errors such as: lack of students' knowledge about grammatical rules, interlingual interference, and narrative text [5]. However, gender as one of the factors that might contribute to the students' errors has not received much attention from the previous researchers. Other previous studies have investigated gender differences in performance in various field of study both in natural science and social science [6] [7] [8] [9] [10] [11].
It is worth noting that while these studies have found evidence for gender differences in performance, they have also been subject to criticism and debate. Therefore, it is difficult to generalize the most common linguistic errors made by female EFL students in writing English text, as it can vary depending on individual students and their language backgrounds. However, some common errors that EFL students in Indonesia and other countries, including females, tend to make in their writing include: Spelling errors that may include misspelling commonly used words or using incorrect spellings for homophones, such as "their" and "there". Grammar errors may include using the incorrect verb tenses, subject-verb agreement, or pronoun usage. Vocabulary errors that may include using incorrect or inappropriate words, using words in the wrong context, or failing to use words that are necessary to convey meaning. Syntax errors may include errors with sentence structure, such as using run-on sentences or sentence fragments. Punctuation errors may include incorrect use of commas, periods, and other punctuation marks [12] [13] [14] [15] [16] [17]. It is crucial to remember that anyone studying English as a foreign language is capable of making these mistakes. By giving students clear training in grammar, syntax, and punctuation as well as opportunities to practice their writing abilities and receive feedback on it, teachers may assist their students in overcoming these problems.
In line with the background of the study above, it is of a great importance to conduct a study that seeks to investigate and analyze the linguistic errors i.e. the grammatical rules and textual errors in relation to the generic structures in the narrative texts written by male and female students. In addition, this study analyzed some possible factors affecting both errors.

II. Method
This research used a mixed methods design in which data were analyzed both quantitatively and qualitatively. The samples of the study were third-semester English department students that consisted of 78 students of 3 classes.
For data collection, we used three instruments of data collection that include written production test in the form of narrative text, the students' retrospective interview, and a focused group discussion. First, in dealing with the data collection that aimed to find the differences between male and female students in terms of the frequencies of linguistic and textual errors in their narrative texts, we randomly selected the samples using a random sampling technique and then evenly grouped the samples based on their gender with the task of composing a narrative text. Their works were then analyzed to find the number of errors that occurred in the texts based on linguistic errors and textual errors types and subtypes. Second, retrospective interview is the type of interview that involved collecting data about past events. We interviewed some students representative of each gender group. Finally, we conducted a focused group discussion with some of the students from both groups. Both retrospective interview and focused group discussion were used to clarify the results of the data collected and also for additional information about possible factors contributing to errors made by male and female students in writing narrative text.
Validity testing was used to test content validity by comparing the contents of the instrument with the subject matter that had been taught. In this case, we were certain that the samples had not previously studied in detail how to write a narrative text. Thus, allowing the emergence of students' error in writing narrative texts. In this study there were two items that were tested, that is, linguistic errors and textual errors.

Validity testing criteria:
If the rcount > rtable, means the item is a valid statement If the rcount < rtable, means the item is not a valid statement The reliability test was used to ascertain that the data gathered were reliable and consistent. This was obtained through double-checking the obtained data for three times on different occasions.
The reliability test was calculated using Cronbach's Alpha analysis.

Reliability testing criteria:
If rcount > rtable, means that the data is reliable If rcount < rtable, means that the data is not reliable We conducted four procedures of error analysis that included identification of errors, classification of errors, description of errors, and explanation of errors. In order to test the hypotheses, we formulated the following hypotheses: a. H0 : GL = 0 H1 : GL ≠ 0 b. H0 : GT = 0 H1 : GT ≠ 0

III. Results and Discussion
This section begins with the results of the study that display a comprehensive exploration of error types, commencing with an enlightening overview accompanied by illustrative examples. We then delve into the assessment of error levels or quantities, examining the methods employed to analyze and quantify these linguistic error and textual errors. Furthermore, we embark on a rigorous investigation through statistical testing using the SPSS tool, aiming to unveil the significance of gender differences in relation to errors observed in narrative writing. Lastly, in the discussion section, we critically compare and contrast our findings with prior studies, illuminating the broader context and contributing to the collective understanding of this captivating subject matter.

