On the Influence of Stroke on Willingness to Attend Classes and Foreign Language Achievement

Introduction

Class attendance (CA) is often considered to be critical in learning (Gump, 2004) and it seems to be even more critical in learning a foreign language, where there is only a small chance of using the language outside the class. As Yi (2006) maintains, “students usually have more opportunities in an EFL class than classes of other subjects to speak, to talk, to read aloud, or even to argue with each other” (p. 132). Moreover, as numerous studies
(e.g., Gump, 2005) have reported a negative correlation between absences and final grades, the literature on CA has mostly dealt with the determinants of students` absenteeism, e.g., the time needed to complete course work, boring classes, illness, and class interference with students social life (Van Blerkom, 1992), and determinants of CA, e.g., motivation, prior GPA, self-financing by students, hours worked on jobs, quality of teaching, and nature of the class (Davadoss & Foltz, 1996). There are, however, other factors which could equally contribute to this literature.

One of such important factors which is largely ignored in the research on CA is the significant role of stroke. Stroke is defined as the recognition of other`s presence and values (Shirai, 2006). Francis and Woodcock (1996) stated that there is a direct relationship between stroke and motivation. Freedman (1993) highlighted the role of stroke-rich environments in the achievement of a higher level of performance. When a teacher cares about students through giving feedback, knowing their names, or maintaining good eye contact with them, it affects students’ academic performance and their CA accordingly. As Stewart and Joines (1987) argued, there are different types of stroke, including positive or negative and verbal or non-verbal. Encouraging students is an example of giving positive strokes to students, blaming students is an example of negative strokes, mentioning their names is a verbal stroke, and frowning at students is an example of a non-verbal stroke in the classroom (Pishghadam & Khajavy, 2014).

As it was defined and exemplified, stroke, in this study, is hypothesized to be a significant factor to manage absenteeism in the context of language learning and teaching. With that in mind, this study intends to examine the role of stroke in students’ willingness to attend classes (WTAC). Furthermore, to give a better understanding of the novel concept of WTAC, this study explores its roles in students’ English language achievement and CA in language classes.

Theoretical Framework

Stroke

In educational settings, like any other context, the quality of the relationships between interlocutors and teachers is of high importance. One of the main approaches to exploring the interpersonal relationships is transactional analysis (TA) proposed by Berne (1988). As “a theory of personality and systematic psychotherapy for personal growth and personal change" (Stewart & Joines, 1987, p. 3), TA has proved fruitful in educational contexts in helping teachers and learners (Stewart & Joines, 1987). Stroke, as one of the main components of the TA approach, refers to an action of any type to recognize others` presence (Berne, 1973). In education, it is simply a matter of care and paying attention to students. It can also be regarded as a unit of human recognition manifested in the form of a feedback. Among four levels of feedback identified by Hattie and Timperley (2007), i.e., task, process, self-regulation, and self, feedback about the self is closely related to the concept of stroke, as it is at the personal level, directed to the self (Pishghadam & Khajavy, 2014). In this vein, several studies argued that the three concepts of stroke, care, and feedback are interrelated and influential in learning (e.g., Pishghadam, Naji Meidani, & Khajavy, 2015; Rathel, Drasgow, Brown, & Marshall, 2013; Wright, Ellis, & Baxter, 2012).

The literature on stroke in language teaching is not hefty, and only a few studies in Ferdowsi University of Mashhad (Iran) have examined the concept. For instance, Pishghadam and Khajavy (2014) designed and validated a measure of student stroke to examine the relationship between this construct and motivation, coming up with a positive correlation between them. A teacher stroke scale was also designed and validated by Yazdanpour (2015) to examine the relationship between stroke and burnout, concluding that the amount of stroke received by teachers, either positively or negatively, would shape their attitudes toward their jobs and their students. Another study worth considering is the one conducted by Irajzad, Pishghadam, and Shahriari (2017). They analyzed the strokes received by high school students in language courses (Persian, Arabic, and English) in Iran. The results showed that teachers provide different types of strokes to students in these language courses. They mainly attributed their findings to the types of habitus they form in their career.

Overall, this positive role of stroke in the class environment seems to be undeniable, though under-researched. As already mentioned, due to the nature of stroke, which deals with the affective aspect of teaching, it seems to have the potential to be an explanatory factor in CA.

Class Attendance

Language learning takes place in an interactional context (Wells, 1981) and classroom interaction seems to be essential for language learning (Allwright, 1984). The classroom context opens an effective channel of communication and interaction among language learners, particularly the EFL ones. Therefore, CA is assumed to be necessary for EFL learners to interact directly through the language and receive feedback for their educational improvement. Despite the significance of CA in EFL contexts, absenteeism still seems to be a major concern in this particular setting.