Linguistic Error Types
Within this section, we delve into the realm of linguistic errors present in both male and female narrative writing. Initially, we showcase a collection of illustrative examples that highlight these linguistic errors. Subsequently, we provide comprehensive explanations for each error category encountered. To facilitate clarity and comprehension, Table 1 is presented, utilizing distinct colors to visually indicate specific error types. The addition error is represented by the orange color, the misformation error by the purple color, the omission error by the green color, and the misordering error by the pink color. This visual aid aids in distinguishing and identifying the different types of errors, enriching the overall understanding of linguistic challenges within narrative writing. The preceding table provides a comprehensive display of linguistic errors made by both male and female students, accompanied by valuable feedback for each error. Utilizing the samples of errors presented, we have classified them into two distinct categories: linguistic errors and textual errors. Within linguistic errors, we have further categorized them into four sub-types: omission, addition, misformation, and misordering. On the other hand, textual errors are divided into four sub-types: orientation, complication, resolution, and re-orientation. Building upon this categorization, we have described the errors using Brown's (1980) taxonomy of linguistic errors, thereby providing a solid framework for analysis and understanding.

Omission Error
Omission error is characterized by the absence of an item that must appear in a well-formed utterance. The examples of the female and male students' errors are shown in sentences (1)

Addition Error
Addition error is characterized by the presence of a linguistic item which should not appear in a well-formed and interference by native language as shown in sentences (4), (5), and (6) below.
4. Addition of plural marker: -s/-es *She always asked Cinderella to clean their house, cook, and all of the houseworks. 5. Addition of preposition: (to, with, at) *The prince married with Cinderella. 6. Addition of redundant verb *After finished helped her sisters, Cinderella came back to her room.

Misformation Error
Misformation error is characterized by the use of the wrong form of the morpheme or structure. The error was interference by the first language as shown in sentences (7), (8), and (9)

. Misordering Error
Misordering error is indicated by false placement of certain morphemes or group of morphemes as shown in sentences (10a-b), (11a-b), (12), and (13) below. 10. Noun modifier a. *That was a beautiful dress party. b. *It also made a pair of shoe glass. 11. Misordered of pronoun she/her. a. *Her was she step sister. b. *She lived together with her step mother and two her step sisters. 12. Misordered of relative pronoun 'who' *Who the girl matched with the glass shoe. 13. Verb modifier *They lived in the house of own cinderella's father.

Textual Error Types
There were four types of textual errors found in the dataset, these are, orientation error, complication error, resolution error, and re-orientation (comment/value) error.

Orientation Error
Orientation error presents the setting of the story, commonly used opening phrases like 'one day', 'once upon a time', 'long ago' and used past tense to inform the events in the past time as seen in sentences (14) and (15): 14. Using present tense to tell a story in the past time *Long ago, there is a beautiful woman in Lombok. She is a princess of Lombok, everyone amaze with her beauty and respected her. 15. The use of opening phrase *-------,the story start with a thousand fireworks exploded the sky. in a very small village in nowhere.

Complication Error
Complication error explains the problem or conflict that happening to the character as shown in (16). It commonly used time connectives like 'first, 'then', 'but', and 'finally' to keep the continuity among the problems. 16. The use of time connectives *He went everywhere -----found his true love. He tried to get that women with his richness .-----, it didn't work to that girl.

Resolution Error
The resolution error pertains to the concluding part of a story, where the conflicts are resolved and the narrative reaches its final stage. This phase often includes the utilization of closing phrases such as 'finally' to bring the story to a close, as exemplified in sentence (17) below.
17. The use of closing phrases *-----, after her speech. She jumped into the sea and suddenly dissapeared. All of the people was so sad because of her decision" 4.

Re-orientation Error
Re-orientation (comment/value) error is the alteration that happens to the character in the story or moral value in the story. It is an optional part or aspect in one story and it is not influential to the contents of paragraph.

Cumulative Count of Linguistic Errors between Genders
The linguistic error level is depicted in Table 2, illustrating the cumulative count of linguistic errors found in the narrative text writing of both female and male students.
The important finding from Table 2 reveals the cumulative count of linguistic errors based on gender. The data indicates that among the total 580 errors analyzed, 336 errors (57.93%) were made by female students, while 244 errors (42.07%) were made by male students. This finding highlights a higher prevalence of linguistic errors among female students compared to their male 24 counterparts. It suggests the need for targeted interventions and support to address and improve linguistic proficiency in narrative writing, particularly for female students. Additionally, this finding underscores the significance of considering gender differences in language studies and pedagogical approaches to foster balanced language development among students.