A number of studies have been conducted to examine the relationship between CA and class performance to predict academic success. In this regard, research has found a strong negative correlation between absences and grade performance, indicating that grades decrease as absences increase in the classroom (e.g., Brocato, 1989; Friedman, Rodriguez, & McComb, 2001; Gump, 2004; Wyatt, 1992). In a study on attendance motivators, Gump (2004) analyzed the final grades and attendance rates of three hundred undergraduates in 12 discussion sectors of Introduction to Japanese Culture. The participants were also expected to attend two fifty-minute lectures per week. The results of the study showed a strong negative association between absences and grade performance in undergraduate economic students.  Although Gump (2004), in agreement with Cross, Fray, and Weber (1993), doubts the value of attendance as the only predictive factor of student achievement, he (2005) regards attendance as the easiest one for students to control. In the same vein, Crede, Roch, and Kieszczynka (2010) found that CA is a better predictor of college grade than any other factors known as predictors of academic success, such as standardized admissions tests (SAT), high school GPA, study habits, and study skills. Moreover, Clump, Bauer, and Whiteleather (2003) investigated the effect of CA on a general psychology course. They concluded that even if students could have full access to information presented in the classroom, CA would still have a crucial role to play in being successful in the course of study.

Another important line of research on CA explores students’ attitudes toward CA and the reasons students consider a class as worth attending (e.g., Davadoss & Foltz, 1996; Van Blerkom, 1992). Gump (2006), for instance, conducted a survey study to examine students` attitudes towards CA. In this study, he pointed out that although students` attitude is not the only factor influencing behavior, it is the one which can be encouraged by university instructors to minimize the negative effects of student absences. In another study, Fjortoft (2005) described students` motivations to find the reasons why they attended or did not attend classes. In this study, there were 9 first-year and 24 second-year pharmacy students in 5 focus groups. From the data collected, 18 variables emerged among which “class handout is not inclusive, faculty presented new information in class ̋, and ̋ faculty apply information to solving real problems̋ were the most frequent ones for attending classes. On the contrary, the most frequent variables for not attending classes were:  ̋class is before or after a test̏, ̋ faculty read their notes”, ̋personal logistics”, and ̋ two or more hour breaks before or after class”. Finally, Azmoudeh, Dowlati, Farzadmanesh, Khosroabadi, and Rakhshani (2013) conducted a cross-sectional study examining medical students` views on CA. The most important factors they found were teacher characteristics, such as the content presented by him/her or the teacher`s interesting speech. Course features or class time were also recognized to be important factors to explain CA

Purpose of the Study

The present study was carried out to investigate the role of stroke in EFL learner`s WTAC and their foreign language achievement (FLA). To this end, we first designed and validated a scale to measure WTAC. Next, we examined whether stroke components could predict WTAC components. And finally, we examined whether WTAC components could predict FLA. Accordingly, the following questions were addressed to answer the objectives of this study:

1. Does WTAC questionnaire enjoy the psychometric properties (reliability & validity)?

2. Do stroke components predict WTAC components?

3. Do WTAC components predict FLA?

Method

Participants

The participants of the present study were 260 EFL learners, aged 18-30, who answered the research questions. It must be mentioned that the variation in the age range was not pre-planned and thus not controlled. They were learners studying English language with different levels of language proficiency, elementary to advanced, in private language institutes of Mashhad, Iran. The reason for this selection was to increase the probability of generalization. Both males (N= 120) and females (N= 140) were asked to take the scales. Moreover, the students were reminded that participation was voluntary and their answers to the questions would be kept confidential. The sampling method for the selection of the participants was convenient sampling.

Instruments

Student Stroke Scale (SSS)

The SSS was designed and validated by Pishghadam and Khajavy (2014) to measure the strokes the students receive in the class. Items of the questionnaire were written according to main characteristics of stroke (i.e., recognition by others and providing feedback) and different types of stroke, namely, positive, negative, verbal, and non-verbal. Students were expected to respond to a 5 point Likert-type scale ranging from 1 (never) to 5 (always). Exploratory factor analysis (EFA) was used to examine the construct validity of the questionnaire, and structural equation modeling (SEM) was used to confirm the factor structure of the SSS obtained from EFA. After analyzing the items of the factors underlying the scale, factors were named as Verbal stroke, Non-verbal stroke, Valuing, and Classroom activities. The estimated reliability of the items is .88 (See Appendix A).

WTAC Scale

WTAC Questionnaire was designed by the researchers to examine the reasons for students` CA. It includes 25 five-point Likert scale items, ranging from (1) ҅strongly disagree҆ to (5) ҅strongly agree҆ (See Appendix B). These items correspond to the 5 major factors comprising the scale, namely, Teacher knowledge (TK), Teacher methodology (TM), Teacher care (TC), Teacher characteristics (TCH), and Teacher environment (TE). The items associated with each factor is indicated in Table 1.