Distribution of Linguistic Errors between Genders
Displayed in Table 3 ( Figure 1) below are the percentages representing the occurrence of linguistic errors made by both female and male students in narrative text. The table provides a detailed breakdown of the percentages of linguistic errors found in the narrative texts of female and male students. It is divided into two columns representing the different error subtypes and two rows representing the gender distribution. The first column denotes the subtypes of errors, namely "Omission," "Addition," "Misformation," and "Misordering." The second and third columns display the respective frequencies and percentages of these errors made by female students, while the fourth and fifth columns represent the same information for male students.  Upon examination, it can be observed that among the errors made by female students, the most common subtype is "Omission" with a frequency of 150 (44.64%) followed by "Misformation" with a frequency of 121 (36.01%). On the other hand, male students' most frequent error subtype is "Misformation" with a frequency of 112 (45.90%), followed by "Omission" with a frequency of 88 (36.07%). Furthermore, the table presents the overall totals, indicating that out of the 336 linguistic errors made by female students, "Omission" accounts for 44.64%, "Addition" for 17.26%, "Misformation" for 36.01%, and "Misordering" for 2.08%. Similarly, out of the 244 linguistic errors made by male students, "Omission" represents 36.07%, "Addition" represents 16.80%, "Misformation" represents 45.90%, and "Misordering" represents 1.23%. This table provides a comprehensive breakdown of the linguistic errors made by female and male students, shedding light on the specific subtypes of errors and their respective frequencies and percentages for each gender. It serves as a valuable reference for understanding the distribution and patterns of linguistic errors in narrative texts among students of different genders.

Cumulative Count of Textual Errors between Genders
As in linguistic errors, there were four categories of textual errors on female and male students' narrative text that found in this research: orientation, complication, resolution, and re-orientation (comment/value). The following table shows the percentages of textual errors on female and male students' narrative text. The important findings from Table 4 present the total number of textual errors found in the narrative texts of female and male students. The data reveals that out of the total 54 textual errors analyzed, female students made 25 errors (46.29%), while male students made 29 errors (53.71%). This indicates that both female and male students exhibited a relatively similar occurrence of textual errors in their narrative writing, with a slightly higher percentage observed among male students.

Distribution of Textual Errors between Genders
The important findings from Table 5 ( Figure 2) reveal the percentage distribution of textual errors in the narrative writing of female and male students, categorized by subtypes.

. Distribution of textual errors in the female and male students' narrative writing (by subtypes).
Examining the data, it can be observed that among female students, the most prevalent subtype of textual error is "Resolution" with a frequency of 11 (44.00%), followed by "Re-orientation" with a frequency of 7 (28.00%). "Orientation" has the lowest frequency with only 2 errors (8.00%), while "Complication" accounts for 5 errors (20.00%). Similarly, among male students, the most common subtype is "Resolution" with a frequency of 11 (37.93%), followed by "Re-orientation" with a frequency of 9 (31.03%). "Complication" and "Orientation" have frequencies of 6 (20.69%) and 3 (10.34%) errors, respectively. It can be observed from these findings that both female and male students exhibit similar patterns in the distribution of textual errors across the different subtypes. The "Resolution" and "Re-orientation" errors are consistently observed as the most prevalent among both genders, while "Complication" and "Orientation" errors show a relatively lower occurrence.

Contrasting Gender Difference and the Linguistic and Textual Errors of Narrative Writing
In examining the linguistic and textual errors in narrative writing, this study delves into the contrasting differences between female and male students. To analyze and highlight these distinctions, a comprehensive list of contrasting errors was employed, providing valuable insights into the specific errors made by each gender as shown in Table 6 below. The very important findings from Table 6 provide a comprehensive comparison of linguistic and textual errors made by female and male students in their narrative writing. 1. Linguistic Errors:  The most common linguistic error subtype among both female and male students is "Misformation," with frequencies of 121 (36.01%) and 112 (45.90%) respectively.  "Omission" errors are more prevalent among female students, accounting for 150 errors (44.64%), compared to 88 errors (36.07%) among male students.  "Addition" errors show a slightly higher occurrence among female students (58 errors, 17.26%) compared to male students (41 errors, 16.80%).  "Misordering" errors are relatively infrequent for both genders, with female students making 7 errors (2.08%) and male students making 3 errors (1.23%).

Textual Errors:
 In terms of textual errors, "Resolution" is the most frequent subtype for both female and male students, with frequencies of 11 (44.00%) and 11 (37.93%) respectively.  Both genders demonstrate similar percentages in the occurrence of "Orientation" errors, with female students making 2 errors (8.00%) and male students making 3 errors (10.34%).  "Complication" errors show comparable percentages among both female students (5 errors, 20.00%) and male students (6 errors, 20.69%).  "Re-orientation" errors also exhibit similar percentages, with female students making 7 errors (28.00%) and male students making 9 errors (31.03%). These findings shed light on the similarities and differences between female and male students regarding their linguistic and textual error patterns. The comparison highlights that both genders commonly struggle with "Misformation" errors. However, female students tend to make more "Omission" errors, while male students make more "Misformation" errors. Regarding textual errors, both female and male students frequently encounter "Resolution" errors. The percentages of other textual error subtypes are relatively similar for both genders.

Testing the Hypotheses
The primary objective of this present study is to gain invaluable insights into the unique patterns and tendencies that may exist between male and female students, ultimately illuminating the contributing factors to variations in their writing abilities. To rigorously examine this phenomenon, we put forth two competing hypotheses: the null hypothesis proposes the absence of a significant gender difference in error occurrence, while the alternative hypothesis suggests the presence of a significant gender difference in error occurrence among Indonesian EFL college students.
1. Null Hypothesis (H0): There is no significant gender difference in the occurrence of errors in writing narrative texts among Indonesian EFL college students. 2. Alternative Hypothesis (H1): There is a significant gender difference in the occurrence of errors in writing narrative texts among Indonesian EFL college students. To test these hypotheses, this present study employed the SPSS for data analysis and statistical testing. By leveraging the capabilities of SPSS, rigorous statistical analyses to examine the extent of the gender difference in error occurrence within the writing of narrative texts among Indonesian EFL college students could be conducted. The utilization of SPSS enables researchers to explore the data comprehensively, providing empirical evidence to either accept or reject the null hypothesis and substantiate the presence of a significant gender difference indicated by the alternative hypothesis.
For information, the ANOVA table is divided into several columns with specific information. 1. Source: This column lists the various sources of variation in the model, including the "Corrected Model," "Intercept," "G" (factor G), "L" (factor L), "G * L" (interaction between factors G and L), "Error," "Total," and "Corrected Total" rows as follows:  The "Corrected Model" row provides information about the overall model fit. It shows that the model accounts for a significant amount of the variation in linguistic errors.  The "Intercept" row represents the effect of the intercept term, which is the baseline or reference level for the model.  The "G" and "L" rows represent the main effects of factors G and L, respectively.  The "G * L" row represents the interaction between factors G and L.  The "Error" row shows the variation that remains unexplained by the model. It represents the random or error variation in the data.  The "Total" row indicates the total variation in the dependent variable.  The "Corrected Total" row shows the total variation corrected for the degrees of freedom used in the model.  The note "a. R Squared = 0.000 (Adjusted R Squared = 0.000)" below the table indicates the goodness of fit of the model. The R-squared value represents the proportion of variance in the dependent variable explained by the model, while the adjusted R-squared value accounts for the number of predictors and degrees of freedom in the model. Degrees of freedom represent the number of independent pieces of information used to estimate a parameter. 4. Mean Square: This column displays the mean square, which is calculated by dividing the sum of squares by the degrees of freedom. It represents the average amount of variation explained per degree of freedom. 5. F: This column shows the F-statistic, which is a ratio of two mean squares. It is used to test the significance of each source of variation. 6. Sig.: This column presents the significance level (p-value) associated with each F-statistic.
The significance level indicates the probability of obtaining the observed results by chance alone. A significant result (p < 0.05) suggests that the factor has a significant effect on the dependent variable. Table 7 below is an ANOVA table that presents statistical results related to linguistic errors that examines the effects of different factors on the frequency of these errors, using a between-subjects design. The dependent variable in this analysis is "Frequency."

F-test of Linguistic Errors
The "Corrected Model" row providing information about the overall model fit shows that the model accounts for a significant amount of the variation in linguistic errors, as indicated by the significant F-statistic (7.228) and the extremely low p-value (0.000). The "Intercept" row, representing the effect of the intercept term as the baseline or reference level for the model, has a significant effect on the dependent variable, as indicated by the high F-statistic (283.748) and the very low p-value (0.000). The "G" and "L" rows representing the main effects of factors G and L, respectively, have a significant effect on the dependent variable, as indicated by the significant Fstatistics and low p-values. Meanwhile, the "G * L" row, representing the interaction between factors G and L, also has a significant effect on the dependent variable, as indicated by the significant F-statistic and a p-value of 0.029. Finally, the note "a. R Squared = 0.372 (Adjusted R Squared = 0.321)" indicates the goodness of fit of the model. The R-squared value (0.372) represents the proportion of variance in the dependent variable explained by the model, while the adjusted R-squared value (0.321) accounts for the number of predictors and degrees of freedom in the model.
Based on the provided data, the hypotheses associated with all the factors (intercept, G, L, and the interaction between G and L) are accepted because their corresponding F-statistics are significant. In other words, it is statistically proven that "there is a significant gender difference in the occurrence of errors in writing narrative texts among Indonesian EFL college students". Table 8 is another ANOVA table that presents statistical results related to textual errors. The table examines the effects of different factors on the frequency of these errors, using a betweensubjects design. The dependent variable in this analysis is "Frequency."