Table 1. Items Measuring Each Factor

Procedure

First, structured face-to-face interviews were conducted to find out what factors are important for learners to attend classes. These interviews included a number of questions inquiring the factors which encourage students to attend classes, factors which prevent them from attending classes, their attitudes towards CA and CA policies, etc. The transcribed answers resulted in a pool of qualitative data which was then explored to unveil the potential themes. A thorough analysis of the data allowed for the extraction of the five factors (mentioned earlier) comprising the concept of CA. Next, based on the learners` views and in consultation with scholars in the field, a number of items were written for each factor and, hence, the questionnaire was designed. It was written in Persian, the mother tongue of the participants, to avoid any misunderstandings in reading items and to increase their response rate. It was then subjected to the pilot-testing in order to disambiguate the items and ensure the content validity of the scale. After the construct validation, the questionnaire, along with the SSS, was given to the EFL learners. Moreover, participants were required to mention their gender, age, and overall average in the questionnaires.

To analyze the data, in the first place, SEM via LISREL version 8.5 was utilized to confirm the validity of the WTAC. Then, the reliability of the validated questionnaire was assessed using the Cronbach Alpha reliability estimate. Finally, multiple regression analysis was employed to determine whether stroke components could predict WTAC, and whether WTAC components could predict FLA.

Result

Validation of WTAC

A five-factor model of WTAC with 25 items was specified (Figure 1). To confirm the factor structure of the proposed model, SEM was utilized. The goodness of fit measures was used to see whether the model fits the data adequately. In this study, Chi-square/degree of freedom (χ2/df), Goodness of Fit Index (GFI), Incremental Fit Index (IFI), Comparative Fit Index (CFI), Adjusted Goodness of Fit Index (AGFI), Normal Fit Index (NFI), and Root Mean-Square Error of Approximation (RMSEA), were used. According to MacCallum, Browne, and Sugawara (1996), χ2/df should be less than 3, RMSEA should be less than .08, AGFI, GFI, IFI, CFI, and NFI should be above .90 to have an acceptable fit model. Results of the CFA indicated that all the goodness-of-fit indices were above the cutoff points (see Table 2). Therefore, the CFA confirmed the factor structure of WTCA. In addition, the Cronbach Alpha estimated the reliability of the whole items as 0.83.

Table 2. Goodness-of-fit Indices

Figure 1. The Result of CFA

X1, X2, X3, X4, and X5 are Teacher Knowledge, Teacher Methodology, Teacher Care, Teacher Characteristics, and Teacher Environment, respectively.

Regression Analysis

Prior to conducting regression analysis, correlations among different subscales of stroke and WTAC were checked. As can be seen in Table 3, significant and positive relationships (ranging from .14 to .40) were found among all subscales of stroke and WTAC subscales. The highest correlation was found between Valuing subscale of stroke and TM subscale of WTAC (r = .40). The lowest correlation was found between Non-verbal subscale of stroke and TCH subscale of WTAC (r = .14).

Table 3. Correlations among Stroke and WTAC Subscales (TK: Teacher Knowledge; TM: Teacher Methodology; TC: Teacher Care; TCH: Teacher Characteristics; TE: Teaching Environment)

^̽ p<.05.

^{̽ ̽}p<.01.

Prediction of WTAC

To answer the second research question, regression analysis was used. Four stroke subscales were regressed on the WTAC scale. The obtained results are presented in Table 4. As can be seen, there is one significant model (F= 13.6, model sig= .000, p<.01). It also shows that R² equals .229 indicating that about 23% of the variance can be predicted by the independent variables, i.e., the scores of Non-verbal and Valuing.

Table 4. Regression Analysis for WTAC

Regarding the standard error of estimate, the smaller it is, the stronger the prediction will be. In this regression model, the standard error of estimate is .40 which is an acceptable value and it shows the precision of the prediction. Considering Beta coefficients, the positive relationships are reported as the Non-verbal stroke is the strong predictor of WTAC
(B= .19, p<.05). The Valuing component of stroke is also a positive predictor of WTAC
(B= .18, p<.05). It is implied that giving Non-verbal and Valuing strokes by teachers would increase students` CA.

Prediction of Foreign Language Achievement

To see whether WTAC components predict FLA, regression analysis was conducted. As Table 5 indicates, a significant model (F= .70, model sig= .04, p<.05) was found with TCH as the predictor. As it is shown in Table 5, R² equals .079 meaning this dimension of WTAC accounts for almost 8% of the variance in FLA.

Table 5. Regression Analysis for FLA

Based on the table, the standard error of estimate is almost 3.4, which shows the accuracy of its prediction. Beta coefficients also show that the relationship between TCH and FLA is significant and positive (B= .02, p<.05).