F-test of Textual Errors
The "Corrected Model" row providing information about the overall model fit shows that the model accounts for a significant amount of the variation in textual errors, as indicated by the significant F-statistic (8.432) and the extremely low p-value (0.000). The "Intercept" row representing the effect of the intercept term has a significant effect on the dependent variable, as indicated by the high F-statistic (134.832) and the very low p-value (0.000). The "G" and "T" rows represent the main effects of factors G and T, respectively. The factor T has a significant effect on the dependent variable, as indicated by the significant F-statistic (18.552) and the low p-value (0.000). However, factor G does not have a significant effect, as its F-statistic (1.200) is not significant (p-value = 0.276). The "G * T" row representing the interaction between factors G and T does not have a significant effect on the dependent variable, as indicated by the non-significant F-statistic (0.120) and the high p-value (0.975). Finally, the note "a. R Squared = 0.387 (Adjusted R Squared = 0.341)" indicates the goodness of fit of the model. The R-squared value (0.387) represents the proportion of variance in the dependent variable explained by the model, while the adjusted R-squared value (0.341) accounts for the number of predictors and degrees of freedom in the model. Based on the information provided in the ANOVA table, it can be determined whether the hypotheses associated with the factors in the model are accepted or rejected. The hypotheses associated with the intercept term and factor T (textual errors) are accepted because their corresponding F-statistics are significant. However, the hypothesis associated with factor G (gender difference) and the interaction between factors G and T are rejected because their F-statistics are not significant.

B. Discussion
The results provide information about the frequency and percentage of errors for each gender category in terms of linguistic errors and textual errors. In textual errors, female students committed fewer errors compared to male. Of a total of 54 textual errors, 46.29% errors were found in the narrative texts of female students, while 53.71% errors were found in male students'. This supports the findings by Hyde and Linn [17] that claims females have slight advantages in reading, speaking, writing, and general verbal ability. The results also support the theory proposed by Hills (2000: 8) that other gender differences include the tendency of males to use more justifiers and references to quantity or place more than females do. Males are also more likely than females to convey 'their opinions, and use judgmental phrases, action verbs, grammatical errors, contradictions, and rhetorical questions'. Male tend to make errors in misformation because they did not pay too much attention on grammatical rules in writing and this result was supported by the theories proposed above.
In linguistic errors, the results also support Hyde and Linn's findings [17] that suggested that female students made a higher number of linguistic errors compared to male. Female students accounted for the majority of linguistic errors (57.93%), with a slightly higher percentage compared to male students (42.07%).
However, the results of linguistic errors contradict Kamari et al. [18]; Nosrati and Nafisi [19], and Furtina et al. [20] findings that reported that the female students made fewer grammar errors than the male students in writing and spelling that involved subject-verb agreements, verb form, singular/plural form, preposition, conjunction, pronoun, and article.

IV. Conclusion
These findings highlight the importance of targeted interventions and instructional strategies that address the specific areas where students, both female and male, tend to make errors. By recognizing the gender-specific patterns in linguistic and textual errors, educators and curriculum designers can tailor their approaches to effectively improve the narrative writing skills of students. It is crucial to acknowledge that female students may benefit from interventions focused on reducing "Omission" errors, while male students could benefit from strategies aimed at minimizing "Misformation" errors. By understanding these patterns, educators can provide targeted support and instruction that addresses the specific needs of each gender. Furthermore, the distribution of textual errors suggests the significance of enhancing skills related to orientation, complication, resolution, and re-orientation in narrative texts for all students. This implies that instructional interventions should concentrate on improving textual proficiency for both genders. By considering these findings, educators and curriculum designers can develop strategies that provide appropriate guidance to students, facilitating their development of strong competencies in structuring and organizing narratives effectively.
Addressing and rectifying textual errors can contribute to improving the overall quality and coherence of students' narrative writing, ultimately enhancing their communication and storytelling skills. Ultimately, understanding the distribution of errors by subtype enables educators to personalize their teaching strategies to target the specific areas where students struggle the most. This knowledge empowers educators to deliver more effective instruction and promote improved narrative writing abilities for students of all genders. It is important to consider that other factors or additional analyses may provide further insights into the relationship between gender and error occurrence. Additionally, examining a larger and more diverse sample could yield more conclusive findings regarding gender differences in error occurrence among Indonesian EFL college students. Technology-assisted interventions such as AI-based corrective writing analysis tools can provide personalized feedback and support for students in addressing linguistic and textual errors in their narrative writing.