Discussion

As already mentioned, this study intended mainly to examine the relationship between stroke, CA, and FLA. To this end, this study first attempted to design and validate a scale to measure the willingness of students to attend classes in EFL contexts. To design the scale, structured face-to-face interviews were done with EFL learners to find out their attitudes towards CA and their reasons for attendance or non-attendance. The views of scholars in the field were also attended to in designing the questionnaire. Then, SEM was applied to substantiate the construct validity of the questionnaire. The results of the goodness-of-fit indices showed a sufficient fit to the data confirming the factor structure of WTAC based on which WTAC can be considered as an efficient scale for measuring the willingness of EFL learners to attend language classes.

To answer the second research question, i.e., whether stroke components predict WTAC, regression analysis was utilized. The result showed that two components of stroke, namely, Non-verbal and Valuing, could significantly predict WTAC. This is a noticeable result as it demonstrates the major influence of two types of stroke on students` rate of CA. Non-verbal stroke, as the first predictor, refers to the types of Non-verbal recognition that teachers give to students, including the acts of smiling, frowning, looking, and paying attention (Pishghadam & Khajavy, 2014). As already stated, stroke can be regarded as a kind of feedback influencing learning. In this respect, Guvendir (2011) argued that the studies which examine the feedback strategies used by teachers mostly focus on the verbal performance of teachers and the role of non-verbal behavior has been largely ignored. Moreover, Kellogg and Lawson (1993) showed that classroom interaction is dominated by non-verbal communications. It was indicated that 82% of teachers` communications are non-verbal. Their study can provide a plausible explanation for the obtained result in this part. Since a major part of teacher`s interactions with students is non-verbal, it would be considered as a significant factor influencing student`s WTAC.

The second predictor of WTAC, i.e., Valuing stroke, refers to the adequate time teacher devotes to students inside/outside the class and the amount of personal experience and scientific knowledge of students that teacher uses in the class (Pishghadam & Khajavy, 2014). Simply put, it shows that teachers actually value their students. A relevant study conducted by Rahimi and Hosseini Karkami (2015) examined the role of teachers` classroom discipline in their teaching effectiveness and students` language learning motivation and achievement. The results of their study showed that the teachers who used recognition and involvement strategies more frequently are more effective teachers. In EFL contexts, classroom is the only place where language learners hope to find chances to use language. To fulfill the learners` potential goals, EFL teachers need to provide learners with enough time to participate in class activities. The EFL teacher should also let students freely use language to express their personal experiences and feelings in the classroom. This subscale of stroke can, therefore, motivate learners to attend classes. This interpretation is in line with Pishghadam and Khajavy`s (2014) study in which they examined the relationship between stroke and motivation. They found that Valuing, amongst all subscales of the stroke, had the highest correlation with intrinsic and extrinsic motivation, and students have the highest level of motivation when the teacher values them and asks them to participate in classroom activities. Clearly enough, a higher level of motivation would increase the level of achievement. The reason, according to Lewis (2001), is that when teachers involve students in decision-makings in the classroom or when students` good behaviour is recognized, they would act more responsibly in class.

With regard to the third research question, i.e., whether WTAC components predict FLA, TCH, among the components of WTAC, was found to be a predictor of FLA. In other words, teacher`s sense of humour and respect for students were the features bringing about FLA in EFL classes. Students would preferably choose those teachers who make learning fun (Howard, 2001). A number of studies have approved of the appropriateness and effectiveness of using humour in foreign language classes (e.g., Deneire, 1995; Wanzer, 2002; Wagner & Urios-Aparisi, 2011). Wagner and Urios-Aparisi (2011) generally view humor as "an important source for increasing student motivation" (p. 402). Krause (2014) argued that as humour brings about amusement and laughter, it is considered not only as a student motivator but as a creator of a more relaxed and positive classroom atmosphere, beneficial for learning. Brosh (1996) also stated that some kind of attitude and feeling is always projected in one`s teaching. In EFL classes, learners need to interact through the new language to develop communicative skills; therefore, teachers` characteristics become considerably important in maintaining interactions positively, starting stimulating conversations, and holding students` attention in the classroom. All in all, it would make sense to conclude that since stroke was found to be a predictor of WTAC and WTAC was found to predict FLA, stroke could be considered as a predictor of FLA. To further elaborate on the relationship, as students receive Non-verbal and Valuing strokes, they would be more motivated to attend classes.

It is unlikely to do a study without any limitations. The limitations of the present study are well recognized and readers should interpret and generalize the findings with due caution. The small sample size, 260 participants, limits the generalizability of the results. Since several factors such as age and gender were not controlled, future research is recommended to examine whether controlling these factors would change the obtained results. This study only used correlational procedures to examine the relations between variables. In future research, advanced statistical procedures like SEM can be used to examine these relations.